Ensembl MCP Server

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, such as get_sequence for sequences and get_homology for homologous genes, but some overlap exists: get_xrefs_by_gene, get_xrefs_by_symbol, and get_xrefs_by_name all handle cross-references with slight variations, which could cause minor confusion. Overall, descriptions clarify boundaries, but the xrefs tools are somewhat redundant.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern, primarily using 'get_', 'lookup_', 'map_', 'overlap_', 'search_', and 'vep_' prefixes, with snake_case throughout. This predictability makes it easy for agents to understand and navigate the tool set without naming conflicts.
Tool Count3/5
With 25 tools, the count is borderline high for a genomic data server, feeling slightly heavy but not excessive. It covers various aspects like sequences, variants, and mappings, but could potentially be streamlined by consolidating overlapping tools (e.g., the xrefs group).
Completeness5/5
The tool set provides comprehensive coverage for genomic and bioinformatics tasks, including data retrieval (e.g., genes, sequences, variants), mapping between coordinates, ontology and taxonomy lookups, and variant prediction with VEP. There are no obvious gaps; it supports full lifecycle operations from search to analysis.
Average 2.9/5 across 25 of 25 tools scored. Lowest: 1.7/5.
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 ISC 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.
Add related servers to improve discoverability.

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

Behavior1/5
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 fails to describe any behavioral traits: it doesn't indicate whether this is a read-only operation, what happens if no matches are found, whether results are paginated, what format the output takes, or any performance characteristics. The description merely restates the name without adding 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.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise with only three words, the description is under-specified rather than efficiently informative. It fails to use its limited space to provide any useful context about the tool's behavior, constraints, or differentiation from siblings. True conciseness balances brevity with completeness, which this description lacks.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no annotations and no output schema, the description is completely inadequate. It doesn't explain what 'search' entails, what results look like, how to interpret them, or when this tool is appropriate versus alternatives. Given the complexity implied by the sibling tools (which include various retrieval and mapping operations), this minimal description leaves critical gaps for an AI agent trying to use the tool correctly.
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 'name' documented as 'Taxonomy name to search'. The description doesn't add any meaningful semantic information beyond what the schema already provides (e.g., it doesn't clarify search semantics like case sensitivity, partial matching, or examples). Given the high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Search taxonomy by name' is a tautology that essentially restates the tool name without adding meaningful specificity. It mentions the verb 'search' and resource 'taxonomy' but doesn't distinguish this tool from its sibling 'search_ontology_by_name' or explain what 'search' means in this context (e.g., exact match, partial match, fuzzy search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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_taxonomy_by_id' (which appears to retrieve by ID rather than name) or 'search_ontology_by_name' (which searches a different resource), nor does it specify any prerequisites, constraints, or typical use cases for this search operation.
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 only states the action without details on permissions, rate limits, response format, or error handling. For a read operation with no annotation coverage, this is insufficient to inform the agent about how the tool behaves beyond its basic function.
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 function without unnecessary words. It is front-loaded and appropriately sized for a simple tool, with no wasted information.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'taxonomy information' includes, how results are structured, or potential limitations. For a tool with no structured behavioral data, more context is needed to guide effective 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?
The input schema has 100% description coverage, with the 'id' parameter documented as 'Taxonomy ID (e.g., 9606 for human)'. The description adds no additional meaning beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as retrieving taxonomy information using an ID, which is clear but vague. It specifies the verb 'Get' and resource 'taxonomy information', but doesn't distinguish it from sibling tools like 'get_taxonomy_classification' or 'search_taxonomy_by_name', leaving ambiguity about what specific information is returned.
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. With sibling tools like 'get_taxonomy_classification' and 'search_taxonomy_by_name', the description lacks any context, prerequisites, or exclusions, leaving the agent to infer usage based on the name 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 but offers minimal insight. It implies a read operation but doesn't cover critical aspects like rate limits, authentication needs, error handling, or the format of returned descendants (e.g., list structure, depth). This leaves significant gaps for safe and effective use.
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 fluff or redundancy. It's front-loaded and appropriately sized, making it easy for an agent to parse quickly.
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 ontology operations, lack of annotations, and no output schema, the description is insufficient. It doesn't explain what 'descendant terms' entail (e.g., hierarchical relationships, data format), potential side effects, or error conditions, leaving the agent under-informed for reliable tool invocation.
