AgentCrush

by kristof-sudo

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GitHub

About

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.

Details

Author: kristof-sudo
Downloads: 297
Categories: Developer Tools, AI

- 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

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name AgentCrush

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

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

JSON

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

JSON

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

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

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

JSON

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

JSON

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

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

From the repository

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).

`search_agents`

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_agent_details`

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_agent_history`

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_agents`

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_categories`

List the 5 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_category_ranking`

Get the full ranking for one of the 5 categories. Returns agents ordered by composite score with all sub-scores visible. Defaults to evidence-ranked only.

`get_methodology`

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.

`get_agent_trust`

Single-call composite trust score (0-100) + classification (verified / provisional / unverified / low_trust) for delegation decisions. Combines confidence_tier, evidence tier, ERC-8004 verified identity, and risk flags. Mirror of GET /api/agent/{handle}/trust.

`verify_counterparty`

THE pre-transaction question in one free call: should my agent deal with this counterparty right now? Returns proceed / caution / reject with reasoning. Liveness-aware: an agent with no public activity signal in 30+ days never gets a clean proceed, even if well-ranked. Use before paying, delegating to, or integrating any agent. Deeper analysis (full risk decomposition, history, signed attestation) is x402/Pro priced — pointers included in the response.

`get_top_movers`

Returns the top weekly rank movers (up + down) computed from agents.weekly_delta. Useful for surfacing notable changes since last week. Default limit 10 per direction.

`get_protocol_adoption`

How many indexed agents touch each major protocol/surface (ERC-8004 verified, Virtuals tokens, Agentverse, x402/Bazaar, HuggingFace, GitHub). Useful for ecosystem-state questions.

`get_agent_changes`

Pairwise delta scan over an agent's recent snapshots. Reports material changes in score, rank, github_stars, follower_count, identity_type, etc. Mirror of GET /api/agent/{handle}/changes.

`get_ecosystem_summary`

One-call ecosystem-level summary: counts (total, evidence-ranked, archived), category mix (model_family/tokenized/service/developer/mcp_server), category leaders, snapshot volume last 30 days. Mirror of GET /api/trends/summary.

`find_agents`

Counterparty discovery: "which agents can do X and are safe to pay?" Returns the top 3 ranked candidates with liveness (30-day Ghost Index rule), trust tier, verified payment rails (x402/MCP/ERC-8004), scores, and endpoints, plus the total match count. The full ranked list (up to 50) is at https://agentcrush.xyz/api/agents/find/full — $0.05 via x402 on Base, or free with an AgentCrush Pro key.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

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

McpServers

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

AgentCrush

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.

| Category | Methodology | Tracked | Evidence-Ranked |
|---|---|---|---|
| Model Families | v1.4-with-deployment | 7 | 7 |
| Tokenized Agents | v1.1-tokenized-tvl | 16 | 16 |
| Service Agents | v1.1-service-forks | 28 | 28 |
| Developer Agents | v2.c-public | 1,289 | 86 |

For AI agents using AgentCrush

Multiple integration paths for LLM clients and AI agents:

```bash

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About

Details

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Setting up with Highlight

From the repository

search_agents

get_agent_details

get_agent_history

compare_agents

list_categories

get_category_ranking

get_methodology

get_agent_trust

verify_counterparty

get_top_movers

get_protocol_adoption

get_agent_changes

get_ecosystem_summary

find_agents

Claude Desktop / Cursor

McpServers

AgentCrush

What AgentCrush is

What AgentCrush is NOT

Four category indices

For AI agents using AgentCrush

Install

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Sentry

Github

Git

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Context 7

1Panel

`search_agents`

`get_agent_details`

`get_agent_history`

`compare_agents`

`list_categories`

`get_category_ranking`

`get_methodology`

`get_agent_trust`

`verify_counterparty`

`get_top_movers`

`get_protocol_adoption`

`get_agent_changes`

`get_ecosystem_summary`

`find_agents`