diff --git a/app/__main__.py b/app/__main__.py
index ca261e98..df60de3a 100644
--- a/app/__main__.py
+++ b/app/__main__.py
@@ -8,7 +8,6 @@
 from fastapi.staticfiles import StaticFiles
 from fluid.utils import log
 
-from app.api.mcp import create_mcp as _create_mcp
 from app.utils.paths import APP_PATH
 from quantflow import __version__
 
@@ -56,17 +55,15 @@ async def api_redoc() -> HTMLResponse:
             title="Quantflow API",
         )
 
-    api.include_router(cointegration_router)
-    api.include_router(heston_router)
-    api.include_router(hurst_router)
-    api.include_router(rates_router)
-    api.include_router(sampling_router)
-    api.include_router(smoother_router)
-    api.include_router(volatility_router)
+    api.include_router(cointegration_router, tags=["timeseries"])
+    api.include_router(heston_router, tags=["pricing"])
+    api.include_router(hurst_router, tags=["statistics"])
+    api.include_router(rates_router, tags=["pricing"])
+    api.include_router(sampling_router, tags=["statistics"])
+    api.include_router(smoother_router, tags=["timeseries"])
+    api.include_router(volatility_router, tags=["pricing"])
     app.include_router(api)
     app.include_router(status_router, include_in_schema=False)
-    if _create_mcp is not None:
-        api.mount("/mcp", _create_mcp().streamable_http_app())
     examples_dir = APP_PATH / "examples"
     if examples_dir.is_dir():
         app.mount(
diff --git a/app/api/cointegration.py b/app/api/cointegration.py
index 53f96c4d..f07cd469 100644
--- a/app/api/cointegration.py
+++ b/app/api/cointegration.py
@@ -5,6 +5,7 @@
 from pydantic import BaseModel, Field
 from statsmodels.tsa.vector_ar.vecm import coint_johansen
 
+from app.api.docs import load_description
 from quantflow.data.fmp import FMP
 
 from .deps import FMPDep, RedisCache, RedisDep
@@ -20,13 +21,17 @@ class CointegrationResponse(BaseModel):
     )
 
