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    Model Context Protocol (MCP) server for GitLab — exposes 1006 GitLab REST & GraphQL API operations as MCP tools (42 meta-tools / 57 enterprise), 24 resources, 38 prompts, and 17 completion types for AI assistants. Written in Go, single static binary, stdio and HTTP transport.
    2
    42
    MIT
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    Connects AI coding assistants to Gaffer test history and coverage data to analyze project health, debug failures, and identify untested code areas. It enables tools to track test stability, cluster failures by root cause, and monitor code coverage trends across projects.
    3
    44 npm
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    MIT
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    Enables AI clients to assess infrastructure-as-code deployment risk, generate incident response runbooks, review CI/CD delivery readiness, and calculate SLO error budgets through structured advisory tools. Runs locally over stdio without cloud credentials or external API calls, returning guidance for platform work, pull request review, and production readiness checks.
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    533 npm
    MIT
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    Checks whether software dependencies (npm packages, GitHub Actions, MCP servers, Docker images) are alive, dormant, abandoned, archived, or deleted using dated observations from lastseen.dev.
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    MIT
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    Enables AI assistants to set up, run, and manage Ballerina + Salesforce integrations inside a WSO2 Integrator workspace, providing 18 tools for OAuth2 token acquisition, project scaffolding, building, deploying, and stopping the integration service.
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    A production-ready MCP server for Jenkins that enables managing jobs, pipelines, builds, and nodes through natural language, with configurable security and guardrails.
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    MIT
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    Enables inspection and management of Apache Airflow DAGs, runs, and logs across multiple instances. It provides tools for monitoring workflows and performing gated write operations like triggering DAGs or clearing task instances.
    16
    39 PyPI
    2
    Apache 2.0