RagdocsSAFE
An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
Features
- Vector-based documentation search and retrieval
- Support for multiple documentation sources
- Semantic search capabilities
- Automated documentation processing
- Real-time context augmentation for LLMs
Tools
search_documentation
Search through stored documentation using natural language queries. Returns matching excerpts with context, ranked by relevance.
Inputs:
query(string): The text to search for in the documentation. Can be a natural language query, specific terms, or code snippets.limit(number, optional): Maximum number of results to return (1-20, default: 5). Higher limits provide more comprehensive results but may take longer to process.
list_sources
List all documentation sources currently stored in the system. Returns a comprehensive list of all indexed documentation including source URLs, titles, and last update times. Use this to understand what documentation is available for searching or to verify if specific sources have been indexed.
extract_urls
Extract and analyze all URLs from a given web page. This tool crawls the specified webpage, identifies all hyperlinks, and optionally adds them to the processing queue.
Inputs:
url(string): The complete URL of the webpage to analyze (must include protocol, e.g., https://). The page must be publicly accessible.add_to_queue(boolean, optional): If true, automatically add extracted URLs to the processing queue for later indexing. Use with caution on large sites to avoid excessive queuing.
remove_documentation
Remove specific documentation source
4995136c99e6OBSERVED · 2026-10-06Connect
Built from this server's own package name, version and transport as found in its source — not copied from anyone's documentation, so it cannot drift against a page we do not control. Replace the environment placeholders with a token scoped to the least it needs.
claude mcp add mcp-ragdocs --env OPENAI_API_KEY=${OPENAI_API_KEY} --env QDRANT_API_KEY=${QDRANT_API_KEY} -- npx -y @hannesrudolph/[email protected]{
"mcpServers": {
"mcp-ragdocs": {
"command": "npx",
"args": [
"-y",
"@hannesrudolph/[email protected]"
],
"env": {
"OPENAI_API_KEY": "${OPENAI_API_KEY}",
"QDRANT_API_KEY": "${QDRANT_API_KEY}"
}
}
}
}Exposed tools (7)
4 read · 1 write · 2 destructive. Blast radius: 2 tools can delete or overwrite — an agent that can be talked into calling a tool can be talked into calling this one.
| Tool | Risk | Description |
|---|---|---|
clear_queue | destructive | Clear all URLs from the queue |
extract_urls | read | Extract all URLs from a given web page |
list_queue | read | List all URLs currently in the documentation processing queue |
list_sources | read | List all documentation sources currently stored in the system. Returns a comprehensive list of all indexed documentation including source URLs, titles, and last update times. Use this to understand what documentation is available for searching or to verify if specific sources have been indexed. |
remove_documentation | destructive | Remove one or more documentation sources by their URLs |
run_queue | write | Process URLs from the queue one at a time until complete |
search_documentation | read | Search through stored documentation using natural language queries. Use this tool to find relevant information across all stored documentation sources. Returns matching excerpts with context, ranked by relevance. Useful for finding specific information, code examples, or related documentation. |
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | WARN |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- declared (1 observation(s))
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- not all pinned
- Secrets in source
- none-found
Findings (2)
clear_queue, remove_documentation
@types/node, ts-node, typescript
Gates applied: no_behavioural_pass.
4995136c99e6full audit observations/trust-audit/mcp-server/hannesrudolph__ragdocs-1.json · Report an issue / request a re-scanAudit history
Every audit this server has had. A grade with a past is a grade somebody is still checking.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-06 | 4995136c99e6 | SAFE | B | 89 | first audit |
Questions
What is the Ragdocs MCP server?
An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
What tools does Ragdocs expose?
7 in total: 4 read-only, 1 that write, and 2 that can delete or overwrite (clear_queue, remove_documentation). Every one is listed on this page with its risk.
Is Ragdocs safe to connect to an agent?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean server reads B. Separately from the audit: 2 of its tools can destroy data, so scope the token you give it to what you actually need.
What credentials does Ragdocs need?
It reads OPENAI_API_KEY and QDRANT_API_KEY from the environment. Give it a token scoped to the least it needs — an agent that can be talked into calling a tool can be talked into calling it with your credentials.
How does Ragdocs run?
It speaks stdio, so it runs as a local process your client starts. It is published on npm as @hannesrudolph/mcp-ragdocs at 1.1.0.
How current is this page?
The grade is for one exact copy of the source (4995136c99e6), read on 2026-10-06. The repository is watched and re-audited when it changes.