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

Monitors and analyzes GitHub repository health by detecting stale branches, old pull requests, unresponsive issues, and security alerts. Integrates with MCP-compatible AI assistants and automation tools.

A minimal MCP server that exposes health-check and server information tools over stdio or HTTP, and can be containerized and deployed to IBM Code Engine.

Enables real-time assessment of GitHub repository health by exposing tools and resources for repository metadata, open issues, recent commits, and GitHub Actions workflow runs, plus prompts for health reports and issue triage.

MCP server that diagnoses ML model regressions by correlating drift reports, eval runs, and deploy logs, providing evidence-cited incident reports through a set of investigation tools.

A pre-action risk gate for AI agents. Your agent calls the forecast tool before any irreversible action — send email, run SQL, make a payment, delete a file — and gets a risk score (0–100) and a GO / CONFIRM / STOP verdict in a few seconds.

An intelligent MCP server that gives AI agents full control over GitHub Actions CI/CD pipelines, including real-time monitoring, log analysis, AI-powered failure diagnosis, and deployment management.

An MCP server that retrieves current GitHub Copilot usage data, including quotas, limits, and usage statistics. It allows AI agents to monitor premium interaction status and detailed account usage via raw or formatted summaries.

MCP server for On-Call Health burnout analysis. Connects AI assistants to your on-call data for workload insights.

This server integrates with GitHub Advanced Security to load security alerts and bring it into your context. Supports Dependabot Security Alerts, Secret Scanning Alerts, Code Security Alerts

Enables multi-agent system auditing with seven-layer analysis, cost governance, compliance mapping, and watchdog monitoring. Offers tools for audit execution, cost analysis, report verification, regulation lookup, remediation suggestions, and fix tracking.

Deploys 25 MCP tools for Apache Doris, enabling SQL execution, metadata queries, monitoring, and data governance over HTTPS via AWS Bedrock AgentCore Runtime.

Enables users to measure, track, and compare response times of GitHub Copilot and other AI models. Provides statistical analysis and performance comparison tools for evaluating AI model efficiency.

Uptime monitoring for websites, APIs, SSL certificates, domain expiry, ping and TCP/UDP ports. 15 tools to list, create, pause and delete monitors, pull incident timelines with error codes, and read hourly or daily uptime and response-time statistics.

Connects AI tools directly to Codemend production error monitoring to list, analyze, and resolve software crashes. It enables users to retrieve AI-generated fixes and automatically open GitHub pull requests to address production issues.

Enables read-only, stack-aware diagnostics for microservices on Kubernetes, turning debugging runbooks into conversational tools that investigate slow requests, changes, leaks, and in-pod conditions without exposing cluster credentials.

MCP Server for notify to telegram / weixin

Analyzes log files locally using Ollama and files structured GitHub Issues automatically, with all processing kept on your machine.

This server lets coding agents inspect Bugpacker bug packages locally, exposing console output, failed requests, repro steps, and package files over MCP while keeping all data on the machine.

Enables MCP clients like Claude or Cursor to query Umami Analytics traffic, run v3 reports, create and manage websites, teams, and users through natural language while keeping credentials on the user's own machine.

Make AIs recommend your business. Measures how AI assistants answer the questions your market asks, which sources they cite, and gives the levers to shape those answers. 43 tools on a hosted Streamable HTTP endpoint at https://mcp.epovest.com/mcp