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LIPiston

obsidian-rag-mcp

by LIPiston

obsidian_rag

Retrieve the most relevant Obsidian notes for a question and return them as context to analyze user messages.

Instructions

Retrieve the most relevant Obsidian notes for question and return them as context.

Use this to analyze a user's message against what is stored in the vault: search first, then reason over the returned context.

Args: question: the user's question / content to analyze. top_k: number of context chunks to retrieve (1-20).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_kNo
questionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A3.6/5.0
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 of behavioral disclosure. It explains that the tool returns context chunks, but it does not state whether the tool is read-only, whether an index must already exist, what happens when no relevant notes are found, or whether external services are invoked. This is a significant gap for a tool with no annotation support.

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 compact, well-organized, and front-loaded. The purpose sentence is immediately clear, the usage guidance is brief, and the Args section is direct. No sentence is wasted.

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?

An output schema exists, so return-value documentation is less critical. The two parameters are well covered. However, the description omits behavioral context such as indexing prerequisites and does not clarify how this tool relates to sibling tools like `obsidian_search` or `obsidian_index_status`, leaving some ambiguity.

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?

Schema description coverage is 0%, so the description must compensate. It does: `question` is explained as 'the user's question / content to analyze,' and `top_k` is clarified as 'number of context chunks to retrieve (1-20).' This adds real meaning beyond the schema's type and default fields.

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 states a specific action and resource: 'Retrieve the most relevant Obsidian notes for `question` and return them as context.' This clearly identifies what the tool does. It does not explicitly distinguish itself from the sibling `obsidian_search` tool, so it falls just short of a 5.

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

Usage Guidelines4/5

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

The description provides a concrete intended use: 'Use this to analyze a user's message against what is stored in the vault: search first, then reason over the returned context.' This tells an agent when to reach for this tool, though it does not mention when not to use it or name alternatives explicitly.

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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