"Find people on LinkedIn by name" matching MCP servers:

Provides AI assistants with progressive code intelligence to explore repository structure, find symbols, and assess the impact of changes using ast-grep.

Knot is a semantic and structural codebase indexer designed for AI coding agents and developers navigating large projects. It combines vector search and graph traversal to find code by meaning, analyze impact via reverse dependencies, and explore file architectures.

Enables AI agents to search Minecraft obfuscated name mappings (class, method, field) across 38 versions, aiding modding, plugin development, and reflection.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

A field-level semantic change impact analysis MCP server for Claude Code, enabling natural language queries to find affected code when modifying fields, strings, or functions across multiple languages.

Provides JavaScript/TypeScript code intelligence via LSP, enabling goto definition, hover info, and find references through MCP tools.

Enables semantic and keyword search over a local codebase by chunking source files along function and class boundaries, embedding them offline, and fusing vector and BM25 results. Runs entirely on your machine in a single SQLite file, so AI coding agents can find relevant code with no API keys, telemetry, or cloud calls.

Enables efficient codebase exploration with tools to list exports, get file outlines, read targeted regions, and find references without loading entire files.

Assigns personality types to functions, rates variable name energy, and suggests aesthetic alternatives for code, providing an unscientific yet entertaining vibe assessment.

Provides project-wide semantic PHP refactoring tools (find references, rename classes/members, move class, class info) for AI assistants by wrapping Phpactor.

Enables precise source code retrieval via tree-sitter AST parsing, cutting AI token costs by 86-99% by indexing codebases and fetching only needed symbols and structural queries (e.g., blast radius, importers) on demand.

Provides intelligent semantic code search using local AI embeddings, enabling natural language queries to find relevant code by meaning rather than exact keywords. Indexes codebases in the background with smart project detection and privacy-first local processing.

A Model Context Protocol server that provides tools to find regex pattern positions in files and list allowed directories, enabling text analysis with LSP-like functionality.

Test coverage intelligence MCP server for TypeScript and JavaScript projects. Analyzes coverage reports to find untested functions, computes cyclomatic complexity, and suggests test cases locally.

Provides GraphRAG code search to AI agents, combining structural code graph traversal with semantic and keyword retrieval so agents can find relevant code without exact name matches.

MCP server providing dependency and package management tools for AI agents. Analyze licenses, find outdated packages, visualize dependency trees, estimate bundle sizes, and audit security vulnerabilities.

An MCP server that enables AI to interact with the private 'my-design' React component library and design tokens for UI generation and technical support. It provides tools for component searching, API documentation retrieval, and migration guidance based on specific internal design specifications.

MCP server that indexes TypeScript/React project symbols in memory, enabling agents to find definitions, references, and dependencies without reading the whole repo. Also includes a local visual dashboard with no outbound network calls.

Bridges MCP and LSP to enable semantic code analysis, including go-to-definition, find references, hover info, and function call relationship checks.

Put your node_modules on a diet — find unused dependencies, analyze package health, and reduce size.