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tycket033-tech

pdf-knowledge-mcp

by tycket033-tech
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: ingest for adding documents, search for retrieval, and ask for QA with citations. No overlapping functionality.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern in snake_case (ask_pdf_expert, ingest_document, search_knowledge), making their roles clear.

    Tool Count4/5

    Three tools is a minimal but reasonable set for a knowledge base server, covering the core operations of ingestion, retrieval, and question answering.

    Completeness4/5

    The server covers the essential CRUD-like operations for a RAG system (ingest, search, QA). Missing delete or update, but these are less critical for the domain.

  • Average 3.7/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 2 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

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  • 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

  • Behavior2/5

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

    No annotations provided; description only says retrieves knowledge and returns answer with citations. Lacks disclosure on side effects, permissions, or dependencies (e.g., prior ingestion).

    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?

    Single sentence that is clear, front-loaded, and efficient. No waste.

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

    Completeness2/5

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

    No output schema; description lacks details on answer format, citation structure, or prerequisites. The tool is complex (RAG-based) and needs more context for correct selection and use.

    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 description coverage is 100% so baseline 3. Description adds context about retrieval but does not enhance parameter meaning beyond 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?

    Clearly states verb 'Answer', resource 'PDF development question', and method 'retrieve local knowledge and return with citations'. Distinguishes from sibling tools like search_knowledge.

    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?

    Implies usage for PDF development questions but provides no explicit when-to-use or when-not-to-use guidance, nor alternatives. The context from siblings is not referenced.

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

  • Behavior3/5

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

    Description mentions 'semantic-style TF-IDF vector retrieval' and 'ranked chunks' but doesn't detail limitations, required permissions, or behavior for edge cases. No annotations provided to offset this.

    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?

    Single, concise sentence with no extraneous information. Every phrase adds value.

    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?

    With 3 parameters and no output schema, the description covers the core purpose and return format (ranked chunks). Could be improved by noting range of acceptable queries or performance considerations.

    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 good descriptions for each parameter (query, tags, limit). The description doesn't add significant meaning beyond what the schema already provides, so baseline 3 is appropriate.

    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 the tool searches a local PDF knowledge base using semantic-style TF-IDF vector retrieval and returns ranked chunks with source references, distinguishing it from siblings like ask_pdf_expert or ingest_document.

    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?

    Implied usage for searching PDF development knowledge, but no explicit guidance on when to use this tool versus ask_pdf_expert (which likely handles interactive Q&A) or ingest_document (which ingests documents).

    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 clearly discloses the tool's behavior: 'The server chunks, vectorizes, indexes, and persists the content locally.' It accurately describes the write operation, though it could further address idempotency or failure modes.

    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 two sentences, front-loaded with verb and resource, and contains no filler. Every sentence adds value.

    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 complexity (8 parameters, no output schema), the description covers the core process but omits details about supported file types (schema allows non-PDF formats) and post-ingestion querying. It is mostly complete but has a minor inconsistency between description ('PDF') and schema (other formats).

    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 parameter descriptions present. The description does not add per-parameter meaning beyond the schema, which is acceptable. Baseline score of 3 is appropriate.

    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 tool's purpose: 'Ingest a PDF development experience document into the local RAG knowledge base.' It specifies the verb (ingest), resource (PDF document), and destination (RAG knowledge base), and distinguishes from sibling tools like ask_pdf_expert (query) and search_knowledge (search).

    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 for adding documents to the knowledge base but does not explicitly state when to use this tool versus alternatives like ask_pdf_expert or search_knowledge. No when-not or direct comparisons are provided.

    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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