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shirokoweb

obsidian-rag-mcp

by shirokoweb

search_notes

Retrieves relevant passages from a local Obsidian vault to answer questions using natural-language queries.

Instructions

Search the user's markdown notes for passages relevant to the query. Use this for any question about the user's notes, then answer using only the returned passages and cite their source filenames.

Args: query: A natural-language question or topic to search for. top_k: How many passages to return (default 4, max 20).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations exist, so the description carries the transparency burden. It clearly implies a read-only search operation and discloses that results are passages with source filenames. However, it does not mention edge-case behavior (e.g., no results or duplicated passages), which is a minor omission for a search tool.

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 concise, front-loaded with the primary purpose, and uses a clear 'Args' block for parameter details. Every sentence earns its place with no redundancy.

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

Completeness5/5

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

Given a simple 2-parameter search tool and the presence of an output schema, the description covers purpose, usage, and parameters comprehensively. It even includes post-condition instructions (cite sources), making it complete for typical use.

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

Parameters5/5

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

Schema descriptions are absent (0% coverage), but the description fully compensates by explaining 'query' as a natural-language question and 'top_k' as a count of passages with default and max values. This adds necessary semantic meaning beyond the raw schema.

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

Purpose5/5

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

The description states a specific action ('Search the user's markdown notes for passages relevant to the query') and reinforces its role as the tool for any note-related question. The verb and resource are unambiguous, making the tool's purpose immediately clear.

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

Usage Guidelines5/5

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

The description explicitly instructs when to use this tool ('Use this for any question about the user's notes') and how to handle the output ('answer using only the returned passages and cite their source filenames'). This provides concrete usage context, even without listing alternative tools.

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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