"Information about TomTom navigation systems or services" matching MCP servers:

A minimal MCP server providing a normalized API for code navigation and repository inspection through tools like symbol searching and call tracing. It enables AI agents to perform structured analysis of codebases by mapping endpoints, inspecting file trees, and tracing symbol relationships.

Provides tools for Python code navigation, analysis, and refactoring, including finding definitions, references, and symbol lists. It enables automated tasks such as renaming symbols and organizing imports to enhance AI-driven development.

Bridges the Model Context Protocol with Language Server Protocol to provide AI agents with persistent access to code intelligence features including navigation, diagnostics, refactoring, and completion across 7+ programming languages.

Translates a lockfile diff into a human-readable upgrade plan. For every dependency bump (npm or PyPI) returns semver class, breaking changes from GitHub release notes, CVEs fixed in the range, migration guide links, and a clear per-package recommendation. Bulk tool ranks up to 50 package changes in parallel by risk (security > caution > review > likely-safe > safe).

Dependency intelligence for AI agents. CVE scanning, health checks, upgrade planning.

MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.

Enables Claude Code to query codebase knowledge graphs directly, reducing token usage 5x–71x by reading a compact graph.json instead of raw files.

Content-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON

Enables an AI agent to audit an untrusted repository through eight parameterless MCP tools that run ten pinned security scanners inside a hardened container, returning native scanner reports, a run manifest tied to the scanned tree, and an audit log. The agent gets no shell, no target path, and no way to build or execute the audited code, so hostile repositories cannot steer the analysis.

Enables coding agents to list functions, scan for risky patterns offline, and generate plain-language explanations of Python, JavaScript, and TypeScript code, including summaries for only the functions affected by recent changes.

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.

RCP-1 is an enterprise-ready MCP server that connects AI assistants to SAP ABAP systems via the ADT REST API, with built-in security, access controls, and deployment options for BTP or on-premise.

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

Deterministic navigation maps over code AND markdown for AI agents: one MCP server, two lenses (tree-sitter TS/JS/Python + markdown). Map a whole project, read one function or one doc-section, ~99% less context. Merges codelens + docslens.

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 a coding agent to get short, token-efficient answers about a project through grouped searches, focused symbol or section reads, and bounded read-only script output, with safety limits on paths, secrets, commands, and response size.

Provides access to Ant Design component library documentation and information for automated code generation. Enables searching components by category or keyword, retrieving component props, examples, API documentation, and available icons.

A code-intelligence MCP server for coding agents that combines a static repository map (via tree-sitter) with precise live navigation (via LSP), providing structured, token-budgeted responses anchored to file:line.

Provides fast, indexed code navigation (definitions, references, callers, etc.) for AI coding agents by leveraging GNU Global (gtags), dramatically reducing context noise compared to grep.

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

MCP server for comprehensive code analysis, navigation, and quality assessment across 25+ programming languages.

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

MCP server that gives Claude deep awareness of your frontend codebase by scanning components, hooks, services, routes, and data flow for React or Vue projects.

MCP server that brings Octopus AI code review into editors, allowing review of code changes and asking questions about your codebase. Integrates with Cursor, Claude, or any MCP client via natural language.

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

Local-first code intelligence for coding agents. Exposes precomputed SCIP navigation, Zoekt lexical search, cross-repo blast radius, and semantic search as MCP tools.

Enables AST-aware code navigation, symbol renaming, dependency graph analysis, and architecture rule enforcement through 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.

Enables AI agents to correlate Ignition SCADA tags with Studio 5000 PLC logic, trace tags end-to-end from SCADA configuration to PLC rungs, and identify commissioning gaps between the two systems.