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Search project docs semantically to find relevant files before coding changes.
This MCP tool is described as semantically searching project documentation and returning relevant markdown files, with no stated need for secrets or remote endpoints, so overall risk appears relatively low. However, it is still executable third-party code, and evidence of community adoption and maintenance is weak, so it should be treated with normal caution for a local file-access tool.
The materials explicitly state that no keys or environment variables are required. No API tokens, account credentials, or other sensitive authentication data are requested, so credential exposure and abuse risk appears low.
The materials state there are no remote endpoints, and the description only mentions building a semantic index over project documentation and returning relevant markdown files. No explicit external data destination or third-party transmission is described.
The system checks indicate that this tool executes code; as an MCP tool, that means its implementation runs locally. The current materials provide no README or detailed capability boundaries, so it should be treated with normal caution as a local executable tool, but this alone is not a high-risk signal.
Its stated function requires searching project documentation, building a semantic index, and returning relevant markdown files, so it can reasonably be inferred that it reads workspace documentation content. There is no clear evidence of writing, excessive access, or broad system-level permissions, but it still has local data-read capability and should be limited to the minimum necessary directories.
Positive factors include being open source under the MIT License, making the code theoretically auditable. However, the source is a third-party registry, the repository has 0 stars, maintenance status is unknown, and README detail is absent, so supply-chain trust is only moderate and the code and dependencies should be reviewed before use.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "docs_search" yet — see the docs or source repo.
Search the project documentation for content related to "user authentication API, token refresh, and login flow," and return the most relevant markdown files with a brief reason for each.
A list of the most relevant documentation file paths with reasons they relate to authentication API changes.
Find documentation about "Docker deployment, environment variable configuration, and production release process," prioritizing the newest and most complete markdown files.
A ranked list of deployment-related documents for quick review before release.
Search the project docs for materials related to "permission management, role definitions, and admin dashboard," and suggest a reading order.
Relevant documents plus a recommended reading order to understand the feature context before development.
Search documentation semantically by indexing sites and finding relevant technical information.
Generate, search, and maintain codebase documentation for faster team development.
Search documentation semantically and get AI answers with source citations.
Let AI index and search local and online sources inside your IDE.
Let AI list, read, and search local Markdown documentation on demand.
Lets AI search and query documentation with flexible retrieval configurations.