MCP servers that give coding agents real developer tooling: linters, package registries, test runners, language docs and local build commands. 13,953 servers in this category.
Allows AI assistants to access long-term time-series sensor history from Home Assistant via a specialized MCP server.
Performs Python code audits using taint-path static analysis and AI-driven verdicts to identify security vulnerabilities and code quality issues.
Enforces security invariants, masks secrets, and provides cryptographic audit receipts for autonomous AI agents and MCP tool runtimes.
Allows an AI assistant to perform read-only analysis of Reddit Ads performance, including campaigns, subreddit breakdowns, and reporting metrics.
Organizes Claude Code and Codex sessions into a unified dashboard with project maps, task tracking, and spoken daily recaps.
Rebuilds PDF tag trees and recovers character maps to create accessible, screen-reader-friendly documents.
Audits repositories against Microsoft's Secure Future Initiative pillars using a read-only scanner and a NIST CSF 2.0 mapped knowledge base.
Enables an AI client to analyze Java heap dumps by running Eclipse Memory Analyzer queries and retrieving structured results.
Mounts MCP servers into Hayate applications across ASGI and Cloudflare Workers using a streamable HTTP boundary.
Performs SAST, secrets, and dependency audits to provide security findings and fix suggestions for AI-driven or human-written code.
Analyzes daily commits across multiple repositories to produce structured Markdown reports and quality dashboards using AI.
Generates schema-driven, type-safe PostgreSQL DML tools for AI agents to perform scoped insert, select, update, and delete operations.
Enables AI agents to perform code discovery and repository searching in air-gapped or firewalled environments using bundled embedding models and grammars.
Provides a local-only voice runtime for MCP clients using Whisper and Kokoro for speech-to-text and text-to-speech on macOS.
Identifies impacted user journeys from code diffs and runs relevant tests while flagging uncertain or untested paths.
Enables AI clients to perform deep codebase comprehension using a hybrid Graph-RAG approach that combines AST call graphs with vector search.
Enables coding agents to track machine learning experiments and access historical research data to avoid repeating previous team efforts.
Provides durable memory, decentralized coordination, and a knowledge lifecycle for multi-agent AI development across local and remote environments.
Turns conceptual Markdown or plain text into source-anchored knowledge graphs that can be queried, challenged, and visualized with provenance.
Provides a test set of address-specific property records and an answer grader to evaluate AI accuracy in geospatial and official source retrieval.
