AgentCrush

Name: AgentCrush
Author: kristof-sudo

@kristof-sudo

关于 AgentCrush

Protocol-neutral market intelligence for the AI agent economy. Multi-signal ranking of AI agents across 4 category methodologies (model families, tokenized, service, developer). 7 read-only MCP tools. Free, no auth, 60 req/min.

#agents #ai #intelligence #ranking #agent-economy #mcp #x402 #erc8004

基本信息

分类

开发工具

传输方式

stdio

发布者

kristof-sudo

提交者

kris-sudo

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "agentcrush": {
      "url": "https://www.agentcrush.xyz/api/mcp/v1"
    }
  }
}

工具

Search AI agents by name or keyword across AgentCrush's evidence-ranked index. Returns matching agents with category, tier, and rank info. Use the `filters` object for structured constraints; future versions will add filter keys without breaking the API.

Get full details for a specific AI agent including all category scores it qualifies for (model_family, tokenized, service, developer). Returns identity, raw signals, sub-scores, evidence-ready status. Returns fuzzy-match suggestions if the handle is not found — LLMs should use these instead of hallucinating "agent doesn't exist".

Get rank and score history for an AI agent over the past 1–90 days. Daily snapshots, deduplicated per calendar day. Returns trend summary (rising/falling/flat). Useful for showing how an agent's standing has evolved.

Compare 2-5 AI agents side-by-side across all their categories. Returns full per-agent scoring data + comparison context. Use for "X vs Y" queries. AgentCrush does not declare a universal winner — comparison shows evidence differences.

List the 4 AgentCrush agent categories with tracked + evidence-ranked counts and current methodology versions. Use this for market-level discovery — what kinds of agents does AgentCrush track and how many of each?

Get the full ranking for one of the 4 categories. Returns agents ordered by composite score with all sub-scores visible. Defaults to evidence-ranked only.

Get the scoring methodology for one category — weights, signal sources, formulas, evidence-ready rule, and known limitations. **Methodology travels with data**: call this when explaining HOW a ranking works so the LLM can give a methodology-accurate answer instead of guessing.

概览

What is AgentCrush?

AgentCrush is a protocol-neutral market intelligence index for the AI agent economy. It tracks AI agents across HuggingFace, LMArena, GitHub, paper citations, on-chain registries (ERC-8004), tokenized agent protocols (Virtuals), service registries (Agentverse / A2A), and machine-payable endpoints (x402 / CDP Bazaar) using a multi-signal methodology with transparent weights and evidence-ranked tiers. It is live at agentcrush.xyz and offers 7 read-only MCP tools, flat JSON endpoints, and an OpenAPI 3.1 spec.

How to use AgentCrush?

Connect any MCP client (Claude Desktop, Cursor) to the MCP server URL https://www.agentcrush.xyz/api/mcp/v1 or use the Smithery CLI (smithery mcp add kristof/agentcrush). For retrieval LLMs that don't speak MCP, use the provided flat JSON endpoints (e.g., GET /api/agent/{handle}/llm-summary).

Key features of AgentCrush

Evidence-ranked multi-signal index for AI agents
Tracks agents across 6+ distinct data ecosystems
1,338+ total agents indexed with transparent weights
7 read-only MCP tools for programmatic access
Protocol-neutral, no single-protocol dependence
Commercial readiness audits via AgentCrush Labs

Use cases of AgentCrush

Market intelligence and competitive analysis of the AI agent economy
Ranking and comparing AI agents across model, tokenized, service, and developer categories
Agent Commerce Readiness audits for startups (starting at $299)
Research on agent ecosystem trends and cross-protocol activity