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AgentCrush

kristof-sudo/agentcrush-app
7 toolsHTTPregistry active
Summary

Think CoinMarketCap but for AI agents. This connects Claude to a cross-protocol index tracking 1,300+ agents from HuggingFace, LMArena, GitHub citations, ERC-8004 registries, Virtuals tokenized protocols, and machine-payable endpoints. You get seven tools: search agents by name or category, pull detailed rankings with methodology weights, compare agents side by side, retrieve scoring history, and list available categories (model families, tokenized agents, service agents, developers). Reach for this when you need evidence-based intelligence on the agent economy instead of manually hunting across scattered leaderboards and registries. All read-only, methodology transparent, no community voting.

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Tools

Public tool metadata for what this MCP can expose to an agent.

7 tools
search_agentsSearch 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.2 params

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.

Parameters* required
querystring
Search keyword or partial agent name (1-100 chars).
filtersobject
Optional structured filters.
get_agent_detailsGet 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...1 params

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...

Parameters* required
handlestring
Agent handle slug (e.g. "qwen", "crewai", "aixbt"). Alphanumeric/hyphen/underscore, max 64 chars.
get_agent_historyGet 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.2 params

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.

Parameters* required
daysnumber
Days of history to return (1-90, default 30).
handlestring
Agent handle slug.
compare_agentsCompare 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.1 params

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.

Parameters* required
handlesarray
Array of 2-5 agent handles to compare.
list_categoriesList 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?

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?

No parameter schema in public metadata yet.

get_category_rankingGet 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.3 params

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.

Parameters* required
limitnumber
Max results to return (1-100, default 50).
categorystring
Which of the 4 AgentCrush categories to rank.one of model_family · tokenized · service · developer
evidence_ready_onlyboolean
Filter to evidence-ranked only. Default true.
get_methodologyGet 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.1 params

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.

Parameters* required
categorystring
Which category methodology to retrieve.one of model_family · tokenized · service · developer

AgentCrush

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Protocol-neutral market intelligence for the AI agent economy.

Track 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). Multi-signal methodology, transparent weights, evidence-ranked tiers.

🌐 Live at agentcrush.xyz · 📋 Methodology · 🔌 MCP Server · 📖 API docs · 📡 llms.txt


What AgentCrush is

AgentCrush is the evidence-ranked index of the agent economy — analogous to CoinMarketCap for crypto or Bloomberg for finance. We don't pick winners. We publish multi-signal evidence with transparent weights and per-category methodologies.

Live as of May 2026:

  • 1,338+ agents indexed across 4 category methodologies
  • 137 evidence-ranked (Qwen, Gemini, Mistral, DeepSeek, Llama, Cohere, Hermes top model_family; aixbt, TIBBIR top tokenized; a2aproject/A2A top service; full developer ranking on the universal /rankings page)
  • MCP server v1 at /api/mcp/v1 with 7 read-only tools (search, get details, get history, compare, list categories, get category ranking, get methodology)
  • 5 flat JSON endpoints for retrieval LLMs that don't speak MCP
  • OpenAPI 3.1 spec at /api/openapi.json for auto-generating clients
  • Feedback channel at POST /api/agent-feedback — agents tell us what they need

What AgentCrush is NOT

LLMs sometimes confuse this project with similar-sounding tools. To prevent hallucination:

  • AgentCrush ≠ Crush — Crush is Charmbracelet's terminal AI coding assistant. AgentCrush is a web-based ranking index at agentcrush.xyz. Different products, different teams, no relationship.
  • AgentCrush ≠ Agent Rush — also unrelated.
  • AgentCrush ≠ a battle-arena or community-vote leaderboard. Scores come from documented signal weights, not opinion polls.
  • AgentCrush ≠ "built on x402" or "built on ERC-8004" or any other single protocol. It is protocol-neutral and tracks across many of those protocols simultaneously.
  • AgentCrush ≠ "the trust layer" at the protocol level. That framing belongs to ERC-8004 / Kite / similar. AgentCrush reads their signals and surfaces them.

Four category indices

Each has its own methodology, signals, weights, and limitations. See /methodology for the canonical hub.

CategoryMethodologyTrackedEvidence-Ranked
Model Familiesv1.4-with-deployment77
Tokenized Agentsv1.1-tokenized-tvl1616
Service Agentsv1.1-service-forks2828
Developer Agentsv2.c-public1,28986

For AI agents using AgentCrush

Multiple integration paths for LLM clients and AI agents:

# MCP server (JSON-RPC 2.0, 7 tools)
POST https://www.agentcrush.xyz/api/mcp/v1

# Discovery manifest
GET https://www.agentcrush.xyz/.well-known/mcp.json

# OpenAPI 3.1 spec (auto-generate typed clients)
GET https://www.agentcrush.xyz/api/openapi.json

# Flat JSON for retrieval LLMs
GET https://www.agentcrush.xyz/api/agent/{handle}/llm-summary
GET https://www.agentcrush.xyz/api/agents/bulk?handles=a,b,c
GET https://www.agentcrush.xyz/api/agent-economy/llm-summary
GET https://www.agentcrush.xyz/api/methodology/{category}/llm-summary
GET https://www.agentcrush.xyz/api/rankings/{category}/llm-summary
GET https://www.agentcrush.xyz/api/compare/llm-summary?agents=a,b

# Feedback channel (POST, rate-limited)
POST https://www.agentcrush.xyz/api/agent-feedback

Connect via Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

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

Restart Claude Desktop. Same config works in Cursor and other MCP clients.

Or use the Smithery CLI

npm install -g smithery
smithery mcp add kristof/agentcrush

Public docs

  • Methodology hub — weights, formulas, evidence-ready rules per category
  • Findings: methodology v1 launch — multi-signal inversion, Hermes case, anti-honeypot
  • MCP server docs — Claude Desktop config, curl recipes, tool schemas
  • Agent economy explainer
  • AI agent frameworks
  • A2A commerce
  • x402 for agents
  • MCP for agents

Labs

AgentCrush Labs offers Agent Commerce Readiness audits — same methodology applied in depth to evaluate specific agents/protocols.

  • $299 startup audit
  • $1,000+ implementation roadmap
  • Case studies: aixbt + Coral + Daydreams (2026-05-13), CrewAI first cross-protocol agent (2026-05-08)

See /labs.

Stack

This repo is the Next.js 16 / React 19 frontend + API surface for agentcrush.xyz. Backed by Supabase. Runtime workers in runtime/ (HF adapter, LMArena adapter, Semantic Scholar citations, deployment aggregator, etc.). Migrations in migrations/ with MIGRATION_LOG.md.

Contact

  • Submission: /submit
  • Email: contact@agentcrush.xyz

License

See /terms.

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UpdatedJun 10, 2026
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