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menyoung

paperqa-mcp-server

by menyoung
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes with no overlap: index_status is for health/status monitoring of the indexing system, while paper_qa is for querying and synthesizing content across papers. Their descriptions clearly differentiate diagnostic vs. query functionality.

    Naming Consistency5/5

    Both tools follow a consistent snake_case naming pattern with clear verb_noun structure: index_status (verb: check/status, noun: index) and paper_qa (verb: query/answer, noun: paper). The naming is predictable and readable throughout.

    Tool Count2/5

    With only 2 tools, the server feels severely under-scoped for a paper query and synthesis domain. A typical paper/library server would need at least basic CRUD operations for papers, search filtering, or metadata management, but here the surface is minimal and relies heavily on external Zotero tools.

    Completeness2/5

    The toolset is incomplete for the apparent domain of paper querying and synthesis. There are no tools for managing papers (add/remove), browsing the library, filtering searches, or handling indexing beyond status checks. The server delegates core functionality to Zotero tools, creating significant gaps for agent workflows.

  • Average 4.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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.

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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?

    With no annotations provided, the description carries the full burden. It effectively discloses behavioral traits: it's a read-only diagnostic tool (implied by 'Check' and 'Returns'), and it specifies what information is returned (summary of indexed papers, errors, unindexed). However, it doesn't mention potential rate limits or authentication needs.

    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 front-loaded with the core purpose, followed by usage guidance. Both sentences earn their place by providing essential information without redundancy, making it highly efficient and well-structured.

    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 the tool's simplicity (0 parameters, no annotations, but has an output schema), the description is complete. It explains what the tool does, when to use it, and what it returns, which is sufficient since the output schema will handle return value details.

    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?

    The tool has 0 parameters with 100% schema coverage, so the baseline is 4. The description appropriately doesn't add parameter details, as none are needed, and instead focuses on the tool's purpose and 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 tool's purpose with specific verbs ('Check the health', 'Returns a summary') and resources ('paper index'), distinguishing it from the sibling 'paper_qa' tool by focusing on diagnostic status rather than querying content.

    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?

    It explicitly states when to use this tool ('to diagnose why paper_qa queries might be failing or timing out'), providing clear context and distinguishing it from the alternative sibling tool 'paper_qa'.

    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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool returns detailed answers with inline citations and specific file path formats, can return an 'Index incomplete' error with instructions for resolution, and has a long response time (30–90 seconds). It does not mention error handling beyond the index issue or rate limits, but covers essential operational aspects.

    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 well-structured and front-loaded with the core purpose, followed by usage guidelines, output details, error handling, and performance notes. Each sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

    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 the tool's complexity (synthesis across papers, citations, potential errors, long runtime) and the presence of an output schema (which handles return values), the description is complete. It covers purpose, usage, output format, error scenarios, and performance, providing all necessary context for effective tool invocation without needing to repeat schema details.

    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?

    The input schema has 1 parameter with 0% description coverage, so the description must compensate. It implies the 'query' parameter is for complex research questions requiring synthesis, as shown in the examples, adding meaningful context beyond the schema's basic type definition. However, it does not specify format constraints or length limits for the query.

    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 with specific verbs ('search and synthesize') and resource ('across all papers in the library'), distinguishing it from sibling tools like 'index_status'. It provides concrete examples of appropriate queries, making the purpose unambiguous and well-defined.

    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 defines when to use this tool ('for questions that require deep reading and synthesis across multiple scientific papers') and when not to use it ('Not for quick metadata lookups or library browsing — use Zotero tools for that'). It also provides an alternative ('Zotero tools') for excluded use cases, offering comprehensive 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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  • 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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