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nestordemeure

lantern-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

    The two tools have clearly distinct purposes: one searches and returns excerpts, the other fetches full OCR text for a specific leaf. There is no ambiguity in their roles, and descriptions reinforce their complementary nature.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun snake_case pattern: search_lantern and get_lantern_text. Even though one adds a modifier, the verb-first structure and shared domain noun keep the naming uniform and predictable.

    Tool Count3/5

    With only two tools, the set feels minimal — at the low end of what's useful. For such a narrow domain (searching and reading a specific archive), two might suffice, but it still borders on overly thin.

    Completeness4/5

    The workflow is complete for the core use case: search to locate relevant pages, then fetch full text when needed. Minor gaps exist, such as no way to browse volumes or get metadata, but these are not essential for typical search-and-read tasks.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 3 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",
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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?

    Discloses per-volume cost and caching behavior: first leaf triggers 8-25 MB download, subsequent leaves free. This goes beyond the basic 'download' description and informs decisions, especially since no annotations are provided.

    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, first states action, second provides guidance. No fluff, efficient.

    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 one-parameter tool with output schema, the description covers purpose, usage criteria, cost behavior, and return value. No missing critical information.

    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?

    The single parameter 'reference' is fully described in schema (100% coverage) including provenance from search_lantern. Description adds no extra semantic beyond schema, but that's unnecessary due to high schema coverage.

    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?

    Clear verb 'Download', specific resource 'one leaf's OCR text', and explicit outcome 'path to cached file'. Distinguishes from sibling search_lantern by focusing on whole-page download vs excerpts.

    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?

    Explicit 'Use only when...' with condition 'search excerpt is not enough and whole page has to be read or grepped'. Also mentions cost trade-off, guiding when to prefer this over search.

    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 provided, the description carries full responsibility for disclosing behavior. It does so thoroughly, revealing the OR-default operator, boolean support, lack of wildcards, and the output format (each result resolves to one scanned leaf with keyword-in-context excerpts in {braces}). The example counts (272,059 vs 27) vividly illustrate the impact of quoting phrases.

    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 appropriately sized given the tool's complexities, with the purpose front-loaded in the first sentence. It is well-structured into distinct paragraphs covering corpus, syntax, and output format, with all-caps emphasis for the critical OR-default warning. Every sentence adds value; the examples and counts are illustrative rather than padding.

    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?

    The description is complete for a search tool with two simple parameters and an output schema. It covers the corpus scope, notable search quirks, and the inline result format, ensuring the agent understands what will be returned. The implicit nod to the sibling tool for full-text retrieval is sufficient given the tool's purpose.

    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 fully documents both parameters ('Search terms' and 'Result page number, 1-indexed'), so the baseline is 3. The description adds significant meaning to the query parameter by explaining how terms are combined (OR by default) and the importance of quoting phrases, which is not captured in the schema. It does not add anything for the page parameter, but that is adequately described.

    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: 'Search the entertainment trade press full text.' It elaborates on the exact corpus indexed (Variety, Billboard, New York Clipper, etc.) and timeframe, distinguishing it from general news searches. The sibling tool get_lantern_text is implicitly differentiated by noting that search results include excerpts 'so no further call is needed to judge a hit.'

    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 provides strong contextual guidance by specifying the corpus is 'the industry's own press' and offering detailed search syntax instructions: 'THE DEFAULT OPERATOR IS OR, NOT AND.' It advises when to quote phrases and explicitly states what is not supported ('wildcards and proximity do not'). However, it does not explicitly name alternatives or state 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.

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  • Evaluate tool definition quality.

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