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    A production-grade MCP server for the Code-Fundi API, enabling AI agents to map codebases, search semantically, and analyze blast radius. It provides tools for repository management, AI-powered research, and impact analysis before shipping changes.
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    40 npm
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    MIT
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    MCP server for overseer.nvim — gives any MCP-capable coding agent running inside Neovim (Claude Code, Codex, Gemini CLI, opencode) the same visibility and control over tasks the user has: list, run, tail, restart, stop. Agent-started dev servers land in the user's own task list instead of becoming invisible orphaned processes.
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    54 npm
    MIT
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    Read-only MCP server for Grafana Loki: lets AI agents (Claude Code, Cursor, Copilot) discover labels, read bounded LogQL pages, count lines by label or time, and export logs — across several Loki stands
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    Apache 2.0
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    Enables local SOC 2 compliance work by scanning Infrastructure-as-Code, mapping findings to SOC 2 Trust Services Criteria, explaining risks in plain English, and drafting audit-ready documents without uploading files.
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    An offline MCP server for SAP ABAP that provides static analysis, ABAP Cloud readiness checks, and RAP scaffolding using abaplint, enabling AI agents to analyze and generate ABAP code without a live SAP system.
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    261 npm
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    MIT
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    Read-only MCP server for self-hosted SonarQube Community Build 26.4+: lets AI agents (Claude Code, Cursor, Copilot) read issues, security hotspots, rules and code snippets to fix findings locally
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    MIT
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    Enables coding agents to read ground truth about a React design system — components, props, cva variants, tokens and docs — over MCP so they write UI that matches it. It also exposes a check_ui linter agents run on their own output, returning rule ids, locations and concrete fixes before review.
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    831 npm
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    MIT
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    Searches hotels and vacation rentals with nightly and total rates, ratings, amenities, and detailed property information through natural language in any MCP client.
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    95 npm
    46 PyPI
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    MIT
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    The interface protocol for AI agents. 8 kernel primitives + 16 stdlib operations to operate any interface. Forge once, run forever — zero AI at runtime. 81 skills across 41 sites.
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    MIT
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    Exposes WAI-ARIA Authoring Practices Guide (APG) patterns, providing narratives, keyboard guidance, and official source code examples for accessible widgets. It includes tools for listing patterns, retrieving specific specs, and performing semantic searches via Ollama.
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    9 npm
    ISC
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    Provides AI coding assistants with access to AccelByte Gaming Services (AGS) Extend SDK functions and models through searchable symbols and detailed documentation. Helps generate SDK code by exposing context about available functions, models, and templates for creating Extend app projects across multiple languages (Go, C#, Java, Python).
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    Apache 2.0
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    MCP server that injects verified FTC documentation and code examples into AI assistants, enabling teams to write correct, competition-ready Java robot code through natural language.
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    13 npm
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    MIT
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    Redmine for AI agents (Claude Code, Cursor, Copilot): search and read issues, projects, members, versions, wiki, time entries and reference data; extract text from attachments (PDF, DOCX, XLSX, ZIP); analytics such as release risks, blocker chains and stale issues. 32 read-only tools by default, 7 optional write tools. Java, stdio, Docker image available.
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    MIT
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    Extract invoices and contracts from text or Markdown into typed JSON with Mistral. Optional OCR supports PDFs and images when your account has access and quota. Six tools by default: documents, OCR, chat, vision, code completion and transcription. Additional API tools via explicit profiles. Runs over stdio or Streamable HTTP. Community-maintained; bring your own Mistral API key.
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    264 npm
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    MIT
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    Enables AI agents to manage inventory conversationally: list and look up products, check current stock levels, record inbound and outbound movements idempotently, review movement history, and surface items below their minimum threshold. Every figure is grounded in tool calls rather than model estimation, and invalid operations return explicit error codes the agent can reason about.
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    MIT