"How to use GitHub to pull, push code, create PRs, and trigger actions" matching MCP servers:

Enables managing GitHub Actions workflows through MCP, including listing and inspecting workflows, triggering workflow_dispatch and repository_dispatch events, and monitoring or canceling workflow runs.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Enables scanning GitHub Actions workflow YAML for real-world security vulnerabilities such as script injection from attacker-controlled expressions, unpinned third-party actions, missing permissions blocks, and dangerous pull_request_target checkouts. It returns each finding with its exact location, an explanation of the risk, and a concrete remediation.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables AI assistants to convert CSS-animated SVGs into videos (MP4/WebM/MKV/MOV) and animated images (aPNG/GIF) with frame-accurate rendering and transparency support.

MCP server for polygenic risk score analysis, wrapping just-prs to enable catalog search, PRS computation, VCF normalization, percentile and risk estimation, and quality assessment.

A Model Context Protocol server that converts Markdown documents to plain text with flexible options and dual protocol support.

An MCP server that enables AI agents to retrieve detailed GitHub Pull Request information using git commit hashes, branch names, or PR numbers. It automatically detects repositories and extracts comprehensive PR data including descriptions, labels, and reviews via the GitHub CLI.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables AI assistants to query PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB databases through MCP with read-only, audited access.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Converts any Python Click or Typer CLI into an MCP server automatically — zero-code transformation that introspects CLI commands and exposes them as MCP tools.

Enables existing Express routers to be exposed as MCP tools, dispatching tool calls through the Express middleware stack in-process without a network hop.

Turns any OData V2 or V4 service, SAP Gateway included, into MCP tools that AI clients such as Claude, ChatGPT, Copilot, and Cursor can call without code by reading $metadata. It lets models discover entity sets and fields, then query services through built-in read-only tools.

Auto-generates MCP tools from your OpenAPI spec, allowing natural language interaction with any API via configurable headers and serverless deployment.

Enables AI agents to transcribe Instagram Reels into text by providing a tool that accepts a Reel URL and returns the speech transcript along with metadata such as author and duration.

Enables AI assistants to call any SOAP/WSDL web service as MCP tools, automatically converting each WSDL operation into a callable tool with authentication and security controls.

A minimal MCP server that auto-generates tools from your OpenAPI spec, optimized for Vercel.

A standalone proxy that transforms any OpenAPI or Swagger-described REST API into an MCP server by mapping API operations to executable MCP tools. It enables AI clients to interact with existing web services through automated HTTP requests based on their official documentation.