For quantitative research, the established Python libraries are NumPy 8 for n-dimensional array computing, SciPy 2 for statistics and optimization algorithms, and Statsmodels 9 for statistical and econometric modelling. For a tool that runs statistical analysis on your data and names the method, MCP Analytics 3 offers an MCP server and a run-analysis action 13, and QuanterLab 4 is a quantitative research platform for building and testing systematic strategies with walk-forward valida
- 1
- 2
SciPyscipy.orgwebsite read, tools not yet checked
Provides algorithms for optimization, integration, interpolation, eigenvalue problems, and statistics.
+2 community tools built for it
- 3
Statsmodelsstatsmodels.orgverified and signed
Provides Python libraries for statistical modelling and econometrics.
+2 community tools built for it
for agents: statistical · statistics · econometrics
- 4
MCP Analyticsmcpanalytics.aiverified and signed
Runs statistical analysis on your data and returns a shareable report with statistics you can cite and the method named, via its run analysis action 13.
official:MCP server · MCP Analytics
for agents: discover tools · find precedent · run analysis
- 5
QuanterLabquanterlab.comwebsite read, tools not yet checked
A quantitative research platform to build and falsify systematic trading strategies with point-in-time discipline and walk-forward validation, with full code export.
official:MCP server · QuanterLab
Sources
- 2SciPyscipy.org · read 2026-10-29
- 3MCP Analytics: Statistical Analysis You Can Defendmcpanalytics.ai · read 2026-10-29
- 4QuanterLab: Quantitative Research Platformquanterlab.com · read 2026-10-29
- 8NumPynumpy.org · read 2026-10-29
- 9Statsmodelsstatsmodels.org · read just now
- 13MCP Analytics: declares "discover tools" (the record we read and signed)unfragile.ai · read 2026-10-24
For agents: the 10 actions behind this answer
· Plotly website read, tools not yet checked
Millions of data teams build with Plotly. Open-source graphing libraries, Dash, and Plotly Studio for interactive data apps in Python.
· SciPy website read, tools not yet checked
Why SciPy? Fundamental algorithms. Broadly applicable. Foundational. Interoperable. Performant. Open source.
discover tools · MCP Analytics verified and signed
Browse the analyses you can run: the ones you commissioned plus the platform Standard Library (prebuilt tools; each result tagged source:'own' or 'standard_library'). Plain-language match; no query lists everything, your own first. Nothing fits? Commission it with create_analysis.
· QuanterLab website read, tools not yet checked
QuanterLab is a quantitative research platform: a visual environment to build, falsify, and understand systematic strategies under point-in-time discipline, with walk-forward validation and full code export. A place to test your hypotheses and understand your risk; every run is simulated, and no rea
pandas · dask verified and signed
· Scientific Python website read, tools not yet checked
Community developed and owned ecosystem for scientific computing
cycle · cycler verified and signed
· NumPy website read, tools not yet checked
Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source.
statistical · statsmodels verified and signed
data · pooch verified and signed
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.