Multi-source intelligence reconnaissance MCP server with support for 18+ search sources including HackerNews, GitHub, npm, PyPI, ArXiv, Reddit, YouTube, and more. Provides both MCP (stdio) and HTTP server modes with x402 micropayment integration.
Scout MCP provides 21 search tools with consistent naming (verb_noun pattern: *_search) and reasonable parameter schemas. However, descriptions are generic and lack LLM-optimization guidance. Most tools accept query + per_page + optional sort/site/category, but descriptions do not explain WHEN to use each tool vs. alternatives, what makes results actionable, or how to chain tools. Output schemas are not documented, LLMs cannot predict what fields to extract. Error handling is absent from visible code. Tool descriptions average ~120 chars, below the 194-char baseline for A+ tools. No parameter descriptions explain format constraints, valid ranges, or dependencies. Security: x_search and other tools call external APIs but no visible rate-limiting, timeout handling, or secret injection patterns in tool definitions.
Search ArXiv for academic papers and preprints. Returns title, authors, abstract summary, PDF link, and categories. Great for cutting-edge research in CS, ML, physics, and mathematics.
Search x402 Bazaar for AI-agent APIs with micropayment access. Queries the CDP Discovery directory of x402-enabled HTTP resources. Returns matching APIs with price, network, and relevance score.
Search Dev.to (Forem) for technical articles and blog posts. Returns title, description, reactions, comments, reading time, and tags. Great for tutorials, how-tos, and developer insights.
Get detailed information about a specific GitHub repository. Returns stars, forks, watchers, language, topics, license, and README preview.
Search GitHub repositories. Returns repository name, description, stars, language, and URL. Supports sorting by stars, forks, or updated date.
Search GitLab for repositories and projects. Returns project name, description, stars, language, and URL.
Output schemas not documented. LLMs cannot predict what fields each tool returns (e.g., does hackernews_search return 'score' or 'points'? 'link' or 'url'?). This forces agents to guess or make extra calls to discover structure.
Descriptions lack LLM-optimization guidance. Generic descriptions like 'Search HackerNews for tech news...' do not explain WHEN to use this tool vs. lobsters_search or reddit_search, what makes results actionable, or what the agent should do with the output. Baseline: 194 chars; most tools ~120 chars.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 55 | 2026-07-28+ | v2 |
Search HackerNews for tech news, startup discussions, and community insights. Returns title, score, comments, and discussion link.
Search Hashnode for technical blog posts and articles. Returns title, description, author, reactions, comments, and publication date.
Search Lemmy for discussions and community posts. Returns post title, score, comments, community, and link. MCP-only tool (community instance, personal use).
Search Lobsters for tech news and discussions. Returns story title, score, comments, and discussion link.
Search npm registry for JavaScript packages. Returns package name, description, version, downloads, and repository link.
Search Product Hunt for new products and launches. Returns product name, tagline, votes, comments, and product page link.
Search PyPI for Python packages. Returns package name, description, version, downloads, and repository link.
Search Qiita for Japanese technical articles. Returns title, description, author, likes, and link. MCP-only tool (unofficial API, personal use only).
Search Reddit for discussions and community posts. Returns post title, score, comments, subreddit, and link. MCP-only tool (public .json feeds).
Generate a comprehensive multi-source intelligence report. Queries up to 18 sources (balanced or comprehensive) and returns aggregated results with source metadata.
Search Semantic Scholar for academic papers and citations. Returns title, authors, abstract, citation count, and PDF link.
Search Stack Exchange (Stack Overflow, ServerFault, etc.) for Q&A. Returns question title, score, answers, tags, and link.
Search X (Twitter) for real-time posts and discussions using xAI Grok API. Returns posts, engagement metrics, and author information.
Search YouTube for videos. Returns video title, description, channel, views, and link. MCP-only tool (YouTube Data API ToS restriction).
Search Zenn for Japanese technical articles and books. Returns title, description, author, likes, and link.
Parameter descriptions missing or trivial. 'Results per page' does not explain why an agent would vary this (context window, latency, cost). 'Search query' does not explain format constraints, boolean operators, or how to phrase queries for each source. No guidance on parameter relationships or dependencies.
No error handling or recovery guidance visible in tool definitions. If a search returns 0 results, times out, or hits a rate limit, the agent has no guidance on what to do next (retry? try a different tool? ask the user?). No actionable error messages.
Tool composition unclear. scout_report aggregates 14-18 sources, but the description does not explain which sources are included in 'balanced' vs 'comprehensive', what the cost difference is, or when to use scout_report vs. calling individual search tools. No guidance on tool chaining.