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akkylab

litra-paper-search-mcp

by akkylab
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.1

  • Disambiguation5/5

    Each tool targets a clearly distinct function: credits, author search, and paper search. No overlap in purpose.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (e.g., search_author, search_papers) and use snake_case uniformly.

    Tool Count4/5

    With 3 tools, the server is minimal but focused. The count is slightly low but still reasonable for a narrow-purpose paper search MCP.

    Completeness3/5

    The tool set covers general search and author search, but lacks tools for retrieving specific paper details or searching by other criteria (e.g., title, year). The presence of check_credits is somewhat tangential.

  • Average 3.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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, so description bears full burden. Only states it searches and returns profile and publications. Does not disclose limit parameter, pagination, rate limits, authentication needs, or what constitutes an 'author profile'.

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

    Conciseness3/5

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

    Two sentences, minimal waste, but lacks mention of the limit parameter and other structural details. Front-loads purpose but is incomplete.

    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?

    Given 2 parameters, no output schema, and no annotations, the description is too brief. It does not explain the output format, pagination, error handling, or prerequisites. Incomplete for an effective search tool.

    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 is 3. Description adds no additional meaning beyond the schema's parameter descriptions (name and limit).

    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 'Search' and resource 'academic papers by author name', with explicit output 'Returns author profile and publications'. Differentiates from sibling 'search_papers' which likely searches by keyword/topic.

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

    Usage Guidelines2/5

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

    No guidance on when to use this tool versus alternatives. Does not mention when not to use or compare to siblings like 'check_credits' or 'search_papers'.

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

  • Behavior2/5

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

    With no annotations, the description carries the full burden of disclosing behavioral traits. It mentions AI-powered scoring and summaries but omits details on authentication, rate limits, or response size. The schema notes experimental year filtering, but that is part of the parameter descriptions, not the main description.

    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 concise, consisting of two sentences that front-load the core purpose. Every sentence adds value with no redundant information.

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

    Completeness3/5

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

    The description covers the essential purpose but lacks information on return format, expected output details, and behavioral constraints. Given the tool has 4 parameters and no output schema, additional context would improve completeness.

    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 input schema covers 100% of parameters with descriptions, so the description adds no extra semantics. Baseline score of 3 is appropriate; the description does not enhance understanding beyond what the schema provides.

    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: searching academic papers with AI-powered relevance scoring and summarization. It specifies the verb 'search' and the resource 'academic papers,' which distinguishes it from siblings like 'check_credits' and 'search_author.'

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

    Usage Guidelines2/5

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

    The description does not provide guidance on when to use this tool versus alternatives or mention conditions for use. No explicit when-to-use or when-not-to-use instructions are given, leaving the AI agent without decision-making support.

    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?

    No annotations are provided, so the description bears the full burden. It accurately indicates a read-only check but offers no additional behavioral context such as authentication requirements, rate limits, or return format. It does not contradict any annotations.

    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 a single, concise sentence that front-loads the key action 'Check'. Every word is necessary and there is no redundancy or filler.

    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 simplicity (no parameters, read-only, no output schema), the description is nearly complete. It could potentially mention what the output contains (e.g., credit balance amount, plan name) but this is not critical for a basic check tool.

    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 no parameters and 100% schema coverage (empty schema), the description adds value by stating the tool's purpose beyond the empty schema. It effectively explains what the tool does without needing parameter details.

    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 retrieves credit balance and subscription plan, with a specific verb 'Check' and resource 'Litra.ai credit balance and subscription plan'. It distinguishes well from sibling tools that involve searching for authors or papers.

    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 checking account details but provides no explicit guidance on when to use this tool versus alternatives or any prerequisites. The context of siblings suggests it's a read-only utility, but no exclusions or caveats are stated.

    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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Glama performs regular codebase and documentation scans to:

  • 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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