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LIPiston

obsidian-rag-mcp

by LIPiston

obsidian_search

Find relevant note chunks in your Obsidian vault using a natural language query; adjust top_k to control the result count.

Instructions

Semantically search the vault for chunks related to query.

Args: query: what to look for (natural language). top_k: number of results to return (1-20).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It conveys a read-oriented semantic search returning chunks, which is helpful, but it does not mention operational prerequisites such as whether a vault index must already exist or what happens if semantic search is attempted before indexing.

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 short, front-loaded with the main action, and uses a tidy Args list. Every sentence and field earns its place with no redundant filler.

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

Completeness4/5

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

For a two-parameter search tool with an output schema, the description covers the essential invocation contract and result granularity. The main missing context is index dependency and explicit sibling routing, but the core calling information is adequate.

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 coverage is 0%, and the description compensates by explaining 'query' as natural language and top_k as the result count with a 1-20 range. This adds meaningful meaning beyond the bare type and default values in the 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 names the verb 'semantically search', the resource 'vault', and the output granularity 'chunks', making the operation specific and easy to distinguish from sibling tools like obsidian_list_notes or obsidian_read_note. This is a clear, well-scoped purpose statement.

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 semantic-search context is clear, so an agent can infer when this tool is appropriate. However, the description does not explicitly state when to prefer it over siblings such as obsidian_rag or obsidian_list_notes, nor does it provide when-not-to-use guidance.

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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