"How to create pull requests on GitHub" matching MCP servers:

Made-to-order data for AI agents via x402 micropayments on Base. Describe a need in plain language, get a custom quote, pay per call. No signup, no API keys. HTTP + MCP transports. 5 tools.

MCP server for the Mamba Labs GitHub Organization Signal Scanner actor on Apify. Resolve a company domain to its GitHub organization with repo, language and activity signals.

Enables an agent to run ten ready-made research and monitoring workflows — competitor digests, cited reports, paper alerts, market maps, regulatory and due-diligence watches — pulling from Firecrawl, Tavily, GitHub and landing results in Notion, Slack, Google Docs, Sheets and Linear. Fields are filed with the user's own keys held in the operating system keychain, and every action that creates, sends or changes anything is shown and approved first.

Converts PDF files to text for use with MCP-compatible applications like Cursor IDE.

Enables AI agents to crawl, analyze, and query job description (JD) data from recruitment sites via MCP, with tools for searching jobs, viewing statistics and portraits, and running local analysis on privately stored data.

Provides GitHub open-source intelligence via MCP tools for discovering trends, evaluating repository health, comparing projects, and analyzing users, issues, and releases. It includes a CLI and can be used with AI assistants like Claude or Cursor.

A high-performance server for analyzing GitHub user starred repositories, providing insights into development trends, technology adoption patterns, and timeline tracking.

Enables AI clients to search a local literature library via vector semantic retrieval, with automatic conversion of Zotero items and arbitrary files into Markdown (including OCR for scanned PDFs). It supports natural language queries and provides answers with page-level citations.

Enables any agent to query the same interview corpus as the UI over stdio, exposing tools to list transcripts, search quoted lines, retrieve verified themes with citations and dropped reasons, and fetch source lines.

Query iOS App Store revenue and download estimates for any app directly from your agent. Two tools: estimate_app returns monthly revenue and downloads with a confidence label (verified / calibrated / modeled), and search_apps resolves an app name to its App Store id.

Answers operational questions about route on-time performance, such as lateness rates and trip evidence, using structured tools for route summary, trip lateness, and data quality.

An MCP server providing progressive discovery tools for SDMX statistical data, enabling AI agents to explore and access SDMX-compliant repositories through interactive tools, resources, and prompts.

MCP server exposing 40 best-practice product management skills plus workflows and lifecycle tools, instantly accessible to any AI via Model Context Protocol.

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 Claude Code to convert PDF files to high-quality PNG images, download academic papers, and batch process PDFs with automatic folder organization.

An MCP server that scrapes e-commerce product and category pages into structured JSON, enabling Claude to build presentations, market analyses, and competitive product comparisons. Currently optimized for Trendyol.

Converts Digital Object Identifiers (DOIs) to BibTeX format using the official DOI content negotiation API, enabling users to quickly generate bibliography entries for academic papers.

Enables searching and retrieving UK research grants, award values, institutions, and publications from the UKRI Gateway to Research API without authentication.

Enables local analysis of scientific papers including PDF parsing, mathematical formula extraction with AST generation, PyTorch code generation from methodology, and automated Markdown report generation with visualizations.

Compute and analyze bus route lateness using trip data, with drill-down and cross-referencing against rider complaints and operational logs.