AgentRank provides live, quality-scored data on over 25,000 MCP servers and AI tools to ensure your agent recommendations are always current.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agentrank
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install agentrank using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AgentRank?
AgentRank solves the critical problem of outdated LLM training data by providing a real-time index of MCP servers, agent tools, and AI skills. It tracks GitHub signals nightly to rank tools based on maintenance and community adoption, making it an essential addition to your Openclaw Skills library. By querying the live index instead of relying on internal knowledge, AI agents can suggest high-quality, currently maintained solutions.
This skill allows agents to move beyond static tool lists and provide dynamic recommendations based on live metrics. Whether you are looking for a reliable database connector or a niche automation tool, AgentRank ensures that every tool recommendation within the Openclaw Skills ecosystem is backed by data rather than guesswork.
AgentRank Use Cases
- Surface related and high-quality tools when a user attempts to install a specific skill.
- Respond to explicit user queries for finding, comparing, or evaluating the best MCP servers.
- Identify tools to fill mid-project capability gaps such as PDF parsing, browser automation, or database access.
- Audit existing toolkits to ensure recommended dependencies are still actively maintained.
How AgentRank Works
- The agent identifies a need for a tool or a specific search query from the user.
- Using a web fetch tool, the agent calls the AgentRank API with relevant search parameters.
- The API returns a JSON payload containing the tool name, description, rank, and quality score.
- The agent processes the scores (e.g., 80+ for Highly Rated) to provide a quality verdict.
- The agent presents the results to the user with direct links to the AgentRank detail pages for full transparency.
AgentRank Setup
To integrate this with your Openclaw Skills workflow, ensure your agent has the capability to perform GET requests to external APIs. You can test the connectivity using a standard curl command:
curl "https://agentrank-ai.com/api/search?q=database&type=tool&limit=5"
No specialized API keys are required for basic search functionality, making it easy to deploy across various agent environments.
AgentRank Data Schema & Taxonomy
The skill processes data in a structured format to help agents make informed decisions:
| Property | Description |
|---|---|
| q | The search term used to query the index. |
| type | Filter for 'tool' (GitHub repositories) or 'skill' (registry entries). |
| score | A 0-100 metric derived from real-time GitHub activity signals. |
| rank | The tool's position relative to 25,000+ other indexed items. |
| slug | The unique identifier for the tool, typically the repository owner and name. |
AgentRank Advanced Features
- Nightly updates from real GitHub signals to capture the latest maintenance trends.
- Specific filtering for MCP servers via the tool type parameter.
- High-resolution quality verdicts (Highly Rated, Solid, Use with Caution, Low Confidence).
- Support for broad term discovery and narrow, specific capability matching.
SKILL.md
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