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 clear documentation for both parameters ('id' and 'closest_terms'), so the description doesn't need to add parameter details. It mentions 'ontology term' which aligns with the 'id' parameter, but provides no extra semantic context beyond what the schema already offers, meeting the baseline for high 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 action ('Get') and target resource ('descendant terms for an ontology term'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_ontology_ancestors' or 'get_ontology_by_id', which would require mentioning directionality or scope.
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_ontology_ancestors' or 'search_ontology_by_name'. It lacks context about prerequisites, such as needing a valid ontology term ID, or exclusions, leaving the agent to infer usage from the tool name 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 'Get' implies a read-only operation, the description doesn't mention any behavioral traits such as rate limits, authentication requirements, response format, or whether it returns all annotations or a subset. This leaves significant gaps for an agent to understand how to use it effectively.
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's front-loaded with the core action and resource, making it easy to parse quickly.
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 lack of annotations and output schema, the description is insufficient for a tool that retrieves data. It doesn't explain what the output looks like (e.g., list of phenotypes, structured data), potential limitations, or error conditions. For a data-fetching tool with no structured output information, this leaves the agent poorly equipped to handle results.
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 clear documentation for both parameters ('gene_id' as Ensembl gene stable ID and 'species' with a default of 'human'). The description doesn't add any additional semantic context beyond what the schema provides, so it meets the baseline score of 3 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 action ('Get') and resource ('phenotype annotations associated with a gene'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_variants_for_region' or 'get_xrefs_by_gene', which also retrieve gene-related data but for different types of 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. With many sibling tools that also query gene-related data (e.g., 'get_xrefs_by_gene', 'get_variants_for_region'), there's no indication of what makes phenotype annotations distinct or when this specific tool is appropriate.
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 only states what the tool does without mentioning critical aspects like whether it's a read-only operation, potential rate limits, error handling, or the format of returned data. This is inadequate 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, direct sentence with no unnecessary words, making it highly concise and front-loaded. It efficiently communicates the core purpose without redundancy or fluff, earning a top score for brevity and clarity.
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 genomic data tools, no annotations, and no output schema, the description is insufficient. It lacks details on behavioral traits, output format, error conditions, or how it differs from similar tools. This leaves significant gaps for an agent to understand and use the tool effectively.
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, providing clear details for both parameters (species and region). The description does not add any meaning beyond the schema, such as explaining what 'regulatory features' entail or providing examples beyond the schema's region format. Baseline score of 3 is appropriate as 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 'regulatory features in a genomic region', making the purpose understandable. However, it does not distinguish this tool from its siblings (e.g., get_variants_for_region, overlap_region) that also operate on genomic regions, missing explicit differentiation.
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. With many sibling tools like get_variants_for_region and overlap_region that also handle genomic regions, there is no indication of specific use cases, prerequisites, or exclusions, leaving the agent without context for 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 a read operation ('Get') but does not mention permissions, rate limits, error handling, or what the return format looks like (e.g., JSON structure). For a tool with no annotations, 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 directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.
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. It does not explain what the tool returns (e.g., classification details like kingdom, phylum), potential errors, or behavioral traits. For a tool with rich context needs, 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?
The input schema has 100% description coverage, with the 'id' parameter documented as 'Taxonomy ID'. The description does not add any meaning beyond this, such as examples or format details. 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 verb ('Get') and resource ('taxonomic classification for a species'), making the purpose understandable. However, it does not distinguish this tool from its sibling 'get_taxonomy_by_id' or 'search_taxonomy_by_name', which likely serve similar purposes, so it lacks sibling differentiation.
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_taxonomy_by_id' or 'search_taxonomy_by_name'. It lacks context, prerequisites, or exclusions, leaving the agent to infer usage from the tool name 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 states what the tool does but doesn't describe how it behaves: no information about response format, pagination, rate limits, authentication requirements, or error conditions. This leaves significant gaps for an agent to understand operational characteristics.
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 states the core purpose without unnecessary words. It's appropriately sized for a simple retrieval tool and front-loads the essential information.
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?
For a tool with no annotations and no output schema, the description is insufficiently complete. While concise, it doesn't address critical contextual elements like what format variants are returned in, whether there are limitations on region size, how results are structured, or what happens with invalid inputs. The agent lacks necessary operational understanding.
