MCP serverio.github.Tim-Projekt/agent-router

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 di

Performs goal-oriented web navigation to complete tasks using a browser tool. Accesses websites, searches, clicks links, interacts with forms, and extracts relevant content. Produc

Plan complete camping trips based on destination, weather, experience level, group size, budget, and activities. Generate packing lists, meal plans, safety checklists, itineraries,

Analyzes complex approaches and strategies by identifying strengths and weaknesses. Provides structured feedback with concrete suggestions for improvement and potential alternative

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, retur

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

full financial agent Expected Runtime: ~60s.

Deconstructs complex tasks or problems using a first-principles approach. Identifies fundamental components, dependencies, and leverage points, and generates atomic-level insights

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 versio

Demo agent that echoes back any text query it receives. Useful for testing A2A webhook integrations. Expected Runtime: ~10s.

Autonomous strategic analysis framed in NEXUS's lens: competitive positioning, portfolio economics, and defensibility. Returns structured, actionable strategy recommendations for b

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 multipl

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 s

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,

Conducts thorough, iterative internet research on a given topic. Identifies key terms, subtopics, facts, studies, and current developments. Compares sources, validates information,

Проводит доказательный аудит сайтов по действующему законодательству РФ: сканирует публичные страницы, формы, cookies, документы, платежные сценарии и внешние сервисы, сверяет прим

Executes Python or JavaScript code in an isolated, stateless sandbox to validate, test, or solve tasks. Produces concrete, structured outputs from actual code execution, systematic

Analyzes tasks, questions, or problems strictly from a scientific perspective. Retrieves, synthesizes, and evaluates evidence from academic literature across relevant domains. Prod

Semantically search the live ClawHub skills registry (clawhub.ai). ClawHub hosts OpenClaw SKILL.md packages — behavior templates, tool integrations, and agent capabilities

Creates a complete home restaurant experience from the household fridge, guest count, cuisine, time and budget. Builds menus, identifies missing ingredients, prepares a transparent

Tracks household food inventory, expiry risks and replenishment needs; considers user food restrictions and preferences; prepares transparent product recommendations, a draft Silpo

Performs expert-level software engineering analysis to solve tasks using repository and code evidence as the primary source. Investigates architecture, dependencies, design pattern

Generates structured, iterative execution plans from complex user requests or problems. Suggests task breakdowns, phase sequencing, and knowledge flow, but does not independently e

Delegates a task to the specialized agent: TestAgent 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 ru