"A router for MCP (Model Context Protocol) tools" matching MCP servers:

Cost-aware multi-LLM routing MCP server that automatically routes tasks to free models first with fallback to paid, using task difficulty classification.

Enables a Codex orchestrator to obtain a deterministic, local recommendation for a model and reasoning effort for a delegated coding task, without inference calls or API keys.

Enables selecting the best AI model and reasoning effort for any task by analyzing task requirements and live pricing, balancing intelligence, speed, and cost. Provides model recommendations, comparisons, and routing status through MCP tools.

Enables users to route Codex desktop chats to subscription models or their own OpenAI-compatible providers by adding Base URLs, API keys, and model catalogs from an in-app settings panel. Keeps keys DPAPI-encrypted, supports Responses and Chat Completions with streaming and tool calls, and needs no separate configuration app or Windows autostart.

Enables AI agents to route chat completion requests to task-appropriate models via LiteLLM/OpenRouter, track usage and budgets, and run side-by-side eval comparisons of models based on cost, latency, and response quality.

Enables agents to load instruction modules on demand from linked git repos, keeping the always-loaded AGENTS.md slim. It syncs each module's repo before reading and returns the module text with the commit it was read at.

Routes model requests to Sol, Terra, or Luna via OpenClaw Gateway's Codex auth profile, with strict validation and no fallback.

Enables zero-auth agent workflows for email OTP extraction, KV checkpoints, webhook bins, wake timers, and clean URL-to-Markdown reading.

An event-driven MCP server that enables agents to share context streams, publish and subscribe to events, manage tasks, and follow protocols, keeping a fleet of agents mutually context-aware in real time.

A server built with mcp-framework that allows users to extend Claude's capabilities by adding custom tools that can be used through the Claude Desktop client.

A unified API server that enables interaction with multiple AI model providers like Anthropic and OpenAI through a consistent interface, supporting chat completions, tool calling, and context handling.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

Enables agent harnesses to make fast, confidence-aware tool selections using Jev, with planner fallback, hierarchical routing, and optional MCTS lookahead, exposed natively over MCP stdio.

Trust infrastructure for the agent economy.

Security-enforcing MCP proxy that sits between an AI agent and any number of downstream MCP servers, intercepting every tool call through a capability-token policy gateway that can allow, deny, or escalate to human approval before the call reaches any real tool. It also exposes built-in operator tools for approval workflows, audit trail queries, token management, voice/HUD output, and hierarchical

AIFeed Open Standard. AIFeed Protocol to the building tools section. AIFeed enables web publishers to provide verified, token-efficient content feeds for AI agents.

A service discovery and proxy for MCP servers that enables registration, discovery, and execution of tools on remote MCP servers. Uses vector-based similarity search through Alibaba Cloud services to intelligently route requests to appropriate MCP services.

Enables MCP clients to route multi-model AI agent steps through cost-aware tier selection, confidence-gated cascade escalation, circuit-breaker failover, semantic caching, and tenant/workflow budget enforcement. Exposes tools for routing, task complexity classification, endpoint health inspection, and budget status checks.

Enables AI agents and MCP clients to dispatch queries to the most suitable model among 70+ LLMs by scoring intent, budget, cost, and latency, returning structured routing decisions with execution telemetry. It runs as a zero-dependency Python MCP server that plugs into Claude Desktop, Cursor, and other MCP-compatible clients.

Universal coordination hub for AI agents. Find collaborators, negotiate terms, form contracts, and build reputation through an MCP interface. Supports natural language search across agent networks.

Enables multiple Claude Code sessions to share a single HTTP MCP endpoint, lazily spawning stdio MCP servers only when their tools are called, reducing idle processes and memory usage.

Enables clients to connect once and access the union of multiple backend MCP servers' tools through a single unified interface, with namespaced tool routing, capability search, and health isolation.

A proxy server that bridges AI agents and external APIs by dynamically translating OpenAPI specifications into standardized MCP tools, enabling seamless interaction without custom integration code.

Enables agents to search a local catalog, load skills, inspect MCP tool schemas, and invoke selected tools through a single unified MCP tool, reducing per-request overhead.

A durable MCP control plane for starting, observing, steering, continuing, cancelling, and handing off long-running coding agents, with bounded MCP calls and persistent worktrees.

Enables dynamic, intent- and budget-aware routing of queries across 70+ LLMs by balancing cost and latency through Pareto-optimal model selection. Integrates with MCP clients like Claude Desktop and Cursor for automated multi-model execution.

Aggregates multiple MCP servers behind a single local endpoint, letting LLM clients connect to one router that exposes a unified set of tools, resources, and prompts.

Enables multi-agent coordination with file locking, shared registry, async messaging, and architectural decision logging via MCP tools.

Aggregates and routes to hundreds of downstream MCP servers behind a single interface, exposing only five meta-tools and dynamically retrieving the relevant tool for an agent on demand.

Automatically selects the optimal LLM model for each task in Cursor IDE by analyzing query complexity, task type, and applying customizable routing strategies across 17 different AI models.

Enables efficient tool routing and context budgeting for AI agents handling hundreds of MCP tools, with circuit breaker, RBAC, and federation for reliable serving.

A Python implementation of the MCP server that enables AI models to connect with external tools and data sources through a standardized protocol, supporting tool invocation and resource access via JSON-RPC.

An MCP server that seats multiple LLMs as a council, letting your assistant query them in parallel or in sequence, relay answers for cross-critique, and merge conclusions within one conversation.

Aggregates and routes multiple MCP servers with intelligent tool recommendation and batch parallel execution, enabling unified access and efficient tool usage.

Enables AI agents to convert a natural-language task into ranked x402 and MCP tools from a merged 16k+ catalog in a single fast pass without an LLM call, with pay-per-call USDC on Base and a free tier.

Routes SKILL.md libraries to any MCP client, enabling task matching and skill loading with embedding-based scoring, keyword fallback, and context-window discipline.

This MCP server enables LLM hosts to plan large tasks and delegate subtasks to specialized local CLI agents, such as Codex and Claude Code, using tools like delegate_task and get_agent_roster.

Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.

MCP server that pings 130+ free coding LLM models across 17 providers in real-time, ranks them by latency, and helps AI agents pick the fastest available model.

Enables orchestrator agents to route coding tasks to native CLI worker agents via MCP, providing tools for agent discovery, task submission, waiting, inspection, and cancellation.