WormBase MCP Server

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose targeting specific biological entities or functions, such as get_gene for genes, get_phenotype for phenotypes, and get_expression for expression patterns. The descriptions explicitly differentiate each tool's scope, with get_entity serving as a fallback for uncovered types, ensuring no ambiguity in tool selection.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using 'get_' or 'search' prefixes, such as get_disease, get_gene, and search. This uniformity makes the tool set predictable and easy to navigate, with no deviations in naming conventions across the 12 tools.
Tool Count5/5
With 12 tools, the server is well-scoped for its biological data domain, covering key entities like genes, proteins, phenotypes, and diseases. Each tool earns its place by addressing specific aspects of WormBase data, avoiding both thin coverage and excessive complexity.
Completeness5/5
The tool set provides comprehensive coverage for accessing WormBase data, including CRUD-like retrieval for all major entity types (e.g., genes, proteins, phenotypes) and a search tool for flexible queries. No obvious gaps exist, as get_entity acts as a catch-all for any uncovered types, ensuring complete access to the database.
Average 3.2/5 across 12 of 12 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
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This repository is licensed under MIT License.
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

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 states the tool retrieves information, implying a read-only operation, but doesn't clarify aspects like authentication requirements, rate limits, error handling, or response format. For a tool with no annotation coverage, this leaves significant behavioral gaps.
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 a single, well-structured sentence that efficiently conveys the tool's purpose and scope. It's front-loaded with the main action and includes specific details without unnecessary elaboration. However, it could be slightly more concise by avoiding the list format, but overall it's effective and wastes no words.
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 complexity (simple read operation with one parameter), lack of annotations, and no output schema, the description is minimally adequate. It covers what information is retrieved but omits behavioral details and usage context. For a tool with no structured output or safety hints, it should provide more completeness, such as response format or error cases.
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 documented as 'Gene identifier'. The description doesn't add any parameter-specific information beyond what the schema provides, such as examples or constraints. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema handles the parameter documentation adequately.
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: 'Get expression pattern information for a gene' with specific details about what information is retrieved (tissue/cell expression, life stage expression, expression images). It uses a specific verb ('Get') and resource ('expression pattern information for a gene'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_gene', which might retrieve different gene information.
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 sibling tools like 'get_gene' or 'search', nor does it specify prerequisites or contexts for usage. The agent must infer usage based on the purpose alone, which is insufficient for optimal tool selection.
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 of behavioral disclosure. It states the tool retrieves interactions but doesn't disclose any behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or what the return format looks like (e.g., list of interactions with details). This leaves significant gaps for an agent to understand how the tool behaves in practice.
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 any unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly, with every word contributing to understanding.
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 complexity of retrieving biological interactions, the lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the return values include (e.g., interaction details, confidence scores, sources), nor does it cover behavioral aspects like error handling or data freshness. This leaves the agent with insufficient context to use the tool effectively beyond basic parameter input.
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 input schema already documents both parameters ('id' and 'interaction_type') with descriptions and an enum for 'interaction_type'. The description adds minimal value beyond this, mentioning the types of interactions but not providing additional syntax, format details, or examples. This meets the baseline for high schema coverage.
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 ('Get') and resources ('protein-protein, genetic, or regulatory interactions for a gene or protein'), making it easy to understand what the tool does. However, it doesn't explicitly distinguish this tool from its siblings like 'get_gene' or 'get_protein', which might also retrieve related information but not specifically interactions.
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 'get_interactions' over sibling tools like 'get_gene' or 'search', nor does it specify any prerequisites or exclusions for usage, leaving the agent to infer context from tool names alone.
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 the full burden of behavioral disclosure. It states the tool retrieves information but lacks details on permissions, rate limits, error handling, or response format. This is a significant gap for a tool with no annotation coverage.
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 a single, efficient sentence that front-loads the purpose. It avoids redundancy and wastes no words, though it could be slightly more structured for clarity.
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 simplicity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks behavioral context and usage guidelines, making it incomplete for optimal agent operation.
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 'id' parameter. The description does not add any parameter-specific details beyond what the schema provides, such as examples or constraints. Baseline 3 is appropriate when the schema handles parameter documentation.
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 verb ('Get') and resource ('scientific paper/publication') along with specific information returned ('authors, abstract, and associated genes'). It distinguishes this from sibling tools like 'get_gene' or 'get_disease' by focusing on papers, though it doesn't explicitly contrast them.
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 like 'search' or other 'get_' tools. The description implies usage for retrieving paper details but offers no context about prerequisites, exclusions, or comparative scenarios.
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 states the tool retrieves information, implying a read-only operation, but doesn't disclose behavioral traits like rate limits, authentication needs, error handling, or response format. For a tool with no 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more structured (e.g., by explicitly listing widget types).
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, no output schema, and a tool that likely returns complex phenotype data, the description is incomplete. It doesn't explain what 'detailed information' includes beyond a few examples, nor does it cover response structure, pagination, or error cases. For a data retrieval tool with rich potential outputs, this leaves significant 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?
