"Finding Information About Public Places" matching MCP servers:

Enables MCP clients to run deterministic checks on text inputs: validating a spec, checking tests against it, and checking an implementation's source, all by parsing Python without executing it. The client supplies the model while the server validates, verifies, and reports structured verdicts, problems, and violations.

A read-only MCP server that verifies public GitHub commits against static contracts and returns signed receipts, supporting only exact lowercase 40-character commit SHAs.

Connects Claude Code to a Latent Defense deployment to map GitHub repositories into an infrastructure graph, discover attack paths using the JEPA energy model, and triage findings from the terminal.

It enables users to interact with a codebase through hybrid retrieval, code graph navigation, impact analysis, and context preparation for AI coding agents.

Semantic code knowledge for your stack. Hosted MCP at over 800+ pre-indexed OSS libraries (C#, Java, TS, Python, Rust, PHP+). 17 SCIP-backed tools: real source, callers, usages, tests, type hierarchies, and outline — one call each, free.

AI-powered MCP Server for Secure Coding. Zero noise, instant proof.

Memtrace is a persistent memory layer for coding agents, built as a bi‑temporal structural knowledge graph over your codebase (AST‑driven symbols and relationships, plus temporal evolution and cross‑service API topology)

Enables querying any GitHub, GitLab, or Bitbucket repository and getting AI-generated answers about the codebase, powered by Gemini.

Enables asking natural-language questions about any GitHub repository and receiving answers grounded in the actual code, with read-only enforcement and automatic repository fetching.

truth is a deterministic fact-checker for the claims an AI coding agent makes about its own work.

Vet ClawHub skills before installing them; detects prompt-injection, exfiltration, and other security issues, outputting a risk score with per-finding evidence.

Detects breaking changes in a TypeScript library's public API and recommends the semver bump, comparing two npm releases, git refs or a working tree. Each finding is graded proven or review-only.

Enables Claude to explore, analyze, and answer questions about any public GitHub codebase by connecting to DeepWiki's AI-powered documentation.

Semantic git queries via MCP. Beyond git log — answer who/what/why about any line, file, or branch with blame, co-change, PR linkage.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

MCP server that provides npm package information for AI agents during TypeScript development.

Extracts deterministic architecture maps from codebases for AI agents, enabling queries about blast radius, routes, security findings, and production readiness without sending code anywhere.

Enables AI agents to search and retrieve details about Odoo code elements (models, fields, views, etc.) with exact file locations using MCP tools.

Provides coding agents with a compact map of a TypeScript/TSX codebase, enabling direct answers about symbol locations, imports, dependencies, and references while reducing token usage.

A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation

Enables AI coding assistants to perform local semantic code search using AST parsing and embeddings, finding code by meaning with exact file and line locations, plus AST-based usage lookup.

Local-first, code-aware engineering cognition for AI coding agents - persistent memory, contextual retrieval, and continuous cognition about your codebase.

Enables evaluating evidence against independent predicate, choice, and score questions across text, project files, images, and PDF pages, and finding relevant code ranges without returning file contents.

Indexes a workspace and exposes code-navigation tools to Codex, enabling symbol lookup, reference finding, dependency analysis, and security audit for local codebases.

Enables agents to validate Dockerfiles for build-breaking syntax errors, risky patterns like root users and secrets in ENV/ARG, and cache-order problems, returning every finding with its line and a fix. It also verifies referenced container images against public registries, reporting tag existence, digests to pin, supported platforms, last rebuild time, and end-of-life status with an upgrade tag.

Exposes boyter/scc code counting and complexity analysis to LLM agents via read-only tools like counting lines, finding top files, and cost estimation.

Enables storing and querying structured information about software code entities (classes, functions, files) and their relationships (calls, imports) along with qualitative observations like design decisions and change rationale.

Provides code refactoring capabilities for TypeScript/JavaScript and Python through Language Server Protocol integration. Enables renaming symbols, extracting functions, finding references, and moving code between files via natural language commands.

Helping coding agents never make mistakes working with public or private libraries without wasting the context window.

Exposes Lachesis's code navigation tools over MCP, letting LLM agents ask precise compiler-level questions about dataflow, taint, callers, and guards in source code.