 
-@cointegration_router.get("/cointegration")
+@cointegration_router.get(
+    "/cointegration",
+    summary="BTC/ETH/SOL cointegration",
+    description=load_description("cointegration.md"),
+)
 async def cointegration(
     fmp: FMPDep,
     redis: RedisDep,
     frequency: FMP.freq = Query(
         FMP.freq.daily,
-        description="Price frequency",
+        description="Price sampling frequency.",
     ),
 ) -> CointegrationResponse:
     cache = RedisCache(
diff --git a/app/api/docs/__init__.py b/app/api/docs/__init__.py
new file mode 100644
index 00000000..49ae0c54
--- /dev/null
+++ b/app/api/docs/__init__.py
@@ -0,0 +1,7 @@
+from pathlib import Path
+
+_DOCS_PATH = Path(__file__).parent
+
+
+def load_description(filename: str) -> str:
+    return (_DOCS_PATH / filename).read_text(encoding="utf-8")
diff --git a/app/api/docs/cointegration.md b/app/api/docs/cointegration.md
new file mode 100644
index 00000000..d6988735
--- /dev/null
+++ b/app/api/docs/cointegration.md
@@ -0,0 +1,9 @@
+Johansen cointegration analysis of BTC, ETH, and SOL log prices.
+
+Returns the mean-reverting spread (residuals) and the cointegrating vector
+(eigenvector for the largest eigenvalue of the Johansen test).
+
+Log prices are standardised before the test; the returned cointegrating vector
+is rescaled back to the original units and normalised to unit length.
+A residual near zero means the spread is close to its long-run equilibrium.
+Large positive or negative residuals signal potential mean-reversion trades.
diff --git a/app/api/docs/double_exponential_sampling.md b/app/api/docs/double_exponential_sampling.md
new file mode 100644
index 00000000..14681df4
--- /dev/null
+++ b/app/api/docs/double_exponential_sampling.md
@@ -0,0 +1,11 @@
+Sample from a double exponential (asymmetric Laplace) distribution and compare
+the empirical PDF to both the analytical PDF and the PDF recovered from the
+characteristic function.
+
+The double exponential distribution models asymmetric jump sizes in jump-diffusion
+models. The asymmetry is controlled by `log_kappa`: positive values produce
+heavier right tails (upward jumps more likely), negative values heavier left tails.
+At `log_kappa = 0` the distribution is symmetric.
+
+The characteristic function inversion provides an independent check that the
+analytical and numerical PDFs agree.
diff --git a/app/api/docs/gaussian_sampling.md b/app/api/docs/gaussian_sampling.md
new file mode 100644
index 00000000..b47fbfaa
--- /dev/null
+++ b/app/api/docs/gaussian_sampling.md
@@ -0,0 +1,12 @@
+Sample from a Vasicek mean-reverting Gaussian process and compare the empirical
+distribution to the analytical marginal PDF.
+
+The process is run for one unit of time with 1000 time steps. The terminal
+distribution is binned into 50 bins and compared against the analytical Gaussian PDF.
+
+A Kolmogorov-Smirnov test is included to quantify goodness of fit.
+A high p-value (e.g. above 0.05) means the simulation is statistically consistent
+with the analytical distribution.
+
+Antithetic variates halve the variance of Monte Carlo estimates by pairing each
+draw with its negative, without additional model evaluations.
diff --git a/app/api/docs/heston_vol_surface.md b/app/api/docs/heston_vol_surface.md
new file mode 100644
index 00000000..35500003
--- /dev/null
+++ b/app/api/docs/heston_vol_surface.md
@@ -0,0 +1,15 @@
+Generate a theoretical implied volatility surface using a stochastic volatility model.
+
+Two models are available:
+
+- **jd** (Jump Diffusion): a Variance-Gamma-style model with a double-exponential
+  jump distribution. Controlled by `vol`, `jump_fraction`, `jump_intensity`, and
+  `jump_asymmetry`. The `kappa` and `rho` parameters are ignored.
+
+- **hj** (Heston with Jumps): the Heston stochastic volatility model augmented with
+  double-exponential jumps. All parameters are used.
+
+The surface is returned as a grid: 10 maturities evenly spaced from 0.1 to 1.0 years,
+and 51 moneyness values from -0.5 to 0.5 (log-moneyness scaled by sqrt(ttm)).
+Implied volatilities that cannot be computed (e.g. deep in/out of the money) are
+returned as 0.
diff --git a/app/api/docs/hurst_vasicek.md b/app/api/docs/hurst_vasicek.md
new file mode 100644
index 00000000..2aa1a4af
--- /dev/null
+++ b/app/api/docs/hurst_vasicek.md
@@ -0,0 +1,9 @@
+Simulate a Vasicek mean-reverting process and estimate its Hurst exponent.
+
+The Vasicek process reverts to zero at speed `kappa`. Higher values of `kappa`
+produce stronger mean reversion and a lower Hurst exponent (well below 0.5).
+Low values of `kappa` approach Brownian motion (Hurst near 0.5).
+
+The Hurst exponent is estimated from both realized variance and three OHLC-based
+estimators (Parkinson, Garman-Klass, Rogers-Satchell) sampled at periods of
+10min, 20min, 30min, and 1h.
diff --git a/app/api/docs/hurst_wiener.md b/app/api/docs/hurst_wiener.md
new file mode 100644
index 00000000..b11aa894
--- /dev/null
+++ b/app/api/docs/hurst_wiener.md
@@ -0,0 +1,15 @@
+Simulate a Wiener (Brownian motion) process over one day at one-second resolution
+and estimate its Hurst exponent using multiple methods.
+
+The Hurst exponent measures long-range dependence in a time series.
+A value of 0.5 indicates pure Brownian motion (no memory).
+Values above 0.5 indicate trending behaviour; values below 0.5 indicate mean reversion.
+
+Three OHLC-based range estimators are computed across multiple sampling periods:
+
+- **Parkinson**: uses high-low range.
+- **Garman-Klass**: uses open, high, low, close.
+- **Rogers-Satchell**: drift-adjusted version of Garman-Klass.
+
+The Hurst exponent for each estimator is derived from the slope of log(variance)
+vs log(sampling period).
diff --git a/app/api/docs/poisson_sampling.md b/app/api/docs/poisson_sampling.md
new file mode 100644
index 00000000..e3ed62c6
--- /dev/null
+++ b/app/api/docs/poisson_sampling.md
@@ -0,0 +1,10 @@
+Sample from a Poisson jump process and compare the empirical jump count distribution
+to the analytical Poisson PMF.
+
+The process is run for one unit of time with 1000 time steps. Jump counts are
+binned and compared against the analytical distribution using a chi-squared
+goodness-of-fit test. Tail bins with expected count below 5 are merged to satisfy
+the chi-squared validity requirement.
+
+A high p-value (e.g. above 0.05) means the simulation is statistically consistent
+with the analytical Poisson distribution.
diff --git a/app/api/docs/supersmoother.md b/app/api/docs/supersmoother.md
new file mode 100644
index 00000000..5fc90181
--- /dev/null
+++ b/app/api/docs/supersmoother.md
@@ -0,0 +1,12 @@
+Apply SuperSmoother and EWMA filters to two years of historical daily price data
+for a given symbol.
+
+SuperSmoother is an adaptive low-pass filter designed by John Ehlers. It suppresses
+high-frequency noise more aggressively than EWMA while introducing less lag at
+trend inflections.
+
+Both filters use the same `period` parameter, making it easy to compare their
+smoothing characteristics on the same data. Shorter periods follow price more
+closely; longer periods produce smoother output with more lag.
+
+Supported symbols include crypto pairs (e.g. BTCUSD, ETHUSD) and US equities.
diff --git a/app/api/docs/volatility_surface.md b/app/api/docs/volatility_surface.md
new file mode 100644
index 00000000..08327113
--- /dev/null
+++ b/app/api/docs/volatility_surface.md
@@ -0,0 +1,21 @@
+Fetch the live implied volatility surface for a crypto or equity asset and return
+the calibrated surface with discount curves and forward prices.
+
+Data sources:
+
+- **BTC, ETH**: live option chain from Deribit (inverse, crypto-quoted contracts).
+- **SPY, AAPL, NVDA**: option chain from Yahoo Finance (standard equity contracts).
+
+The surface is calibrated using Black-Scholes implied volatilities. Outlier options
+(bid-ask spread too wide, or IV outside plausible bounds) are disabled before
+the surface is returned.
+
+Two discount curves are calibrated from the option data:
+
+- **quote_curve**: discount curve for the numeraire (USD for equity, crypto for inverse).
+- **asset_curve**: discount curve for the underlying asset.
+
+The forward curve and per-maturity implied forwards from put-call parity are also
+included, which are useful for detecting curve arbitrage or funding dislocations.
+
+Responses are cached; live data may be up to a few minutes old.
diff --git a/app/api/docs/yield_curve.md b/app/api/docs/yield_curve.md
new file mode 100644
index 00000000..f7959522
--- /dev/null
+++ b/app/api/docs/yield_curve.md
@@ -0,0 +1,17 @@
+Fit a yield curve model to a set of (time-to-maturity, rate) pairs and return
+the calibrated curve plus interpolated rates.
+
+Four curve types are supported:
+
+- **nelson_siegel**: the Nelson-Siegel parametric model, well suited for smooth
+  yield curves with a single hump. Good default choice.
+- **cir_curve**: Cox-Ingersoll-Ross model, a mean-reverting short-rate model.
+- **vasicek_curve**: Vasicek model, a Gaussian mean-reverting short-rate model.
+- **no_discount**: flat discount curve (no discounting), useful as a baseline.
+
+Rates should be continuously compounded and expressed as decimals (e.g. 0.045 for 4.5%).
+Time to maturity is in years (e.g. 0.25 for 3 months, 2.0 for 2 years).
+
+If `max_ttm` is provided, the response includes `num_points` interpolated rates
+evenly spaced up to that maturity, which is useful for plotting a smooth curve.
+Otherwise, rates are returned at the input maturities only.
diff --git a/app/api/heston.py b/app/api/heston.py
index 7d70914d..ffdcedbe 100644
--- a/app/api/heston.py
+++ b/app/api/heston.py
@@ -2,6 +2,7 @@
 from fastapi import APIRouter, Query
 from pydantic import BaseModel, Field
 