Schema description coverage is 100%, so the schema already documents both parameters ('id' and 'widgets') thoroughly. The description adds minimal value beyond the schema by mentioning 'associated genes, RNAi experiments, and variations', which loosely maps to some 'widgets' options, but doesn't provide additional syntax or format details. Baseline 3 is appropriate when 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 verb 'Get' and the resource 'phenotype', specifying what information is retrieved (detailed information including associated genes, RNAi experiments, and variations). It distinguishes from siblings like 'get_gene' or 'get_variation' by focusing on phenotype data, though it doesn't explicitly contrast with them in the description text.
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 like 'get_entity' or 'search'. It lacks context about prerequisites (e.g., needing a phenotype ID) or exclusions, leaving the agent to infer usage from the tool name and parameters alone.
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 of behavioral disclosure. While it implies a read-only operation ('Get information'), it doesn't describe authentication requirements, rate limits, error conditions, or what happens if the strain ID doesn't exist. For a tool with zero annotation coverage, this leaves significant behavioral 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 a single, efficient sentence that front-loads the core purpose and lists key information types without unnecessary elaboration. Every word earns its place, making it appropriately sized for this tool.
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 tool with 2 parameters. It doesn't explain what the return values look like (e.g., structure of strain information), error handling, or behavioral constraints. For a biological data retrieval tool, more context about the response format would be 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?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain the relationship between 'widgets' and the listed information types like 'phenotypes'). Baseline 3 is appropriate when 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 information') and resource ('C. elegans strain'), and lists the types of information retrieved (genotype, available from, associated phenotypes). However, it doesn't explicitly differentiate this tool from sibling tools like 'get_gene' or 'get_phenotype' which might retrieve similar biological data.
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 sibling tools like 'get_gene' or 'get_phenotype' that might retrieve overlapping information, nor does it specify prerequisites or exclusions for usage.
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 the full burden of behavioral disclosure. It states what information is retrieved but doesn't cover aspects like read-only nature, potential rate limits, error handling, or response format. For a tool with no annotations, 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 front-loads the core purpose without unnecessary details. Every word earns its place, making it easy to parse quickly.
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 (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose but lacks behavioral context and usage guidelines, which are important for an agent to operate effectively without structured annotations.
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 the parameters (id and widgets). The description adds no additional meaning beyond what the schema provides, such as explaining the significance of DOID vs. WormBase IDs or widget purposes. Baseline 3 is appropriate when the schema handles parameter documentation.
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 verb 'Get' and the resource 'information about human diseases with C. elegans models', specifying it includes 'associated genes and orthologs'. It distinguishes from siblings like get_gene or get_phenotype by focusing on disease information, though it doesn't explicitly contrast with them.
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 like get_entity or search. The description implies usage for disease data but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer based on tool names alone.
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 the full burden of behavioral disclosure. It states what information is returned but doesn't describe how the tool behaves: e.g., whether it's a read-only operation (implied by 'Get'), error handling for invalid IDs, rate limits, authentication needs, or what happens if widgets are omitted. This leaves significant gaps for a tool with no annotation coverage.
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 and lists included information without unnecessary words. Every part earns its place by specifying scope and content.
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 (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers what the tool does but lacks behavioral details and usage guidance. Without annotations or output schema, more context on behavior and returns would improve completeness for a read operation.
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 fully documents both parameters (id and widgets). The description adds no additional parameter semantics beyond what's in the schema, such as explaining the relationship between id formats or widget options. Baseline 3 is appropriate when the schema does all the work.
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 verb 'Get' and resource 'detailed information about a C. elegans gene', specifying what information is included (description, function, expression, phenotypes, orthologs). It distinguishes from siblings like get_disease or get_protein by focusing on genes, though it doesn't explicitly contrast with similar tools like get_entity or search.
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 choose get_gene over get_entity (which might be more general) or search (which might find genes among other things), nor does it specify prerequisites or exclusions for usage.
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 what information is retrieved but lacks behavioral details such as authentication requirements, rate limits, error handling, or response format. For a read operation with no annotation coverage, this leaves significant gaps in understanding how the tool 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 a single, efficient sentence that front-loads the core purpose and lists key details without waste. Every word earns its place by specifying the action, resource, and examples of returned information, making it highly concise 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 moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and output types but lacks behavioral context and usage guidelines. Without annotations or output schema, more detail on response structure or operational constraints would improve completeness, but it's not entirely incomplete.
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 both parameters ('id' and 'widgets') thoroughly. The description adds no additional parameter semantics beyond implying that 'widgets' controls which detailed information (e.g., sequence, domains) is fetched, but this is redundant with the schema. Baseline 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: 'Get detailed information about a protein including sequence, domains, motifs, and structure.' It specifies the verb ('Get') and resource ('protein') with concrete examples of the information returned. However, it doesn't explicitly differentiate from sibling tools like 'get_gene' or 'get_entity', which likely retrieve different biological entities.
