"Learning or Writing Code in Python" matching MCP servers:

Enables academic research through paper search across multiple databases (IACR, CryptoBib, Crossref, Google Scholar), PDF processing, and GitHub repository browsing. Features modular architecture with FastMCP-based proxy server routing to specialized academic tools.

Enables AI agents to look up any Louisiana statute, civil code, or procedure article by citation, returning full section text with enactment and amendment history parsed out. Also supports full-text topic search across the code bodies and browsing every section within a Revised Statutes title, keyless and sourced live from the Louisiana State Legislature.

Provides access to the Korean elementary 2022 revised curriculum learning graph, enabling users to search and retrieve curriculum standards, learning topics, prerequisites, and roadmaps from a fully local database.

Turns a first-person lab video, a protocols.io protocol or a PDF into an Opentrons Flex or OT-2 protocol that passes Opentrons' simulator. Tools list runs, read the steps, return protocol.py, analyze it and run it on Opentrons' robot-server.

Read-only MCP server for the July 2026 survey of AI in open-source design systems, enabling agents to query 19 systems' affordances, coercion techniques, and platform data via 9 tools, 2 resources, and 2 prompts.

Enables LLMs to generate visual summary reports from CSV datasets using MCP Resources, Tools, and Prompts, without exposing raw data to the model.

This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.

Enables users to train, evaluate, select, explain, and use logistic regression and KNN models through MCP tools, including sample input retrieval, predictions, confusion-matrix metrics, SHAP explanations, and Parquet audit export.

Enables AI agents to operate DSpace repositories through a typed interface, including SAF package building, metadata editing, scientometric harvesting, open-access PDF downloads, and repository administration.

Enables training and comparing Logistic Regression and KNN breast-cancer classifiers, getting sample inputs and predictions, evaluating confusion-matrix metrics, selecting the best model, generating SHAP explanations, and exporting a Parquet audit log through MCP tools.

Enables users to train and compare Logistic Regression and KNN models on the Wisconsin breast cancer dataset, run predictions on patient feature inputs, compute confusion-matrix metrics and select the best model by malignant recall, generate global and local SHAP explanations, and export Parquet audit logs of every tool call. Also provides sample test-set inputs so agents can invoke prediction tools with valid 30-feature payloads.

A Python implementation of the Model Context Protocol (MCP) server that enables searching and extracting information from arXiv papers, designed to be extensible with additional MCP tools.

Enables searching and discovering machine learning papers, state-of-the-art benchmarks, tasks, datasets, methods, and leaderboards from Papers with Code. Supports mapping papers to their benchmark results and browsing evaluation tables.

Enables secure execution of statistical Python code in isolated micro-VM sandboxes, generating charts and analytical summaries.

Real human judgment as agent tools -- an AI agent can ask a question and get back a structured, schema-validated JSON answer from a real quality-scored human. 16 response types (yes/no, ratings, rankings, A/B tests, sentiment, image/video/audio review, voice/video/photo capture). Fully programmatic signup with a $5 free trial credit, no card required.

Enables AI agents to hire verified human operators for tasks requiring physical presence, human perception, or judgment, such as real-world verification, product testing, and data collection.

Search and load AI fine-tuning and evaluation datasets, prompts and few-shot contexts from the ContexteTech catalogue, grounded in official European sources, in 9 languages. Dataset download links (JSONL, ShareGPT, Alpaca, CSV) and cost estimates per model.

Provides AI agents with real-time access to corporate credit data, including debt structures, bond pricing, and guarantor chains extracted from SEC filings. It enables complex financial analysis such as screening companies by leverage, tracing corporate hierarchies, and searching covenant language.

Enables time series analysis following Box-Jenkins-Treadway methodology, supporting guided or autonomous modes for model identification, estimation, and diagnosis via an LLM.

Enables configuration-driven, budgeted multi-source web research across platform and web search adapters, merging duplicate links, preserving provenance, and returning honest source coverage reports.

