"Resources for Building Agentic AI" matching MCP servers:

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

Provides real-time access to Japanese building standards laws and regulations from e-Gov API, helping prevent AI hallucinations by validating citations against original legal texts.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

Enables MCP clients to read, summarize, create, update, and delete tasks on a team task board according to the caller's tenant and role. It also supports running the same board operations exposed to forms, JSON routes, Artisan, and the copilot.

Provides comprehensive building and office address information queries for enterprises in China, including fuzzy enterprise search, office address details and statistics, and building information queries to support geographical distribution and occupancy analysis.

Eleven local stdio MCP servers exposing a reproducible EPA SWMM stormwater-modelling workflow: model building, simulation with run manifests and continuity checks, calibration, GIS/QGIS, design storms and climate scenarios, uncertainty analysis, plotting, and modelling memory.

A persistent, cross-referenced engineering knowledge base for AI agents, with MCP tools for wiki knowledge, agent runtime, and repository context. Preserves sources and citations, surfaces knowledge-quality issues, and PIN-gates private scopes and write/admin operations.

Google AdSense for AI agents, enabling MCP server monetization by serving privacy-respecting contextual ads and earning 70% revenue share on clicks.

An MCP server that transforms natural language questions into SQL, executes queries on PostgreSQL, and returns human-friendly responses, with Redis memory for context.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

Enables AI coding agents to orchestrate the full software development lifecycle on GitHub, including planning, issue creation, code review, security triage, and release readiness checks.

Provides a Model Context Protocol server with tools for knowledge base search, retrieval, and addition, plus system time, resources, and prompts. Supports stdio and Streamable HTTP transports for local and shared deployments.

A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

Enables AI-driven orchestration of GitHub development workflows including automated issue analysis, code generation, code review, and PR creation through multiple specialized agents. Integrates with GitHub Actions to automate the complete development process from issue to pull request.

A persistent, bitemporal, injection-safe memory MCP server for coding agents, offering unified retrieval across six memory layers, non-destructive belief revision, and multi-tenant namespace isolation.

Agentic RAG Knowledge Assistant is a secure, tenant-isolated MCP server built with FastAPI, PostgreSQL, and pgvector that enables document ingestion, semantic retrieval, and vector search over PDF, DOCX, and text files through authenticated MCP tools.

Enables natural-language-driven analytics over CSV/Parquet or built-in data by decomposing questions into analytical tasks, running them via deterministic DuckDB and SciPy tools, and publishing only claims verified against the underlying rows.