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, context for protein retrieval, or comparisons to sibling tools such as 'get_gene' for gene information or 'search' for broader queries. 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions what information is returned (molecular details, phenotypes, strains), it doesn't describe critical behaviors like error handling (e.g., what happens if an invalid ID is provided), response format, pagination, rate limits, or authentication requirements. For a read operation with no annotation coverage, this leaves significant 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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by specifying the action, resource, and key information returned. There's zero waste or redundancy.
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 (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage context, behavioral traits, and output expectations. Without annotations or an output schema, the description should do more to compensate, but it falls short of being complete for effective agent use.
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 fully documents both parameters ('id' and 'widgets'). The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain format requirements for 'id' beyond the schema's examples, nor does it clarify the purpose or usage of 'widgets' beyond the schema's list. Baseline 3 is appropriate when 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: 'Get information about a genetic variation/allele including molecular details, phenotypes, and strains.' It specifies the verb ('Get'), resource ('genetic variation/allele'), and scope of information returned. However, it doesn't explicitly distinguish this tool from similar sibling tools like 'get_gene' or 'get_phenotype' beyond the resource type.
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 sibling tools like 'get_gene' or 'get_phenotype' that might provide overlapping or related information, nor does it specify prerequisites or exclusions for usage. The user must infer usage from the tool name and description alone.
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 describes what the tool returns (GO terms with three annotation types) but doesn't mention whether this is a read-only operation, if there are rate limits, authentication requirements, error conditions, or response format. For a tool with zero annotation coverage, this leaves significant behavioral 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 a single, well-structured sentence that efficiently communicates the core functionality. It's front-loaded with the main action and includes necessary detail about the three annotation types without unnecessary elaboration. Every word earns its place.
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 (retrieving structured ontology data), no annotations, and no output schema, the description provides adequate basic information about what the tool does but lacks details about behavioral characteristics, response format, and error handling. It's minimally viable but has clear gaps for a data retrieval 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?
The schema description coverage is 100% with the single parameter 'id' documented as 'Gene identifier'. The description doesn't add any parameter-specific information beyond what's in the schema. With high schema coverage and only one parameter, 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.
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 with specific verbs ('Get Gene Ontology terms') and resources ('for a gene'), and distinguishes it from siblings by specifying the exact annotations returned (molecular function, biological process, cellular component). It goes beyond just restating the name by explaining what GO terms are and what types of annotations are included.
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 by specifying it's for retrieving GO terms for genes, but doesn't explicitly state when to use this tool versus alternatives like get_gene or search. There's no guidance on prerequisites, exclusions, or comparative scenarios with sibling tools.
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 of behavioral disclosure. While 'Get information' implies a read-only operation, the description doesn't specify authentication requirements, rate limits, error conditions, or what format the returned information takes. For a tool with 3 parameters and no output schema, this leaves significant behavioral 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 perfectly concise with just two sentences that each serve a clear purpose: stating the tool's function and providing usage guidance. There's no wasted verbiage, and the most important information (purpose and when to use) is front-loaded.
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 complexity (3 parameters, no output schema, no annotations), the description is adequate but incomplete. It excels at purpose and usage guidance but lacks behavioral details about what information is returned, how errors are handled, or authentication requirements. The absence of an output schema means the description should ideally provide more context about return values.
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%, with all parameters well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema (type, id, widgets). This meets the baseline expectation when schema coverage is complete.
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 verb ('Get information') and resource ('any WormBase entity type'), and explicitly distinguishes it from siblings by specifying 'for entity types not covered by specific tools.' This provides precise differentiation from the listed sibling tools like get_gene, get_disease, etc.
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 explicitly states when to use this tool ('for entity types not covered by specific tools'), providing clear guidance on alternatives. This directly addresses the sibling tools listed, making it easy for an agent to choose between this general-purpose tool and the specialized ones.
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?
With no annotations provided, the description carries the full burden. It discloses that the tool supports natural language queries and specific IDs, but lacks details on behavioral traits like rate limits, authentication needs, error handling, or result format. It adequately describes the core functionality but misses operational 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 appropriately sized and front-loaded, with two sentences that efficiently convey the tool's purpose and usage examples without unnecessary details, earning its place.
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 complexity of a search tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on result format, pagination, error cases, or how it differs from sibling tools, leaving gaps for an AI agent to understand full behavior.
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 schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the 'query' parameter semantics with examples ('genes involved in longevity', 'WBGene00006763'), enhancing understanding beyond the schema, though it doesn't cover all parameters in detail.
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 with specific verbs ('search WormBase') and resources ('genes, proteins, phenotypes, strains, and other biological entities'), distinguishing it from sibling tools that fetch specific entity types rather than performing general searches.
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 for usage by specifying what can be searched (biological entities) and giving query examples, but it does not explicitly state when to use this tool versus the sibling 'get_*' tools or provide exclusions.
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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