Enables AI assistants to load CSV datasets, compute summary statistics, filter rows, rank columns, and compute correlations through the Model Context Protocol.

Provides tools to search and execute Code Ocean capsules and pipelines while managing platform data assets. It enables users to interact with Code Ocean's computational resources and scientific workflows directly through natural language interfaces.

Enables analysts to build and evaluate simultaneous VARMA models for systems of time series via exact maximum likelihood on the ATSW ladder, using univariate models as the yardstick. It guides users through loading univariate fits, identifying residual cross-correlations, estimating joint models, forecasting, impulse responses, variance decomposition, and recording decisions.

Enables MCP clients to fetch live football odds and team form, and flag matches where the market price diverges from a simple form-based estimate.

Enables AI clients to query ContentRadar data and write low-risk content using natural language, with 15 tools for monitoring, analysis, and content management.

Generates EnergyPlus weather files (EPW, DDY, STAT) for any coordinates: actual years from 1990, typical years and future climate to 2100, plus ASHRAE design conditions and nearest weather stations.

Enables AI agents to read cited stock news, earnings, and filings; screen stocks; run point-in-time backtests; check portfolios; and query a local dashboard across US, A-share, and Hong Kong markets.

Enables searching and reading a library of PDF books page by page, and verifying whether a quote appears on its cited page.

A read-only reference server for 303 curated generative art algorithms implemented in Python (py5), spanning physics, fractals, cellular automata, shaders, and more. Agents can search by keyword, visual mood (ethereal, chaotic, crystalline…), or multi-layer artistic intent to discover algorithms, read structured summaries, and fetch bounded source snippets.

An MCP-native research assistant — search backend, intent-routing, and writing pipeline that carries a question through search, verification, and writing, delivering whatever research you need: quick answers, verified sources, or a finished document.

MCP server that analyzes your writing style to generate personal voice rules, scores drafts for AI slop, and verifies authorship, enabling LLMs to write in your specific voice.

Give AI agents 10 offline tools for market observations, repeated headlines, forecast evaluation, correlation risk and statistical checks. Preserve source clocks, missing data and stated assumptions. Python, CLI and MCP share the same zero-dependency core; no feed collection or trade execution.

MCP server pulling academic publications (arXiv, PubMed, HF Daily Papers), trending code (GitHub, HF Hub), and medical-device regulatory data (FDA 510(k), recalls) into newspaper-style briefings. Per-category round-robin, weighted configuration, sandbox-safe Python launcher.

Checks whether a citation has been retracted, corrected, or flagged with an expression of concern by querying Crossref — including retractions that Crossref backfills from the Retraction Watch database, which publishers often never record in their own metadata. Lets an AI agent verify a DOI, or every DOI in a reference list, before using it in research or writing.

An MCP server that guides Copilot to version-correct documentation and source code for Industrial Ecology Python packages, enabling reliable, environment-aware coding assistance for LCA workflows.

Provides machine learning researchers with tools for creating publication-quality scientific visualizations, statistical plots, and 2D data representations. It streamlines the research workflow by enabling AI assistants to generate complex figures from CSV, JSON, or direct data inputs.

Seed oil (PUFA) data for 500+ US restaurant chains: letter grades, the oil each chain fries in, cleanest menu items, and rankings. Zero-dependency Python stdio server backed by the free hosted Seed Oil Tracker endpoint, no key or account needed.

Enables exploratory data analysis and machine learning on CSV datasets with tools for profiling, missing values, correlation, plotting, model training, and prediction.

Recommends study designs for a research question, states the identifying assumptions to verify, and returns runnable R, Python, or Julia code with a formatted citation for every source, drawn from methodological textbooks, an R package, and research articles on causal inference, econometrics, statistics, and machine learning.

Enables MCP clients to search for published academic articles and retrieve detailed metadata, abstracts, citation counts, and related information from the Elsevier Scopus API. Supports citation verification and writing-style analysis grounded in peer-reviewed sources.