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nestordemeure

ANNO MCP Server

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 serves a distinct purpose: search_anno retrieves matching issues, get_snippets locates query occurrences within an issue, and download_text obtains OCR text. There is no functional overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: search_anno, get_snippets, download_text. The naming is predictable and clear.

    Tool Count5/5

    With 3 tools, the server is well-scoped for its purpose of searching and retrieving text from a large archive. Each tool adds necessary functionality without bloat.

    Completeness4/5

    The toolset covers the essential search-to-text workflow. Minor gaps exist, such as lack of image retrieval or browsing capabilities, but these are beyond the stated scope.

  • Average 4.9/5 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 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior5/5

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

    With no annotations, description fully discloses behavior: one request per page, caching, periodicals raise errors, and return type (path to cached file). 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.

    Conciseness5/5

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

    Well-structured with sections (main, Args, Returns, IMPORTANT, Example), each sentence is informative and no wasted words.

    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 2 parameters, no annotations, and an output schema, the description covers purpose, parameters, behavior, limitations, and provides an example. Fully complete.

    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?

    Despite 0% schema coverage, the description clearly explains both parameters: identifier with an example, and page as optional single page or omit for whole issue. Adds 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?

    The description explicitly states the action ('Download OCR plain text') and resource ('ANNO newspaper issue') and distinguishes from sibling tool get_snippets by noting that periodicals should use get_snippets instead.

    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?

    Provides clear when-to-use guidance: pass page when get_snippets has identified the page, and warns against using for periodicals (ANNOP_). Does not explicitly list when not to use but covers main scenarios.

    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?

    No annotations are provided, so the description carries full burden. It discloses the 10-snippet limit, works for periodicals, and explains the return format. However, it does not mention authentication requirements, rate limits, or whether results are cached, leaving minor gaps.

    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-organized with Args, Returns, Note, and Examples sections. Every sentence adds value, and the purpose is stated upfront. It is appropriately sized for the tool's complexity.

    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 fully defines the return structure. It covers all necessary aspects: purpose, parameters, return values, limitations, and usage context. Distinguishes from sibling tools effectively.

    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%, so the description must compensate. It provides clear definitions for both parameters: identifier as 'Document id from a search result, e.g. ANNO_dmo19330626' and query as 'Terms to locate within the issue', plus concrete examples.

    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 verb+resource: 'Find which pages of one ANNO issue a query appears on, with context.' This clearly distinguishes from siblings like search_anno and download_text.

    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?

    Explicitly frames the tool as a cheap triage step, explains why it's useful (false positives can be rejected without download), and notes it works for periodicals where OCR text cannot be downloaded. This provides clear when-to-use guidance.

    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?

    With no annotations, the description fully carries the behavioral burden. It discloses search characteristics (bare words ANDed, operator case sensitivity, wildcards), pagination limitations, and the nature of result counts (true match counts). It also indicates that 91% of holdings are full-text searchable.

    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 appropriately sized. It opens with a clear one-sentence summary, then provides scope, usage notes, parameter details, return structure, and examples. Every sentence adds value, and the information is 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?

    Despite lacking an output schema, the description details the return structure with all fields (page, total_results, documents, etc.) and provides multiple examples. For a search tool with 3 parameters and no output schema, this is exceptionally complete.

    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%, so the description must compensate. It does so comprehensively: explains query syntax with examples, clarifies page default and fixed size, describes sort options and their implications. This adds meaning far 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 clearly states the tool searches ANNO, a historical newspaper archive, and specifies its scope (28 million pages, 1,600 titles). It distinguishes from siblings by noting that results resolve to an issue, not a page, directing to get_snippets for page-level detail.

    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 provides explicit guidance on when to use this tool vs. alternatives (get_snippets for page-level results). It details query syntax (AND, OR, NOT, wildcards), pagination behavior (fixed at 10, 1-indexed), and safe use of sort options (date ordering for sweeping queries).

    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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