+from app.api.docs import load_description
 from quantflow.options.pricer import OptionPricer
 from quantflow.sp.heston import HestonJ
 from quantflow.sp.jump_diffusion import JumpDiffusion
@@ -18,7 +19,11 @@ class VolSurfaceGridResponse(BaseModel):
     )
 
 
-@heston_router.get("/heston-vol-surface")
+@heston_router.get(
+    "/heston-vol-surface",
+    summary="Theoretical implied volatility surface",
+    description=load_description("heston_vol_surface.md"),
+)
 async def heston_vol_surface(
     model: str = Query(
         "jd",
diff --git a/app/api/hurst.py b/app/api/hurst.py
index be6bdd13..233e196d 100644
--- a/app/api/hurst.py
+++ b/app/api/hurst.py
@@ -7,6 +7,7 @@
 from fastapi import APIRouter, Query
 from pydantic import BaseModel, Field
 
+from app.api.docs import load_description
 from quantflow.sp.ou import Vasicek
 from quantflow.sp.wiener import WienerProcess
 from quantflow.ta.ohlc import OHLC
@@ -86,7 +87,11 @@ def _ohlc_hurst(df: pd.DataFrame, serie: str, periods: tuple) -> dict[str, float
     return result
 
 
-@hurst_router.get("/hurst-wiener")
+@hurst_router.get(
+    "/hurst-wiener",
+    summary="Hurst exponent of a Wiener process",
+    description=load_description("hurst_wiener.md"),
+)
 async def hurst_wiener(
     sigma: float = Query(2.0, description="Wiener process volatility", ge=0.1, le=10),
 ) -> HurstWienerResponse:
@@ -138,7 +143,11 @@ async def hurst_wiener(
     )
 
