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Prowl MCP Server

@opcastil11

代码仓库主页

基本信息

分类

其他

许可证

Apache-2.0

作者

opcastil11

关于 Prowl MCP Server

Open-source benchmark runner for the Prowl Agent Discovery Network

Prowl MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "prowl-bench": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv",
        "&&",
        "source",
        ".venv/bin/activate"
      ]
    }
  }
}

Prowl MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Prowl?

Prowl is an open-source benchmark runner that evaluates any API across 8 dimensions of agent-readiness using multi-LLM scoring. It measures how easily AI agents can discover, understand, and use an API, targeting developers who want to optimize their APIs for the agent economy.

How to use Prowl?

Install via pip install prowl-bench, set at least one LLM API key (Anthropic, OpenAI, or Google), then run prowl-bench run <url>. Optionally specify a template, credentials, output format (JSON), or a minimum score for CI. Prowl also provides a Python API for programmatic use.

Key features of Prowl

  • Multi-LLM scoring across Claude, GPT-4o, and Gemini
  • 8 weighted dimensions measuring agent efficiency
  • 4-phase pipeline: Analyze, Plan, Execute, Interpret
  • Auto-detected benchmark templates (API, platform, MCP, docs, DeFi, crypto)
  • CI integration with --min-score flag
  • Community submission to the Prowl network

Use cases of Prowl

  • Benchmark your own API to improve agent-friendliness
  • Evaluate third-party APIs before integrating them into agent workflows
  • Catch agent-efficiency regressions in CI/CD pipelines
  • Compare API documentation quality across providers
  • Score MCP servers or DeFi protocols for agent readiness

常见问题

1

What does Prowl measure that traditional API testing tools don’t?

Traditional tools measure uptime and response time; Prowl measures whether an AI agent can actually use the API across dimensions like token efficiency, first‑try success, error clarity, and response parseability.

2

What dependencies or runtime requirements does Prowl have?

Prowl requires Python 3.10+ and no system dependencies. You need at least one LLM API key (Anthropic, OpenAI, or Google) for scoring; more keys produce more balanced results.

3

What data does Prowl send to third parties during a benchmark?

Prowl sends extracted API spec data and test results to the configured LLM providers for scoring. Execution phase HTTP requests go through a sandbox that blocks SSRF and prompt injection. No data is shared with Prowl unless you explicitly use the `--submit` or `--vendor-submit` flags.

4

How does multi-LLM scoring work?

Each available LLM (Claude, GPT-4o, Gemini) independently scores all 8 dimensions. Scores are then averaged to reduce single-model bias. The JSON output includes per-model breakdowns.

5

What templates are available and how are they chosen?

Six templates are auto-detected from service metadata: `api_benchmark`, `platform_profile`, `mcp_compliance`, `docs_quality`, `defi_yield`, and `crypto_app`. You can also force a template with the `--template` flag.

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