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ecmulli

Aspen Catalog MCP

by ecmulli
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JavaScript
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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: check_availability verifies if a specific book/title is available, while search_catalog performs broader searches across materials. There is no overlap in functionality, making it easy for an agent to choose the right tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (check_availability and search_catalog) with clear, descriptive names. The naming style is uniform throughout, using snake_case and action-oriented verbs that accurately reflect each tool's purpose.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a library catalog system. While the tools cover basic search and availability checks, typical catalog operations like borrowing, reserving, or managing user accounts are missing, making the set too thin for comprehensive library interactions.

    Completeness2/5

    The tool surface is significantly incomplete for a library catalog domain. It lacks essential operations such as borrowing/returning items, placing holds, managing user profiles, or accessing detailed item metadata. This creates dead ends for agents trying to perform common library tasks beyond basic searching.

  • Average 3.2/5 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.

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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 are provided, so the description carries the full burden of behavioral disclosure. It mentions the return values ('Returns titles, authors, formats, and availability links'), which adds some context beyond the input schema. However, it doesn't describe important behavioral traits like whether this is a read-only operation (implied but not stated), rate limits, authentication needs, error handling, or pagination behavior. For a search tool with zero annotation coverage, this is insufficient.

    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 appropriately concise with two sentences: one stating the purpose and scope, and another describing the return values. It's front-loaded with the core functionality. There's no wasted text, though it could be slightly more structured (e.g., separating usage guidelines).

    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?

    Given the tool's moderate complexity (search with three parameters) and no annotations or output schema, the description is partially complete. It covers the purpose and return values, but lacks usage guidelines, behavioral details (e.g., rate limits), and doesn't compensate for the missing output schema by fully explaining the response format. It's adequate as a minimum but has clear gaps.

    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 schema description coverage is 100%, so the schema already documents all three parameters (query, maxResults, searchIndex) with descriptions, constraints, and defaults. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain search syntax or format details). According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    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 library catalog for books, DVDs, audiobooks, and other materials.' It specifies the verb ('Search') and resource ('library catalog'), and lists the types of materials covered. However, it doesn't explicitly distinguish this from the sibling tool 'check_availability' (which might check availability of specific items rather than searching the catalog), so it doesn't reach the highest score.

    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 provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'check_availability' or explain how they differ (e.g., search vs. specific item lookup). There's no context about prerequisites, limitations, or when not to use it. The description only states what it does, not when to use it.

    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?

    No annotations are provided, so the description carries the full burden. It mentions checking availability but doesn't disclose behavioral traits like response format, error handling, rate limits, or authentication needs. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

    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 two sentences, front-loaded with the core purpose and followed by a useful context sentence. Every sentence earns its place, with no wasted words, making it efficient and well-structured.

    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?

    Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and usage but lacks details on behavior and output. Without annotations or output schema, more context on what the tool returns 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?

    Schema description coverage is 100%, so the schema already documents the parameter 'titles' as a list of book titles. The description adds minimal value beyond this, mentioning 'specific book or title' but not providing additional semantics like format examples or constraints. Baseline 3 is appropriate when schema does the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Check if a specific book or title is available at the library.' It specifies the verb ('check') and resource ('book or title'), and distinguishes it from the sibling tool 'search_catalog' by focusing on availability rather than broader searching. However, it doesn't explicitly contrast with the sibling, so it's not a perfect 5.

    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 clear context: 'Useful for filtering book recommendations to only those the library carries.' This implies when to use it—when you need to verify availability for recommendations. It doesn't explicitly state when not to use it or name alternatives, but the context is helpful.

    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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