 
-@hurst_router.get("/hurst-vasicek")
+@hurst_router.get(
+    "/hurst-vasicek",
+    summary="Hurst exponent of a Vasicek process",
+    description=load_description("hurst_vasicek.md"),
+)
 async def hurst_vasicek(
     kappa: float = Query(10.0, description="Mean reversion speed", ge=1.0, le=500.0),
 ) -> HurstVasicekResponse:
diff --git a/app/api/mcp.py b/app/api/mcp.py
deleted file mode 100644
index 8854a222..00000000
--- a/app/api/mcp.py
+++ /dev/null
@@ -1,90 +0,0 @@
-"""MCP server for quantflow - crypto volatility tools served via HTTP."""
-
-from pathlib import Path
-
-from mcp.server.fastmcp import FastMCP
-
-from quantflow.data.deribit import Deribit, InstrumentKind
-
-
-def create_mcp() -> FastMCP:
-    mcp = FastMCP("quantflow")
-
-    @mcp.tool()
-    async def crypto_instruments(currency: str, kind: str = "spot") -> str:
-        """List available instruments for a cryptocurrency on Deribit.
-
-        Args:
-            currency: Cryptocurrency symbol e.g. BTC, ETH
-            kind: Instrument kind - spot, future, option (default: spot)
-        """
-        async with Deribit() as client:
-            data = await client.get_instruments(
-                currency=currency, kind=InstrumentKind(kind)
-            )
-        if not data:
-            return f"No instruments found for {currency} ({kind})"
-        rows = "\n".join(str(d) for d in data[:20])
-        return f"Instruments for {currency} ({kind}):\n{rows}"
-
-    @mcp.tool()
-    async def crypto_historical_volatility(currency: str) -> str:
-        """Get historical volatility for a cryptocurrency from Deribit.
-
-        Args:
-            currency: Cryptocurrency symbol e.g. BTC, ETH
-        """
-        async with Deribit() as client:
-            df = await client.get_volatility(currency)
-        if df.empty:
-            return f"No volatility data for {currency}"
-        return df.to_csv(index=False)
-
-    @mcp.tool()
-    async def crypto_term_structure(currency: str) -> str:
-        """Get the volatility term structure for a cryptocurrency from Deribit.
-
-        Args:
-            currency: Cryptocurrency symbol e.g. BTC, ETH
-        """
-        async with Deribit() as client:
-            loader = await client.volatility_surface_loader(currency)
-        vs = loader.surface()
-        ts = vs.term_structure().round({"ttm": 4})
-        return ts.to_csv(index=False)
-
-    @mcp.tool()
-    async def crypto_implied_volatility(currency: str, maturity_index: int = -1) -> str:
-        """Get the implied volatility surface for a cryptocurrency from Deribit.
-
-        Args:
-            currency: Cryptocurrency symbol e.g. BTC, ETH
-            maturity_index: Maturity index (-1 for all maturities)
-        """
-        async with Deribit() as client:
-            loader = await client.volatility_surface_loader(currency)
-        vs = loader.surface()
-        index = None if maturity_index < 0 else maturity_index
-        vs.bs(index=index)
-        df = vs.options_df(index=index)
-        df["iv"] = df["iv"].map("{:.2%}".format)
-        return df.to_csv(index=False)
-
-    @mcp.tool()
-    async def vol_surface_snapshot(currency: str, path: str) -> str:
-        """Fetch a live volatility surface from Deribit and save it as a JSON snapshot.
-
-        Args:
-            currency: Cryptocurrency symbol e.g. BTC, ETH
-            path: File path to write the snapshot to
-        """
-        async with Deribit() as client:
-            loader = await client.volatility_surface_loader(currency)
-        vs = loader.surface()
-        vs.bs()
-        vs.disable_outliers()
-        inputs = vs.inputs(converged=True)
-        Path(path).write_text(inputs.model_dump_json(indent=2))
-        return f"Saved {len(vs.maturities)} maturities to {path}"
-
-    return mcp
diff --git a/app/api/rates.py b/app/api/rates.py
index 427c402e..134c2c05 100644
--- a/app/api/rates.py
+++ b/app/api/rates.py
@@ -4,6 +4,7 @@
 from fastapi import APIRouter, Query
 from pydantic import BaseModel, Field
 
+from app.api.docs import load_description
 from quantflow.rates import AnyYieldCurve, YieldCurve
 
 rates_router = APIRouter()
@@ -17,7 +18,11 @@ class YieldCurveResponse(BaseModel):
     rates: list[float] = Field(description="List of rates for the fitted curve")
 
 
-@rates_router.get("/yield-curve")
+@rates_router.get(
+    "/yield-curve",
+    summary="Fit and interpolate a yield curve",
+    description=load_description("yield_curve.md"),
+)
 async def yield_curve(
     ttm: list[float] = Query(
         ..., description="List of time to maturities corresponding to the rates"
diff --git a/app/api/sampling.py b/app/api/sampling.py
index 18add3e9..db9a21bc 100644
--- a/app/api/sampling.py
+++ b/app/api/sampling.py
@@ -3,6 +3,7 @@
 from pydantic import BaseModel, Field
 from scipy.stats import chisquare, ks_1samp
 
+from app.api.docs import load_description
 from quantflow.sp.ou import Vasicek
 from quantflow.sp.poisson import PoissonProcess
 from quantflow.ta.paths import Paths
@@ -35,7 +36,11 @@ class DoubleExponentialResponse(SamplingResponse):
     char_y: list[float] = Field(description="Y values from characteristic function")
 
