Agent wallet and budget management for AI agents.
Agent payments, API key vaulting, and governed mandates. Agents spend within user-defined limits.
Centralized API gateway routing to MCP servers with per-call billing.
Security guardrails for AI agent payments.
Run MCP tool calls through Runestone Gatekeeper — policy enforcement, human approval, audit.
Governed execution cells for AI agents: Docker/K8s sandboxes with policy, audit, and approvals.
Operator-as-agent MCP hub. 6 tools. First $5 free, then $0.001/call.
Agent-harness picks and decision guides; pick_infrastructure adds live GitHub/HN discovery.
Claude Desktop reminders for Windows. Survives MSIX boundary; persists across restarts. Zero deps.
Technical SEO tools for AI agents: structured data, meta tags, robots, SERP truncation. No API key.
33 image tools: free local editing, AI upscaling, background removal, and product photos.
League of Legends math for AI agents: MMR estimation, climb projections, KDA, hours. No API key.
Twitter/X tools for AI agents — read timelines, post tweets, search, and engage.
Grant management for AI agents: deadlines, business-day math, budgets, in-kind value. No API key.
Read and write WiseGrid sheets, rows, columns and reports from any AI agent.
MCP server for browsing, searching, exporting, and backing up your Cursor AI chat history.
Minimal stdio MCP server for parallel task execution by AI agents.
Enterprise-ready portable memory for AI agents: encrypted, shareable, provably erased.
Enterprise-ready portable memory for AI agents: encrypted, shareable, provably erased. Join SAIHM.
SAP Fiori - Model Context Protocol (MCP) server.
Model Context Protocol (MCP) server for AI-assisted development of MDK applications.
Doctor-reviewed blood-test markers, conditions & symptoms as agent tools. EN/RU/HE. Hosted.
Embedded, local-first memory and retrieval for AI agents. One SQLite file, no server, no API key.
Temporal engineering context for agents via MCP.
About AI & Agents MCP servers
This category collects servers that make agents better at being agents. You will find orchestration layers that run multi-step pipelines, routers that hand a task to the right model or sub-agent, evaluation and tracing tools, prompt and skill libraries, and bridges that let one assistant call another. Some wrap commercial model APIs; others are thin coordination layers around open-source frameworks.
Because these servers often sit between your agent and other systems, look closely at where data flows and what is logged. Prefer servers that document their tool contracts clearly, support streaming where relevant, and can run locally when the workload is sensitive. Many are new and move quickly, so check the repository's recent activity before relying on one in production.