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    MCP router that fronts your other MCP servers behind a few small generic tools, so their tool definitions stay out of the context window and are discovered on demand.
    6
    1,031 npm
    322 PyPI
    2
    MIT
  • A
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    Zero-trust PII & secret airlock for coding agents (reversible <TOKEN_N> file read/write sanitization, safe subprocess runner with stdout scrubbing, and 0.2ms Git pre-commit scanning via pure-ONNX GLiNER + regex).
    7
    96 PyPI
    1
    MIT
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    Checks a code repository without running it: Safe, Works and Complete scores with an A-F grade, catching leaked keys, dangerous calls, invented or lookalike packages, broken imports and missing project files. Standard-library Python; MCP tools scan, orphans and explain.
    5
    235 PyPI
    MIT
  • A
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    Graph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.
    6
    18
    MIT
  • F
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    A persistent, Git-versioned map of your whole codebase and database schema that coding agents read before they touch anything. Local-first MCP server + CLI in Go: a governed repository index of code knowledge that gives Claude Code, Codex, Cursor, and opencode long-term context, memory, and code intelligence.
    9
    1,912
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  • A
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    Indexes polyglot repositories into a local architecture graph resolving DI bindings, HTTP/gRPC/GraphQL routes, message queues, and ORM/SQL table lineage. Provides dozens MCP tools for call-chain tracing, blast-radius impact analysis, OpenCypher queries, and deterministic CI architectural gates.
    32
    18
    MIT
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    Turns any OpenAPI spec into two sandboxed tools, search and execute, so an AI agent can discover endpoints and run small JavaScript programs against an entire API using under 1,000 tokens of context. Requests are read-only by default and the API key never enters the sandbox.
    MIT
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    Enables a model to write short Python programs that call a wrapped MCP server's tools as async functions, rather than invoking them one at a time. The program runs in a locked-down Docker sandbox, and every tool call passes through a host-side bridge that enforces an allowlist, schema-validated arguments, call budgets, timeouts and output caps.
    1
    MIT
  • A
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    quality
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    An enhanced sequential thinking tool optimized for programming tasks that helps break down complex coding problems into structured, self-auditing thought steps with branching and revision capabilities.
    1
    68 npm
    257
    MIT
  • A
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    Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.
    25
    76 PyPI
    5
    MIT
  • A
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    quality
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    MCP server for GitHub code retrieval and reuse, using SQLite+FTS5 indexing and search history to enable search-first, requirements-refined code search from GitHub repositories.
    9
    2
    MIT
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    Odds are, most of what you want to build is already open source. Tell your AI agent what you're making, and it finds it: which repos to use as they are, which to adapt, and what's left for you to write. Every repo it names is real, licensed for your use or flagged, and checked live.
    3
    79 npm
    1
    MIT
  • A
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    quality
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    An MCP server that gives orchestrator agents fine-grained control over interactive Claude Code sessions running inside tmux, enabling mid-session steering, interruption, and token-efficient result extraction.
    15
    MIT
  • A
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    quality
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    This MCP server enables AI models to analyze local Python codebases using abstract syntax trees, providing tools for file structure analysis, symbol search, import graphing, docstring auditing, and refactoring prompts without loading entire source files into context.
    5
    MIT
  • A
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    Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.
    10
    MIT
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    Rust-native code index MCP server with first-class 1C:Enterprise (BSL) support. Static binary, no runtime — 25 MCP tools (18 universal + 7 BSL-specific), tree-sitter AST for 10 languages, federation across multiple repos.
    161
    MIT
  • A
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    Not graded
    quality
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    Enables AI agents to safely execute Python code and shell commands in an ephemeral, isolated scratchpad with pre-execution AST security checks, timeout enforcement, token-shielded output, and file diff tracking. It also exposes static code-security inspection to flag forbidden imports and reflective escape attempts before execution.
    1
    MIT
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    MCP server over a curated catalog of 78 AI tools for engineering teams. Every entry states when it fits, when it does not, its limits, licence terms and open issue numbers, all verified against the project's own repository — search by keyword, category or licence, and get full records or published comparisons.
    Creative Commons Zero v1.0 Universal
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    Provides sandboxed code execution for AI agents with support for Python, JavaScript, and shell commands. Includes comprehensive safety features like destructive pattern blocking, timeout protection, and restricted file access for secure production use.
    37 npm
    37 PyPI
    MIT
  • A
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    MCP server that detects and guards against tool poisoning and prompt injection attacks in tool descriptions and schemas. It provides risk scoring, pattern detection, safe rewriting, and audit reports with zero external API cost.
    MIT
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    Enables AI coding assistants to run static analysis on GitHub repositories, raw code snippets, or local directories to detect cloud cost leaks in Terraform/AWS configs, PostgreSQL migration table-lock hazards, and OWASP/Next.js API security gaps. Each finding returns structured severity, line-level detail, and drop-in fixes.
    MIT