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

Provides access to real-time LLM pricing, speed metrics, and performance benchmarks for over 300 models from Artificial Analysis. It enables users to list, filter, and compare models based on costs, tokens per second, and intelligence indices.

Enables comparing and recommending AI models based on scenario-specific criteria such as capability, context, relative cost, and self-hosting, with explanations of the scoring weights.

Enables AI assistants like Claude Code and Cursor to get evidence-based tool and workflow recommendations from community data.

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.

Enables an AI to build, inspect, test, and render logic circuits as .dig files, then open them in the Digital app.

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

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.

Supply chain risk scoring for npm, PyPI, and GitHub repos

A multi-agent orchestration system that enables multiple Claude instances to collaborate through a centralized hub with a shared workspace and real-time communication. It features integrated task management, role assignment, and persistent memory to facilitate complex, synchronized agent workflows.

Provides read-only navigation of Conductor project intelligence, exposing architecture notes, agent inventories, feature traces, and operational documentation through MCP tools.

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.

A meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.

Provides a set of everyday utility tools via MCP, including greetings, calculations, time, UUID generation, secure passwords, BMI/age calculation, and unit conversion.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

An MCP server that recommends UI components and utility methods from private code repositories using AI to enhance code reuse and development efficiency.

Free, self-hosted MCP server that reports which URLs ChatGPT, Claude, Perplexity, Gemini, and Bing cite for any query. Install with npx -y @automatelab/citation-intelligence.

Enables AI coding agents to make evidence-aware design-pattern decisions by analyzing cases, comparing options, detecting misuse, stress-testing assumptions, planning adoption, writing ADRs, and generating evidence plans with transparent scoring.

Chain of Draft Server is a powerful AI-driven tool that helps developers make better decisions through systematic, iterative refinement of thoughts and designs. It integrates seamlessly with popular AI agents and provides a structured approach to reasoning, API design, architecture decisions, code r