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shirokoweb

obsidian-rag-mcp

by shirokoweb
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Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.1

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The single tool's purpose is clear and distinct.

    Naming Consistency5/5

    The tool name 'search_notes' follows a clear verb_noun pattern. As the only tool, it is internally consistent.

    Tool Count3/5

    A single tool is borderline. It is not trivial and serves the core purpose of searching notes, but the tool surface feels thin for a server intended to support RAG over an entire note collection.

    Completeness4/5

    The search tool covers the primary retrieval need. Minor gaps exist, such as no ability to list all notes or fetch a specific note directly, but these can be worked around by searching with broad queries.

  • Average 4.8/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 4 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • 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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  • Evaluate tool definition quality.

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