 
-@sampling_router.get("/gaussian-sampling")
+@sampling_router.get(
+    "/gaussian-sampling",
+    summary="Gaussian process sampling vs analytical PDF",
+    description=load_description("gaussian_sampling.md"),
+)
 async def gaussian_sampling(
     kappa: float = Query(1.0, description="Mean reversion speed", ge=0.1, le=5.0),
     samples: int = Query(1000, description="Number of sample paths", ge=100, le=10000),
@@ -61,7 +66,11 @@ async def gaussian_sampling(
     )
 
 
-@sampling_router.get("/poisson-sampling")
+@sampling_router.get(
+    "/poisson-sampling",
+    summary="Poisson process sampling vs analytical PMF",
+    description=load_description("poisson_sampling.md"),
+)
 async def poisson_sampling(
     intensity: float = Query(2.0, description="Poisson intensity", ge=2.0, le=20.0),
     samples: int = Query(1000, description="Number of sample paths", ge=100, le=10000),
@@ -94,7 +103,11 @@ async def poisson_sampling(
     )
 
 
-@sampling_router.get("/double-exponential-sampling")
+@sampling_router.get(
+    "/double-exponential-sampling",
+    summary="Double exponential sampling vs analytical PDF",
+    description=load_description("double_exponential_sampling.md"),
+)
 async def double_exponential_sampling(
     log_kappa: float = Query(
         0.1, description="Log of asymmetry parameter", ge=-2.0, le=2.0
diff --git a/app/api/smoother.py b/app/api/smoother.py
index ab27818e..40ebeccc 100644
--- a/app/api/smoother.py
+++ b/app/api/smoother.py
@@ -4,6 +4,7 @@
 from fastapi import APIRouter, Query
 from pydantic import BaseModel, Field
 
+from app.api.docs import load_description
 from quantflow.ta.ewma import EWMA
 from quantflow.ta.supersmoother import SuperSmoother
 
@@ -23,7 +24,12 @@ class SmootherResponse(BaseModel):
     data: list[SmootherPoint] = Field(description="Time series with smoothed values")
 
 
-@smoother_router.get("/supersmoother")
+@smoother_router.get(
+    "/supersmoother",
+    summary="SuperSmoother and EWMA price filter",
+    description=load_description("supersmoother.md"),
+    response_model=SmootherResponse,
+)
 async def supersmoother(
     redis: RedisDep,
     fmp: FMPDep,
diff --git a/app/api/volatility.py b/app/api/volatility.py
index 68fab547..009bfdbe 100644
--- a/app/api/volatility.py
+++ b/app/api/volatility.py
@@ -5,6 +5,7 @@
 from fastapi import APIRouter, Query
 from pydantic import BaseModel, Field
 
+from app.api.docs import load_description
 from quantflow.data.deribit import Deribit
 from quantflow.data.yahoo import Yahoo
 from quantflow.options.inputs import VolSurfaceInputs
@@ -54,7 +55,11 @@ class VolSurfaceResponse(BaseModel):
     )
 
 
-@volatility_router.get("/volatility-surface")
+@volatility_router.get(
+    "/volatility-surface",
+    summary="Live implied volatility surface",
+    description=load_description("volatility_surface.md"),
+)
 async def volatility_surface(
     redis: RedisDep,
     asset: str = Query(
diff --git a/docs/api/index.md b/docs/api/index.md
index c64cbe60..7dffd0e8 100644
--- a/docs/api/index.md
+++ b/docs/api/index.md
@@ -53,7 +53,6 @@ Time series filters and indicators for financial data.
 | Module | Description |
 |---|---|
 | [EWMA](ta/ewma.md) | Exponentially weighted moving average |
-| [Kalman Filter](ta/kalman.md) | Kalman filter for state estimation |
 | [Supersmoother](ta/supersmoother.md) | Ehlers two-pole supersmoother filter |
 | [OHLC](ta/ohlc.md) | OHLC bar utilities and resampling |
 | [Paths](ta/paths.md) | Simulated path containers and statistics |
diff --git a/docs/api/ta/kalman.md b/docs/api/ta/kalman.md
deleted file mode 100644
index 4f1d395d..00000000
--- a/docs/api/ta/kalman.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# Kalman Filter
-
-::: quantflow.ta.kalman.KalmanFilter
diff --git a/docs/mcp.md b/docs/mcp.md
deleted file mode 100644
index 1065e3c9..00000000
--- a/docs/mcp.md
+++ /dev/null
@@ -1,42 +0,0 @@
-# MCP Server
-
-Quantflow exposes its crypto volatility tools as an [MCP](https://modelcontextprotocol.io) server, allowing AI clients such as Claude to query live Deribit data directly.
-
-The server is hosted at:
-
-```
-https://quantflow.quantmind.com/.api/mcp
-```
-
-It uses the [Streamable HTTP](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#streamable-http) transport. No API key is required — all tools use the public Deribit API.
-
-## Claude Code
-
-```bash
-claude mcp add --transport http quantflow https://quantflow.quantmind.com/.api/mcp
-```
-
-## Claude Desktop
-
-Add to your Claude Desktop config (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):
-
-```json
-{
-  "mcpServers": {
-    "quantflow": {
-      "type": "streamable-http",
-      "url": "https://quantflow.quantmind.com/.api/mcp"
-    }
-  }
-}
-```
-
-## Available tools
-
-| Tool | Description |
-|---|---|
-| `crypto_instruments` | List instruments for a currency on Deribit (spot, future, option) |
-| `crypto_historical_volatility` | Historical volatility index from Deribit |
-| `crypto_term_structure` | Volatility term structure across maturities |
-| `crypto_implied_volatility` | Implied volatility surface (all maturities or a single one) |
-| `vol_surface_snapshot` | Fetch a live vol surface and write it as a JSON file |
diff --git a/docs/release-notes.md b/docs/release-notes.md
index 07129dd2..13f7d679 100644
--- a/docs/release-notes.md
+++ b/docs/release-notes.md
@@ -154,7 +154,6 @@ and signature changes were made along the way: see **Breaking changes** below.
 
