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

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

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 an AI assistant to debug ABAP code on a connected SAP system by setting breakpoints, running code, and capturing call stacks and variables, with one-shot or interactive stepping.

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

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.

Enables any general-purpose agent to mod IL2CPP, Mono (.NET), and Unreal games by resolving hook targets from engine metadata rather than memory guessing. Provides 45 tools covering the full workflow of finding a game, indexing types/fields/methods, injecting a Frida probe, hooking methods, and reading or writing game memory.

Enables users to scan a website, project folder or HTML file for generic AI-generated copy, template design and unfinished details, scoring each finding with file-and-line evidence and a per-page breakdown. It turns the results into phased, step-by-step repair plans that Claude, Cursor and other AI tools can apply, then re-measures the score after each change.

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.

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.

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.

Enables token-efficient local code retrieval by indexing a codebase once with tree-sitter and serving exact symbol bodies on demand, avoiding whole-file reads.

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 AI coding assistants to run pre-publish security and code-quality scans across 17 categories, generate a 0–100 security score and Markdown report, quickly check critical vulnerabilities, and receive fix/refactor suggestions by finding ID.

Enables coding agents to map a multi-repo workspace by resolving which repository a service name refers to, reading short per-repo cards, listing related repos with file-and-line evidence, finding where a table, package, path, or symbol lives across repositories, and refreshing the map. It answers orientation, localization, and cross-repo impact questions without the agent re-exploring every sibling repo.

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.