"Finding People on LinkedIn" matching MCP servers:

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 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.

Exposes Semgrep as deterministic, rule-matching analysis tools over MCP so a code-review agent can scan only the lines changed in a diff, scan whole files on demand, and check analyzer availability, version, ruleset, and limits. Results are returned as compact per-finding lines with source snippets suitable as comment anchors, along with counts of suppressed out-of-diff matches.

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.

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

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

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.

A Model Context Protocol server that helps AI code editors locate TypeScript symbol definitions in codebases, particularly useful for finding original definitions of imported symbols and classes.

Provides LLMs with safe, read-only access to local codebases for searching, reading files, and finding function definitions. All source code remains local, ensuring privacy while enabling AI assistants to explore project structures and functionality.

Kin is an open-source code repository for people and AI agents. It versions code and recorded relationships together.

Local repository health diagnosis and fix planning for AI-written JavaScript, TypeScript, Python and mixed projects. Its stdio MCP server exposes scans, finding explanations, repair plans, verification, privacy review and DPDP technical readiness checks.

Enables AI-powered code analysis and repository scanning through six MCP tools, including house categorization, friction detection, recursive pattern finding, and equilibrium evaluation, with conversation memory and 24/7 cron operation on Cloudflare's free tier.

AST-aware TypeScript/JavaScript codebase exploration for AI agents, providing high-precision symbol resolution, reference finding, and structural analysis via MCP tools.

Enables coding agents to query a deterministic AST knowledge graph of a codebase over MCP—finding symbols, callers/callees, dependencies, impact, and relevant tests via compact path:line JSON—so they can navigate code without grepping, embeddings, or an LLM.

Provides unified development tools including code analysis, debugging, refactoring, documentation, testing, and project automation through multiple LLM providers (KIMI, GLM, OpenRouter). Features agentic audit capabilities with multi-model consensus for finding issues and generating direct fixes.

Enables source-first orchestration and knowledge indexing with inspectable architectural capabilities through a unified MCP interface, including named-attribute retrieval and atomic knowledge ingestion.

MCP server for local-first lexical code search, providing tools for searching code, finding symbols, and reading chunks from indexed repositories.

Connects AI agents to local analysis tools that inspect native binaries, JavaScript/Electron apps, .NET assemblies, and websites without source code, so features can be explained, traced, and rebuilt. Every finding is returned with its supporting evidence and stated limitations.

Enables AI coding agents to perform semantic code search locally, finding code by meaning rather than exact keywords.