 - New logo set (favicon, lockup, marks, social banners) under
   `docs/assets/logos/`. ([#53](https://github.com/quantmind/quantflow/pull/53))
-- New `docs/mcp.md` page covering the MCP server.
 - Bibliography rebuilt from BibTeX via `docs/bib2md.py`; glossary expanded;
   mathjax tweaks for inline rendering.
   ([#47](https://github.com/quantmind/quantflow/pull/47),
diff --git a/frontend/observablehq.config.js b/frontend/observablehq.config.js
index 95cdb0b0..7be60b04 100644
--- a/frontend/observablehq.config.js
+++ b/frontend/observablehq.config.js
@@ -13,7 +13,10 @@ export default {
   root: "src",
   output: "../app/examples",
   base: "/examples",
-  head: `<meta name="quantflow-api-origin" content="${apiOrigin}">\n${headSnippet}`,
+  head: `<meta name="quantflow-api-origin" content="${apiOrigin}">
+<script async src="https://www.googletagmanager.com/gtag/js?id=G-CM0DR45HDR"></script>
+<script>window.dataLayer=window.dataLayer||[];function gtag(){dataLayer.push(arguments);}gtag('js',new Date());gtag('config','G-CM0DR45HDR');</script>
+${headSnippet}`,
   style: "style.css",
   pages: [{name: "Volatility Surface", path: "/volatility-surface"}, {name: "Yield Curve", path: "/yield-curve"}, {name: "Sampling", path: "/sampling"}, {name: "SuperSmoother", path: "/supersmoother"}, {name: "Cointegration", path: "/cointegration"}, {name: "Hurst Exponent", path: "/hurst"}, {name: "Heston Vol Surface", path: "/heston-vol-surface"}],
   header: `
@@ -44,5 +47,5 @@ export default {
       })();
     </script>
   `,
-  footer: "Quantflow live examples"
+  footer: 'Quantflow live examples · <a href="https://quantflow.quantmind.com/.api/redoc">API Reference</a>'
 };
diff --git a/mkdocs.yml b/mkdocs.yml
index 2bf80493..d9d3ad4a 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -94,7 +94,6 @@ nav:
       - Timeseries Analysis:
           - api/ta/index.md
           - EWMA: api/ta/ewma.md
-          - Kalman Filter: api/ta/kalman.md
           - OHLC: api/ta/ohlc.md
           - Paths: api/ta/paths.md
           - Supersmoother: api/ta/supersmoother.md
@@ -132,7 +131,6 @@ nav:
       - Lévy Process: theory/levy.md
       - Option Pricing: theory/option_pricing.md
   - Live Examples: https://quantflow.quantmind.com/examples
-  - MCP Server: mcp.md
   - Glossary: glossary.md
   - Contributing: contributing.md
   - Bibliography: bibliography.md
diff --git a/pyproject.toml b/pyproject.toml
index 834725a5..a4f609d3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -75,7 +75,6 @@ docs = [
     "griffe-pydantic>=1.1.0",
     "griffe-typingdoc>=0.2.7",
     "kaleido>=1.2.0",
-    "mcp>=1.26.0",
     "mkdocs-macros-plugin>=1.3.7",
     "mkdocs-material>=9.7.0",
     "mkdocs-redirects>=1.2.1",
diff --git a/quantflow/ta/kalman.py b/quantflow/ta/kalman.py
deleted file mode 100644
index 4ec5606c..00000000
--- a/quantflow/ta/kalman.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from pydantic import BaseModel, Field, PrivateAttr
-from typing_extensions import Annotated, Doc
-
-
-class KalmanFilter(BaseModel):
-    r"""One-dimensional Kalman filter for time series data.
-
-    This implementation uses a simple 1D state-space model:
-
-    $$
-    \begin{align}
-        x_t &= x_{t-1} + w_t, \quad w_t \sim \mathcal{N}(0, Q) \\
-        z_t &= x_t + v_t, \quad v_t \sim \mathcal{N}(0, R)
-    \end{align}
-    $$
-
-    The Kalman filter estimates the hidden state $x_t$ given noisy measurements $z_t$.
-    The ratio $Q/R$ determines the smoothing behavior.
-    """
-
-    R: float = Field(default=1.0, gt=0.0, description="Measurement noise covariance")
-    Q: float = Field(default=0.01, gt=0.0, description="Process noise covariance")
-
-    _x: float | None = PrivateAttr(default=None)  # State estimate
-    _P: float = PrivateAttr(default=1.0)  # Error covariance
-    _K: float = PrivateAttr(default=0.0)  # Kalman Gain
-
-    def value(self) -> float | None:
-        """Get the most recent smoothed value (state estimate), if available."""
-        return self._x
-
-    def update(
-        self,
-        value: Annotated[float, Doc("New noisy measurement to update the filter")],
-    ) -> float:
-        """Update the filter with a new value and return the smoothed result."""
-        # Initialize on first update
-        if self._x is None:
-            self._x = value
-            self._P = self.R
-            return value
-
-        # Prediction step
-        # x_pred = x_prev (Random walk model)
-        # P_pred = P_prev + Q
-        x_pred = self._x
-        P_pred = self._P + self.Q
-
-        # Update step
-        # K = P_pred / (P_pred + R)
-        # x_new = x_pred + K * (measurement - x_pred)
-        # P_new = (1 - K) * P_pred
-        K = P_pred / (P_pred + self.R)
-        x_new = x_pred + K * (value - x_pred)
-        P_new = (1 - K) * P_pred
-
-        # Update state
-        self._x = x_new
-        self._P = P_new
-        self._K = K
-
-        return x_new
-
-    @property
-    def error_covariance(self) -> float:
-        """Current estimated error covariance."""
-        return self._P
-
-    @property
-    def kalman_gain(self) -> float:
-        """Most recent Kalman gain."""
-        return self._K
