"AI Footprints - Environmental and Digital Impact of Artificial Intelligence" matching MCP servers:

Read-only MCP server for cross-stack impact analysis across Java Spring Boot backend and TypeScript frontend linked through OpenAPI.

Fast pre-commit dependency gate for AI-assisted code changes. Answers "is this safe to commit?" with a PASS/WARN/BLOCK verdict in seconds, so you can catch risky blast radius before a bad commit, not after it. No database, no heavy setup.

Analyzes code changes using static code-graph and git co-change history to identify likely affected files, returning tiered confidence reports with evidence. Exposes tools for AI assistants to inspect current changes, static impact, historical co-change patterns, and detailed explanations.

Privacy-first, read-only repo intelligence for AI coding agents: scans local codebases and recommends MCP servers, repos, and research without sending data to the cloud.

An intelligent MCP toolset for software testers that monitors code changes, analyzes test impact, recommends tests, and assesses risk, supporting multiple AI coding frameworks via dual transport modes.

MCP server that analyzes TypeScript/JavaScript codebases via AST parsing and dependency graph tracing to identify affected tests, detect dead code, circular dependencies, and trace import chains, enabling AI agents to run only relevant tests.

A local-first MCP server that gives AI coding agents durable project memory, dependency graphs, and impact analysis to answer team knowledge and cross-file change questions before editing.

MCP server providing 29 tools across 5 layers for semantic TypeScript/JavaScript code intelligence, enabling AI agents to find references, trace impacts, guard APIs, and explain errors without text-search false positives.

AI-powered smart contract security analysis for AI agents and developers, enabling scanning of Solidity repos for vulnerabilities.

An MCP server that gives AI coding agents the ability to analyze a Python repository's test health: coverage, flaky tests, and ML-based pull-request risk prediction.

Provides codebase indexing and retrieval tools that give AI agents token-efficient, query-relevant context packages (symbols, imports, and dependencies) instead of scanning entire repositories.

Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.

Enables coding agents to search locally indexed repositories with hybrid semantic and lexical retrieval, returning exact source citations with file paths and line ranges.

Hardware-accelerated codebase mapping that indexes Git repositories into Postgres/pgvector and serves code search, relationships, and static analysis results via a stdio MCP server.

An MCP server that gives LLMs structured, read-only insight into local code repositories — directory tree, languages, dependencies, scripts, and config files.

A production-grade MCP server providing advanced code intelligence capabilities such as semantic search, dependency analysis, and natural language Q&A across multiple programming languages.

Provides AI coding agents with fast, deterministic access to code graphs, knowledge lifecycle state, and cross-document symbol links, enabling compact context retrieval and seamless session handoffs.

Intelligent code search MCP server with AST analysis, call graphs, dependency tracking, and semantic embeddings for developers.

Provides a semantic understanding of your codebase by parsing with tree-sitter and building a graph of symbols and dependencies. Enables AI assistants to navigate code, analyze changes, and discover architecture using 18 tools with minimal context overhead.

Enables AI coding assistants to ground on real D365 F&O AOT metadata locally and offline, preventing hallucinated X++ field names and Chain of Command signatures.