not-human-search-mcp
Search AI-ready websites, inspect indexed site details, verify MCP endpoints, and discover tools and APIs using the Not Human Search MCP server
Best use case
not-human-search-mcp is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Search AI-ready websites, inspect indexed site details, verify MCP endpoints, and discover tools and APIs using the Not Human Search MCP server
Teams using not-human-search-mcp should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/not-human-search-mcp/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How not-human-search-mcp Compares
| Feature / Agent | not-human-search-mcp | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Search AI-ready websites, inspect indexed site details, verify MCP endpoints, and discover tools and APIs using the Not Human Search MCP server
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Not Human Search MCP
## Overview
Not Human Search is a remote MCP server that lets AI agents search a curated index of 1,750+ AI-ready websites, inspect indexed site details, submit new sites for analysis, and verify live MCP endpoints via JSON-RPC probe. It is designed for AI agents that need to discover tools, APIs, and services at runtime without relying on hardcoded lists.
## When to Use This Skill
- Use when an AI agent needs to discover tools, APIs, or MCP servers for a specific task
- Use when you want to check whether a website exposes machine-readable endpoints (llms.txt, OpenAPI, MCP)
- Use when verifying that an MCP endpoint is actually responding to JSON-RPC
- Use when building agent workflows that need to find and connect to external services dynamically
## MCP Configuration
Add the Not Human Search MCP server to your client configuration. The endpoint uses streamable HTTP and requires no authentication.
### Claude Desktop / Cursor / Windsurf
```json
{
"mcpServers": {
"not-human-search": {
"url": "https://nothumansearch.ai/mcp"
}
}
}
```
No API key or authentication is required.
## Available Tools
### `search_agents`
Search the index of 1,750+ AI-ready websites by keyword. Returns ranked results with scores, categories, and available endpoints.
```
search_agents({ query: "code review tools", limit: 10 })
```
### `get_site_details`
Check a specific domain's AI-readiness score and available machine-readable endpoints.
```
get_site_details({ domain: "linear.app" })
```
### `get_stats`
Get aggregate index statistics, including total indexed sites, categories, and endpoint coverage.
```
get_stats({})
```
### `submit_site`
Submit a URL for crawling and AI-readiness analysis.
```
submit_site({ url: "https://example.com" })
```
### `verify_mcp`
Verify whether a URL is a live MCP endpoint by sending a JSON-RPC probe and checking for a valid response.
```
verify_mcp({ url: "https://example.com/mcp" })
```
### `list_categories`
List available discovery categories for narrowing searches.
```
list_categories({})
```
### `get_top_sites`
Retrieve top-ranked indexed sites.
```
get_top_sites({ limit: 10 })
```
### `register_monitor`
Register a domain monitor using a user-provided email address.
```
register_monitor({ domain: "example.com", email: "user@example.com" })
```
## Examples
### Example 1: Discover Code Review Tools
```text
Use @not-human-search-mcp to find code review tools that expose MCP or API endpoints.
```
The agent will call `search_agents({ query: "code review", limit: 10 })` and return ranked results with scores and endpoint details.
### Example 2: Check if a Site is AI-Ready
```text
Use @not-human-search-mcp to check the AI-readiness of linear.app.
```
The agent will call `get_site_details({ domain: "linear.app" })` and return the site's score breakdown.
### Example 3: Verify an MCP Endpoint
```text
Use @not-human-search-mcp to verify that https://heliumtrades.com/mcp is a working MCP server.
```
The agent will call `verify_mcp({ url: "https://heliumtrades.com/mcp" })` and confirm whether it responds to JSON-RPC.
## Best Practices
- Use `search_agents` for broad discovery, then `get_site_details` for detailed analysis of specific indexed results
- Use `verify_mcp` to confirm an MCP endpoint is live before wiring it into an agent workflow
- Use `submit_site` when a relevant site is absent from the index and the user wants it analyzed
- Use `register_monitor` only with an email address the user explicitly provides for monitoring
- Combine with other MCP skills to build dynamic tool-discovery pipelines
## Limitations
- The search index covers 1,750+ sites and is updated regularly, but may not include every site on the internet.
- Scoring reflects machine-readable signals (llms.txt, OpenAPI, MCP, structured data) rather than content quality.
- `verify_mcp` sends a JSON-RPC probe to the target URL; only use it on URLs you expect to be MCP endpoints.
- `register_monitor` requires a user-provided email address and consent to receive monitoring notifications.
## Related Skills
- `@mcp-builder` - For building your own MCP servers
- `@ai-dev-jobs-mcp` - Search AI/ML job listings via MCPRelated Skills
xvary-stock-research
Thesis-driven equity analysis from public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools (Claude Code, Cursor, Codex).
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
seo-aeo-keyword-research
Researches and prioritises SEO keywords with AEO question queries, difficulty tiers, cannibalization checks, and a content map. Activate when the user wants to find keywords, research search terms, or build a keyword strategy.
search-specialist
Expert web researcher using advanced search techniques and
hybrid-search-implementation
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
humanize-chinese
Detect and rewrite AI-like Chinese text with a practical workflow for scoring, humanization, academic AIGC reduction, and style conversion. Use when the user asks to 去AI味, 降AIGC, 去除AI痕迹, 论文降重, 知网检测, 维普检测, humanize chinese, detect AI text, or make Chinese text sound more natural.
hig-components-search
Apple HIG guidance for navigation-related components including search fields, page controls, and path controls.
exa-search
Semantic search, similar content discovery, and structured research using Exa API. Use when you need semantic/embeddings-based search, finding similar content, or searching by category (company, people, research papers, etc.).
deep-research
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
context7-auto-research
Automatically fetch latest library/framework documentation for Claude Code via Context7 API. Use when you need up-to-date documentation for libraries and frameworks or asking about React, Next.js, Prisma, or any other popular library.
azure-search-documents-ts
Build search applications with vector, hybrid, and semantic search capabilities.
azure-search-documents-py
Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.