diff --git a/quantflow_tests/test_ai.py b/quantflow_tests/test_ai.py
deleted file mode 100644
index ab1d09c8..00000000
--- a/quantflow_tests/test_ai.py
+++ /dev/null
@@ -1,121 +0,0 @@
-"""Tests for the quantflow MCP crypto tools."""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pandas as pd
-import pytest
-from mcp.server.fastmcp import FastMCP
-
-from app.api.mcp import create_mcp
-
-
-def text(result: Any) -> str:
-    """Extract text from a call_tool result."""
-    blocks = result[0] if isinstance(result, tuple) else result
-    if blocks and hasattr(blocks[0], "text"):
-        return blocks[0].text
-    return str(result)
-
-
-@pytest.fixture
-def mcp_server() -> FastMCP:
-    return create_mcp()
-
-
-async def test_crypto_instruments(mcp_server: FastMCP) -> None:
-    mock_client = AsyncMock()
-    mock_client.get_instruments.return_value = [
-        MagicMock(__str__=lambda self: "BTC-SPOT")
-    ]
-    with patch("app.api.mcp.Deribit") as MockDeribit:
-        MockDeribit.return_value.__aenter__ = AsyncMock(return_value=mock_client)
-        MockDeribit.return_value.__aexit__ = AsyncMock(return_value=False)
-        result = await mcp_server.call_tool("crypto_instruments", {"currency": "BTC"})
-    assert "BTC" in text(result)
-
-
-async def test_crypto_instruments_empty(mcp_server: FastMCP) -> None:
-    mock_client = AsyncMock()
-    mock_client.get_instruments.return_value = []
-    with patch("app.api.mcp.Deribit") as MockDeribit:
-        MockDeribit.return_value.__aenter__ = AsyncMock(return_value=mock_client)
-        MockDeribit.return_value.__aexit__ = AsyncMock(return_value=False)
-        result = await mcp_server.call_tool("crypto_instruments", {"currency": "BTC"})
-    assert "No instruments" in text(result)
-
-
-async def test_crypto_historical_volatility(mcp_server: FastMCP) -> None:
-    mock_client = AsyncMock()
-    mock_client.get_volatility.return_value = pd.DataFrame(
-        [{"date": "2025-01-01", "volatility": 0.8}]
-    )
-    with patch("app.api.mcp.Deribit") as MockDeribit:
-        MockDeribit.return_value.__aenter__ = AsyncMock(return_value=mock_client)
-        MockDeribit.return_value.__aexit__ = AsyncMock(return_value=False)
-        result = await mcp_server.call_tool(
-            "crypto_historical_volatility", {"currency": "BTC"}
-        )
-    assert "volatility" in text(result)
-    assert "2025-01-01" in text(result)
-
-
-async def test_crypto_historical_volatility_empty(mcp_server: FastMCP) -> None:
-    mock_client = AsyncMock()
-    mock_client.get_volatility.return_value = pd.DataFrame()
-    with patch("app.api.mcp.Deribit") as MockDeribit:
-        MockDeribit.return_value.__aenter__ = AsyncMock(return_value=mock_client)
-        MockDeribit.return_value.__aexit__ = AsyncMock(return_value=False)
-        result = await mcp_server.call_tool(
-            "crypto_historical_volatility", {"currency": "BTC"}
-        )
-    assert "No volatility data" in text(result)
-
-
-async def test_crypto_term_structure(mcp_server: FastMCP, vol_surface: Any) -> None:
-    mock_loader = MagicMock()
-    mock_loader.surface.return_value = vol_surface
-    mock_client = AsyncMock()
-    mock_client.volatility_surface_loader.return_value = mock_loader
-    with patch("app.api.mcp.Deribit") as MockDeribit:
-        MockDeribit.return_value.__aenter__ = AsyncMock(return_value=mock_client)
-        MockDeribit.return_value.__aexit__ = AsyncMock(return_value=False)
-        result = await mcp_server.call_tool(
-            "crypto_term_structure", {"currency": "ETH"}
-        )
-    assert "ttm" in text(result)
-
-
-async def test_crypto_implied_volatility(mcp_server: FastMCP, vol_surface: Any) -> None:
-    mock_loader = MagicMock()
-    mock_loader.surface.return_value = vol_surface
-    mock_client = AsyncMock()
-    mock_client.volatility_surface_loader.return_value = mock_loader
-    with patch("app.api.mcp.Deribit") as MockDeribit:
-        MockDeribit.return_value.__aenter__ = AsyncMock(return_value=mock_client)
-        MockDeribit.return_value.__aexit__ = AsyncMock(return_value=False)
-        result = await mcp_server.call_tool(
-            "crypto_implied_volatility", {"currency": "ETH"}
-        )
-    assert "iv" in text(result)
-
-
-async def test_vol_surface_snapshot(
-    mcp_server: FastMCP, vol_surface: Any, tmp_path: Path
-) -> None:
-    mock_loader = MagicMock()
-    mock_loader.surface.return_value = vol_surface
-    mock_client = AsyncMock()
-    mock_client.volatility_surface_loader.return_value = mock_loader
-    out = str(tmp_path / "snapshot.json")
-    with patch("app.api.mcp.Deribit") as MockDeribit:
-        MockDeribit.return_value.__aenter__ = AsyncMock(return_value=mock_client)
-        MockDeribit.return_value.__aexit__ = AsyncMock(return_value=False)
-        result = await mcp_server.call_tool(
-            "vol_surface_snapshot", {"currency": "ETH", "path": out}
-        )
-    assert "Saved" in text(result)
-    assert Path(out).exists()
diff --git a/quantflow_tests/test_ta_filters.py b/quantflow_tests/test_ta_filters.py
index ffc83490..69850ac3 100644
--- a/quantflow_tests/test_ta_filters.py
+++ b/quantflow_tests/test_ta_filters.py
@@ -3,7 +3,6 @@
 import pytest
 
