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Augmented-Nature

GeneOntology MCP Server

Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: get_go_term retrieves specific term details, get_ontology_stats provides statistical summaries, search_go_terms enables keyword-based discovery, and validate_go_id handles identifier validation. The descriptions clearly differentiate between retrieving, searching, validating, and statistical analysis functions.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern with snake_case formatting: get_go_term, get_ontology_stats, search_go_terms, validate_go_id. The naming convention is perfectly uniform throughout the set, making the tools predictable and easy to understand.

    Tool Count4/5

    Four tools is reasonable for a GeneOntology server, covering core operations like retrieval, search, validation, and statistics. While slightly minimal, each tool serves a distinct purpose and the count aligns well with the domain's typical query patterns. A few additional tools for relationships or annotations could enhance completeness but aren't essential.

    Completeness3/5

    The tools cover basic query operations well but have notable gaps for a GeneOntology domain. Missing are tools for term relationships (e.g., get_parents, get_children), annotations (e.g., get_annotations_for_term), or ontology traversal. While agents can work with the provided tools, they cannot perform common GO analysis tasks like exploring term hierarchies or retrieving associated genes.

  • Average 3/5 across 4 of 4 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves information but doesn't describe traits like whether it's read-only (implied by 'Get'), error handling for invalid IDs, rate limits, authentication needs, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

    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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action ('Get detailed information'), making it easy to parse. Every part of the sentence earns its place by specifying the resource and scope.

    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 the tool's complexity (retrieving detailed information), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'detailed information' includes (e.g., term name, definition, relationships), potential errors, or response structure. For a tool with these gaps in structured data, the description should provide more context to be fully helpful.

    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 has 100% description coverage, with the 'id' parameter clearly documented as a GO term identifier with an example. The description adds no additional parameter semantics beyond what the schema provides, such as format constraints or validation rules. With high schema coverage, the baseline score of 3 is appropriate as the 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 with a specific verb ('Get') and resource ('detailed information for a specific GO term'). It distinguishes from siblings like 'search_go_terms' (which likely searches multiple terms) and 'validate_go_id' (which validates IDs rather than retrieving information). However, it doesn't explicitly mention what constitutes 'detailed information' or differentiate from 'get_ontology_stats' which might provide broader statistics.

    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 prerequisites (e.g., needing a valid GO term ID), exclusions, or comparisons to sibling tools like 'search_go_terms' for broader queries or 'validate_go_id' for ID validation. Usage is implied but not explicitly stated.

    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 provided, the description carries full burden for behavioral disclosure. It states what the tool does but lacks critical behavioral details: it doesn't specify if this is a read-only operation (implied but not stated), what format the statistics are returned in, whether there are rate limits, authentication requirements, or how 'recent updates' are defined. For a tool with zero annotation coverage, this is a significant gap in transparency.

    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, efficient sentence that front-loads the core purpose ('Get statistics about GO ontologies') and provides specific examples in parentheses. There's zero wasted text, and it's appropriately sized for a simple tool with one parameter. Every word earns its place.

    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 the tool's simplicity (1 optional parameter, no output schema, no annotations), the description is incomplete. It lacks information on return format, error handling, or behavioral constraints. While the purpose is clear, the absence of annotations and output schema means the description should provide more context about what the tool returns and how it behaves, which it doesn't do adequately.

    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 has 100% description coverage, with the single parameter 'ontology' fully documented in the schema. The description adds no additional parameter semantics beyond what's in the schema—it doesn't elaborate on ontology options, default behavior when parameter is omitted, or examples. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra value.

    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 action ('Get statistics') and resource ('GO ontologies'), with specific examples of what statistics are retrieved ('term counts, recent updates'). It distinguishes from sibling tools like get_go_term (which retrieves a single term) and search_go_terms (which searches terms), but doesn't explicitly differentiate from validate_go_id. The purpose is specific and actionable.

    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 when to prefer this over get_go_term for statistical overviews versus detailed term data, or how it relates to search_go_terms for broader queries. There's no context about prerequisites, timing, or exclusions, leaving usage entirely implicit.

    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 provided, the description carries full burden but lacks behavioral details. It doesn't disclose whether this is a read-only operation, potential rate limits, authentication needs, or what the output format looks like (e.g., list of terms with IDs). The description only states the search action without behavioral context.

    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, efficient sentence with zero waste. It front-loads the core purpose ('Search across Gene Ontology terms') and adds necessary scope details ('by keyword, name, or definition') without redundancy or fluff.

    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 no annotations and no output schema, the description is incomplete for a search tool with 5 parameters. It doesn't explain what results look like (e.g., structured data with IDs and names), how pagination works (implied by 'size' but not described), or behavioral traits like rate limits. This leaves significant gaps for an AI agent.

    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 fully documents all 5 parameters. The description adds no parameter-specific information beyond implying search scope ('keyword, name, or definition'), which aligns with the schema's 'query' description. Baseline 3 is appropriate as the schema handles parameter semantics.

    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 action ('Search across') and resource ('Gene Ontology terms'), specifying the search scope ('by keyword, name, or definition'). It distinguishes from sibling 'get_go_term' (which retrieves specific terms) by emphasizing search functionality, though it doesn't explicitly contrast with 'get_ontology_stats' or 'validate_go_id'.

    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 is provided on when to use this tool versus alternatives. The description implies usage for searching GO terms, but it doesn't mention when to choose this over 'get_go_term' (e.g., for known IDs vs. keyword searches) or other siblings, nor does it specify prerequisites or exclusions.

    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 full burden. It mentions two behaviors (format validation and existence checking) but doesn't disclose important traits like whether this is a read-only operation (likely, but not stated), what happens with invalid formats (e.g., returns error vs. false), response format, or any rate limits. The description is minimal and leaves behavioral aspects ambiguous.

    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 extremely concise (one brief sentence) and front-loaded with all necessary information. Every word earns its place by specifying both validation actions. There's no wasted text or redundancy.

    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 the tool's moderate complexity (validation and existence checking), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., boolean, validation details, error messages) or behavioral nuances. For a tool with no structured output documentation, the description should provide more context about results and usage.

    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% (the 'id' parameter is fully described in the schema as 'GO identifier to validate'), so the baseline is 3. The description adds no additional parameter information beyond what the schema provides (no format examples, no constraints like pattern requirements). It simply restates the parameter's purpose without enhancing understanding.

    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 with specific verbs ('validate format' and 'check if term exists') and identifies the resource ('GO identifier'). It distinguishes from sibling tools like 'get_go_term' (which retrieves term details) and 'search_go_terms' (which searches for terms) by focusing on validation and existence checking. However, it doesn't explicitly differentiate from 'get_ontology_stats' which might have overlapping functionality.

    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 context (when you need to validate a GO identifier format and verify term existence), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_go_term' (which might also validate as part of retrieval) or 'search_go_terms' (which might handle partial identifiers). No exclusions or prerequisites are mentioned.

    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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