"Running code or executing programming scripts" matching MCP servers:

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.

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 YouTube competitor research, signal analysis, and transcript-aware pack building for content ideation and scriptwriting.

Enables AI agents to interact with the Baltamatica scientific computing kernel, executing code, managing variables, and running scripts while preserving workspace state across calls.

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.

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 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.

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

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 AI agents to interact with Jupyter notebooks via MCP tools for querying, modifying, executing, and setting up notebooks, with state preservation and real-time collaboration.

Enables pre-submission review of ML papers inside Claude Code or Claude Desktop by checking format and anonymization risks, resolving every reference, finding prior work for each claim, verifying quotes against the PDF, and running methodology and writing checks. It reports findings only, never scores, ratings, or accept/reject verdicts, and never rewrites the author's text.

Enables running Simulatte's 26 research SKUs (concept testing, pricing, etc.) from any MCP-compatible AI client, returning structured JSON results.

Enables AI agents to monitor MetaTrader 5 accounts on remote Windows VPS via SSH, providing read-only access to positions, history, and logs without executing trades.

MCP server for Tracetify — trace how any product actually grew, without leaving Claude Code or Cursor.

Enables AI assistants to search, authenticate, and manage digital repository content through the REST API, including creating, updating, and deleting items, uploading file attachments, running workspace submissions, and browsing communities and collections. It can run either locally over stdio or on AWS Lambda via HTTP transport.

Enables running SEO and AI/GEO visibility audits on websites, reading audit results, competitor analyses and expert reports, and importing or retrieving editorial calendars, all from any MCP client such as Claude Desktop, Claude Code or Cursor.

Enables users to discover, search, validate and cross-map versioned statistical code systems — including industry, occupation, product, education and questionnaire response hierarchies — with deterministic retrieval and provenance. It supports questionnaire development, survey coding and reproducible statistical harmonisation without running any AI model inside the server.

Provides deterministic, plain-English narratives explaining machine learning model predictions via the Model Context Protocol. It enables users to query classification factors directly through natural language, bypassing the need for complex plots or manual code execution.

Enables AI assistants to programmatically access and manage Gemini Notebook (formerly Google NotebookLM), including creating notebooks, adding sources, querying content, generating studio content, downloading artifacts, and running batch or cross-notebook workflows through natural language.