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seanwinslow28

Vault Knowledge MCP Server

by seanwinslow28
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct operation: semantic search, contradiction graph traversal, and article retrieval by slug. No functional overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: search_concepts, find_contradictions, get_article. Clear and predictable.

    Tool Count5/5

    Three tools is appropriate for a focused read-only knowledge vault. Each tool addresses a core need without redundancy.

    Completeness4/5

    Covers search, contradiction detection, and article retrieval. Lacks a listing or browsing capability, but semantic search mitigates this gap.

  • Average 4.1/5 across 3 of 3 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 status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior3/5

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

    No annotations provided; description supplies 'Read-only' trait and output structure. Lacks details on rate limits, auth, or side effects beyond that.

    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?

    Two concise sentences with no wasted words. Front-loaded with primary action, followed by return format details.

    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?

    Given the tool's simplicity (2 params, no output schema), the description covers input type, output fields, and read-only nature. Lacks mention of pagination or semantic details, but adequate for a basic search tool.

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

    Parameters3/5

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

    Schema coverage is 100% with descriptions for both parameters. The description reinforces 'natural-language query' but adds no new meaning beyond 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?

    Description clearly states verb 'search' and resource 'concept, connection, and Q&A articles' with a specific scope. It distinguishes from siblings: find_contradictions and get_article serve different purposes.

    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?

    Context of semantic search is clear, but no explicit guidance on when to use this tool versus siblings. Usage is implied rather than stated directly.

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

  • Behavior4/5

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

    With no annotations, the description carries full burden. It discloses the read-only nature, the restriction to the allowlist, and the returned components. It does not contradict any annotations (none provided). Could mention errors or pagination, but adequate.

    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 three sentences, each providing distinct value: action, restriction, and classification. No redundant or unnecessary information.

    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?

    For a simple fetch tool, the description covers purpose, return data, constraints, and read-only nature. No output schema is needed as the description summarizes output. All necessary context is provided.

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

    Parameters3/5

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

    Schema coverage is 100% with a clear description of the 'slug' parameter. The description does not add additional semantics beyond stating 'article by slug', which aligns with 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 clearly states 'Fetch one article by slug' and specifies what is returned (frontmatter, body, resolved wikilinks). It distinguishes from sibling tools like search_concepts and find_contradictions by focusing on fetching a single article.

    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 context (fetching by slug, read-only, restricted to allowlist) but does not explicitly provide when to use or alternatives. Sibling tool names suggest different purposes, but no direct guidance.

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

  • Behavior4/5

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

    With no annotations, the description carries full burden. It explicitly declares 'Read-only,' which is a key behavioral trait, and specifies the return fields. However, it omits details like authentication requirements, rate limits, or behavior when no contradictions exist, though these are less critical for a read-only query.

    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?

    Two sentences with no wasted words. The first sentence clearly states the main action, and the second concisely adds parameter details and output format. Well-structured and front-loaded.

    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?

    For a simple query tool with one parameter and no output schema, the description covers all essential aspects: action, scope options, return fields, and behavioral constraint (read-only). It is complete enough for an agent to select and invoke correctly.

    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 100% (1 parameter with enum and description). The description adds value beyond the schema by specifying the return format ({from_slug, to_slug, confidence, surfaced_at, source_run_id}) and reinforcing the read-only nature, which helps the agent understand the output.

    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 clearly states the action ('Traverse the typed reasoning graph') and the resource ('active contradicts edges'). It also distinguishes itself by noting it is 'the sharpest of six relation types,' and sibling tools like search_concepts and get_article have different purposes.

    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 explains the scope parameter options and the return format, implying when to use the tool (e.g., for accessing all or recent contradictions). However, it does not explicitly state when not to use it or how it differs from siblings in terms of use cases, leaving some ambiguity.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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