 from quantflow.ta.ewma import EWMA
-from quantflow.ta.kalman import KalmanFilter
 from quantflow.ta.supersmoother import SuperSmoother
 
 
@@ -44,24 +43,6 @@ def test_ewma_from_alpha_validation() -> None:
         EWMA.from_alpha(alpha=0.0)
 
 
-def test_kalman_initialization_and_first_update() -> None:
-    kf = KalmanFilter(R=1.0, Q=0.1)
-    assert kf.value() is None
-    first = kf.update(5.0)
-    assert first == 5.0
-    assert kf.value() == 5.0
-    assert kf.error_covariance == 1.0
-
-
-def test_kalman_steady_updates_and_properties() -> None:
-    kf = KalmanFilter(R=1.0, Q=0.1)
-    kf.update(10.0)
-    updated = kf.update(12.0)
-    assert 10.0 < updated < 12.0
-    assert 0.0 < kf.kalman_gain < 1.0
-    assert kf.error_covariance > 0.0
-
-
 def test_supersmoother_initialization() -> None:
     smoother = SuperSmoother(period=10)
     assert smoother.raw_value() is None
diff --git a/uv.lock b/uv.lock
index 8e81e96e..80c49876 100644
--- a/uv.lock
+++ b/uv.lock
@@ -679,65 +679,6 @@ toml = [
     { name = "tomli", marker = "python_full_version <= '3.11'" },
 ]
 
-[[package]]
-name = "cryptography"
-version = "48.0.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "cffi", marker = "platform_python_implementation != 'PyPy'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/9f/a9/db8f313fdcd85d767d4973515e1db101f9c71f95fced83233de224673757/cryptography-48.0.0.tar.gz", hash = "sha256:5c3932f4436d1cccb036cb0eaef46e6e2db91035166f1ad6505c3c9d5a635920", size = 832984, upload-time = "2026-05-04T22:59:38.133Z" }
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