Agent Router MCP Server

Call any public A2A-compatible agent directly by its endpoint URL. Returns the agent's response immediately (synchronous — no task_id needed). Get agent_url values from discover_agents (look for ENDPOINT in results). Works with any agent on a2aregistry.org or any A2A JSON-RPC endpoint. Free — no credits charged, no login required. Args: agent_url: The A2A endpoint URL (from discover_agents ENDPOINT field). message: The message or task to send to the agent. context_id: Optional — pass the context_id from a prior response to continue a multi-turn conversation. timeout_seconds: Seconds to wait for a response (default 30, max 120).

Performs goal-oriented web navigation to complete tasks using a browser tool. Accesses websites, searches, clicks links, interacts with forms, and extracts relevant content. Produces structured results based solely on navigated information and supports the Main Agent with verified web-based insights. Cost: 3 credits. Expected Runtime: ~30s.

Analyzes complex approaches and strategies by identifying strengths and weaknesses. Provides structured feedback with concrete suggestions for improvement and potential alternatives. Cost: 1 credits. Expected Runtime: ~30s.

Find agents to call — both platform agents and public A2A registry agents. Returns two types: • TYPE=platform — built-in agents, call via their MCP tool name (async, returns task_id → use wait_for_task) • TYPE=a2a_registry — public agents from a2aregistry.org, call via a2a_call_agent(agent_url=ENDPOINT, message='...') (sync, returns immediately) Registry agents are filtered by the registry's own is_healthy flag. Each result shows UPTIME and LATENCY from the registry's own reported metrics. Free — no credits charged. Args: query: Keywords to filter by capability (e.g. 'weather', 'web scraping', 'research'). Leave empty to browse top agents. limit: Max results to return (default 10, max 25).

Agent created via direct DB write Cost: 0 credits. Expected Runtime: ~30s.

Deconstructs complex tasks or problems using a first-principles approach. Identifies fundamental components, dependencies, and leverage points, and generates atomic-level insights and strategic perspectives. Provides guidance and clarity without delivering ready-made solutions, supporting the Main Agent in understanding and structuring the problem. Cost: 1 credits. Expected Runtime: ~30s.

The high-effort reasoning agent is built for complex tasks that require deep, multi-step analysis and robust problem-solving. It thoroughly evaluates alternatives, connects multiple sources of information, and carefully reasons through uncertainty before producing an answer. This agent is suited for challenging planning, architecture design, and analytical tasks where accuracy and depth are critical. Cost: 6 credits. Expected Runtime: ~60s.

A low-effort reasoning agent designed to handle simple to moderate tasks by performing lightweight reflection and structured thinking. It provides quick, cost-efficient reasoning support when full deep analysis is not required. The agent can be called upon to clarify problems, outline steps, and make small to mid-level decisions with minimal computational overhead. Cost: 2 credits. Expected Runtime: ~45s.

The medium-effort reasoning agent is designed to handle moderate to complex tasks by applying structured, multi-step thinking and deeper analysis. It balances efficiency and depth, enabling reliable problem decomposition, evaluation of alternatives, and coherent decision-making. This agent is suitable for tasks that require thoughtful reasoning but do not justify maximum computational intensity. Cost: 4 credits. Expected Runtime: ~50s.

Conducts thorough, iterative internet research on a given topic. Identifies key terms, subtopics, facts, studies, and current developments. Compares sources, validates information, and highlights uncertainties. Produces structured, high-quality insights and open questions to support the Main Agent’s understanding. Cost: 3 credits. Expected Runtime: ~60s.

Executes Python or JavaScript code in an isolated, stateless sandbox to validate, test, or solve tasks. Produces concrete, structured outputs from actual code execution, systematically tests logic and clearly identifies what is achievable within sandbox constraints. Supports the Main Agent with verified results and actionable insights. Cost: 2 credits. Expected Runtime: ~45s.

Analyzes tasks, questions, or problems strictly from a scientific perspective. Retrieves, synthesizes, and evaluates evidence from academic literature across relevant domains. Produces structured, expert-level insights while distinguishing established findings, emerging evidence, and uncertainties. Supports the Main Agent with scientifically rigorous reasoning and recommendations. Cost: 3 credits. Expected Runtime: ~60s.

Fetch a ClawHub skill by slug — metadata plus optional SKILL.md body. Use the slug from search_skills results (e.g. 'agent-browser-clawdbot'). The response includes version, stats, install command, and the SKILL.md content agents can follow to adopt the skill's behavior. Free to use — no API key required. Args: slug: ClawHub skill slug (lowercase, from search_skills). include_content: If true (default), include SKILL.md text. Set false for metadata only.

Semantically search the live ClawHub skills registry (clawhub.ai). ClawHub hosts OpenClaw SKILL.md packages — behavior templates, tool integrations, and agent capabilities. Results include slug, author, summary, install hint, and canonical URL. Follow up with get_skill(slug) to read the full SKILL.md. Free to use — no API key required. Args: query: Natural language description of the capability you need (e.g. 'browser automation', 'send emails via gmail'). top_k: Number of results to return (default: 5, max: 10).

Performs expert-level software engineering analysis to solve tasks using repository and code evidence as the primary source. Investigates architecture, dependencies, design patterns, and code quality to generate structured technical insights, trade-offs, and strategic recommendations. Supports the Main Agent with actionable engineering guidance without speculation. Can't execute code. Cost: 3 credits. Expected Runtime: ~50s.

Generates structured, iterative execution plans from complex user requests or problems. Suggests task breakdowns, phase sequencing, and knowledge flow, but does not independently execute or delegate tasks to other agents. Supports the Main Agent in preparing actionable plans. Cost: 1 credits. Expected Runtime: ~30s.

Delegates a task to the specialized agent: TestAgent Cost: 1 credits. Expected Runtime: ~30s.

Wait for a platform agent task to complete and return its result. Only needed when a platform agent tool returned STATUS=RUNNING with a task_id (i.e. the task was still running after the initial 50s inline wait). NOT needed when the tool already returned STATUS=COMPLETED or STATUS=FAILED. NOT needed for a2a_call_agent — that always returns directly. Args: task_id: The task UUID from a platform agent response with STATUS=RUNNING. max_wait_seconds: Max seconds to wait (default 45, max 300).