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MCP-Mirror

Library Docs MCP Server

by MCP-Mirror

get_docs

Search documentation for Langchain, Llama-Index, MCP, and OpenAI libraries to find technical information and code examples.

Instructions

Search the docs for a given query and library.
Supports langchain, llama-index, mcp, and openai.

Args:
    query: The query to search for (e.g. "Chroma DB")
    library: The library to search in (e.g. "langchain")

Returns:
    Text from the docs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
libraryYes
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions the action ('search') and returns 'Text from the docs,' but fails to disclose key behavioral traits such as search scope (e.g., full-text, title-only), result limits, error handling, or authentication needs. This leaves significant gaps for an agent to understand the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a concise list of supported libraries and clear sections for Args and Returns. Every sentence adds value without redundancy, making it efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no annotations, no output schema), the description is minimally adequate. It covers the purpose and parameters but lacks details on behavioral aspects like search behavior or output format. Without annotations or output schema, more context on how results are returned would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'query' is for searching (e.g., 'Chroma DB') and 'library' specifies the target (e.g., 'langchain'), including a list of supported libraries. This compensates well for the schema's lack of descriptions, though it could provide more detail on parameter constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search the docs for a given query and library.' It specifies the verb ('search') and resource ('docs'), and mentions the supported libraries. However, without sibling tools, it cannot differentiate from alternatives, so it doesn't reach a perfect score of 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by listing supported libraries ('langchain, llama-index, mcp, and openai'), which suggests when to use it based on library compatibility. However, it lacks explicit guidance on when not to use it or alternatives, and there are no sibling tools to compare against, so the guidance is limited to implied context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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