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

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.

MCP server that provides impact preview and approval workflow for AI agent actions, allowing users to see diffs and risk assessments before any changes are executed.

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.

Enables AI assistants to query your local browsing history using keyword search, RAG Q&A, and activity stats.

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.

Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.

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.

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.

A local MCP server for intelligent PDF management that converts papers to markdown and enables hybrid grep and semantic search, allowing token-efficient exploration of academic documents.

Provides MCP tools to search, retrieve, and answer questions from the public Open Finance Brasil Confluence documentation using BM25-based local indexing.

Exposes digital twin device data capabilities as MCP tools, allowing MCP clients to query device status, read real-time metrics, fetch time-series data, and check alerts. Includes a simulated PLC driver with a clean interface for connecting real devices via OPC-UA, Modbus, or gateway APIs.

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 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.

MCP server providing virtual memory for AI context with durable SQLite storage, retrieval, and bounded prompt assembly to manage context windows.

Obsidian Intelligence turns your vault into a Model Context Protocol server. Any MCP-enabled AI client (Claude Desktop, Claude Code, Cursor, OpenWebUI, Cline, Continue.dev, ChatGPT Desktop) can then search, analyze, and reason over your vault — without your notes ever leaving your machine.

Enables querying normalized real-world asset data through MCP tools, including listing assets, retrieving asset details, comparing yields against SOFR, and checking eligibility by jurisdiction.

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

Enables AI agents to perform workspace file operations (read/write/recover) with transaction-safe guarantees, durable recovery, and explicit human approval via a separate Broker.

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.