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nestordemeure

gallica-mcp

by nestordemeure
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: search_gallica finds documents, get_snippets extracts text passages from a document, and download_text retrieves the full OCR text. There is no functional overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (search_gallica, get_snippets, download_text), making the API predictable and easy to understand.

    Tool Count4/5

    With 3 tools, the set is minimal yet sufficient for core document search and text retrieval workflows. It could be expanded with metadata or navigation tools, but the current count is reasonable for this scope.

    Completeness4/5

    The tools cover the essential operations of searching Gallica, locating content within documents, and downloading full text. Minor gaps exist (e.g., no standalone document metadata retrieval), but the core use cases are well-supported.

  • Average 4.6/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 13 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 Apache 2.0.

  • 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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  ]
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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 are provided, so the description carries the full burden. It discloses the underlying API (ContentSearch), specifies output structure with snippets and page numbers, and does not suggest any destructive behavior. No contradictions.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Description is well-structured with a clear purpose sentence, usage context, parameter docs, return structure, and examples. Slightly verbose for a simple tool, but each sentence adds value. Front-loaded with purpose.

    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 no output schema, the description thoroughly explains the return dictionary. It covers purpose, usage context, parameter details, return format, and examples. No gaps for a tool of this complexity.

    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 coverage is 0%, but the description adds extensive meaning for both parameters: identifier is explained with an example ARK, query is described with full syntax and examples including exact phrases and Boolean operators. This far exceeds the schema's minimal definition.

    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 explicitly states the tool fetches text snippets for search terms in a Gallica document, using a specific verb and resource. It distinguishes from siblings by mentioning it is useful after searching (complementing search_gallica) and its output includes page numbers, unlike download_text which likely returns full text.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Description provides context that the tool is for locating specific content after searching, and details query syntax matching search_gallica. However, it does not explicitly state when to use alternatives like download_text or when not to use the tool.

    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 full burden. It discloses that the tool searches OCR content, supports boolean operators and exact phrases, returns paginated results, and uses exact matching by default. It also details the return format and query syntax. It does not mention rate limits, authentication, or performance considerations, but covers core behavior well.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with clear sections (Args, Returns, Examples). It is longer than average but justified by the complexity of the query syntax and the need for comprehensive documentation. Every sentence adds value without 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 the tool's complexity (CQL syntax, pagination, multiple output fields) and no output schema, the description is remarkably complete. It explains the return dictionary structure with all fields, provides multiple usage examples, and mentions sibling tools for related functionality.

    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 has only 2 parameters with 0% coverage. The description provides extensive semantics for the query parameter, including CQL query syntax, operators, and examples. The page parameter is briefly explained with default and indexing. This adds substantial meaning beyond the minimal 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 'Search Gallica for documents matching a text query,' specifying the verb (search), resource (Gallica documents), and scope (matching a text query). It distinguishes from siblings by referencing get_snippets for snippet extraction and implying download_text for downloading.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly directs when to use get_snippets for text snippets, providing an alternative. However, it lacks an explicit 'when not to use' statement or mentions of prerequisites, though the context is clear enough for adequate decision-making.

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

  • Behavior5/5

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

    No annotations are provided, so the description carries full burden. It discloses that files are very large (100KB-1MB+), warns about token usage and performance, and explains the tool saves to cache and returns a file path. This is comprehensive behavioral disclosure.

    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 with sections (Args, Returns, Important, Example), is concise, and each sentence adds value. There is no wasted text.

    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 tool with one parameter and output schema present (context indicates true), the description covers purpose, usage, behavioral warnings, and an example. It is complete and self-contained.

    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?

    With 0% schema description coverage, the description adds significant meaning by explaining the identifier parameter as a Gallica ARK identifier with an example. This compensates for the lack of schema detail.

    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 downloads OCR text from a Gallica document and saves to cache in plain text format. It distinguishes from siblings like get_snippets and search_gallica by its specific action of downloading full text.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description includes a strong warning about large files, advising against reading the entire file and recommending use of read tools with offset/limit. This provides clear usage guidance, though it doesn't explicitly state when not to use the tool or suggest alternatives.

    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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