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 thoroughly. The description doesn't add any additional meaning beyond what's in the schema - it doesn't explain parameter relationships, provide examples beyond the schema's region example, or clarify edge cases. 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 action ('Get') and resource ('genetic variants for a genomic region'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'overlap_region' or 'vep_region' that might also operate on genomic regions, but the specificity of 'genetic variants' provides reasonable distinction.
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. Sibling tools like 'overlap_region' or 'vep_region' might serve similar purposes with different outputs, but the description offers no comparison or context about appropriate use cases.
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 action ('Get') but does not describe any behavioral traits such as whether it's a read-only operation, potential rate limits, authentication needs, or what the output looks like. 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.
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 to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It does not explain what the tool returns (e.g., format of external references), behavioral aspects, or usage context. For a tool with no structured data beyond the input schema, more detail is needed to be fully helpful to an 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?
The description does not add meaning beyond what the input schema provides. The schema has 100% description coverage for its two parameters ('gene_id' and 'external_db'), clearly documenting them. Since schema coverage is high, the baseline score is 3, as the description does not compensate or enhance parameter 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 verb ('Get') and resource ('external references (cross-references) for a gene'), making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'get_xrefs_by_name' or 'get_xrefs_by_symbol', which likely serve similar purposes but with different input parameters, so it misses full sibling differentiation.
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 does not mention sibling tools like 'get_xrefs_by_name' or 'get_xrefs_by_symbol', nor does it specify any context, prerequisites, or exclusions for usage, leaving the agent with no usage direction.
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. 'Search' implies a read-only operation, but the description doesn't cover critical aspects like whether this is a safe read, what the return format is (e.g., list of results), pagination, rate limits, or authentication needs. 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 with zero waste. It's front-loaded and appropriately sized for the tool's complexity, avoiding unnecessary elaboration. Every word contributes directly to stating the tool's purpose, 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.
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 100% schema coverage, the description is incomplete. It doesn't explain what 'cross-references' are, what the search returns, or behavioral traits like safety or performance. For a search tool with siblings, more context is needed to help the agent use it effectively, making this inadequate for the tool's complexity.
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 both parameters ('species' and 'name') well-documented in the schema. The description adds no additional meaning beyond the schema, such as explaining what 'cross-references' are or how the search behaves. Baseline is 3 since the schema does the heavy lifting, but the description doesn't compensate or enhance parameter 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 'Search cross-references by name' clearly states the verb (search) and resource (cross-references) with a specific search criterion (by name). It distinguishes from siblings like 'get_xrefs_by_gene' and 'get_xrefs_by_symbol' by specifying the search field, though it doesn't explicitly contrast them. The purpose is specific but could be more precise about what 'cross-references' entails.
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_xrefs_by_gene' or 'get_xrefs_by_symbol' for different search criteria, nor does it specify prerequisites or exclusions. Usage is implied by the name and description alone, leaving the agent to infer context without explicit direction.
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 mentions the tool returns 'detailed information' but doesn't specify format, structure, or potential limitations (e.g., rate limits, authentication needs, what happens if the gene isn't found). For a lookup tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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 - a single sentence that states the core functionality. It's front-loaded with the main purpose and includes useful examples. However, it could be slightly more structured by separating the lookup action from the return information for better clarity.
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 lookup tool. It doesn't explain what format the 'detailed information' returns, doesn't mention error conditions or edge cases, and provides minimal behavioral context. For a tool that presumably returns structured gene data, this leaves too much unspecified for the agent to use it effectively.
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 all three parameters. The description adds minimal value beyond the schema - it provides an example (BRCA2) for the symbol parameter but doesn't explain parameter interactions or provide additional context about species selection or expansion behavior. 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: 'Look up a gene by its symbol' with specific examples (BRCA2) and mentions the type of information returned (ID, location, description). It distinguishes from siblings like 'lookup_gene_by_id' by specifying the lookup method (symbol vs. ID). However, it doesn't explicitly differentiate from 'get_xrefs_by_symbol' which might be a similar lookup tool.
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 this over 'lookup_gene_by_id' or 'get_xrefs_by_symbol', nor does it provide any context about prerequisites, limitations, or typical use cases. The agent must infer usage from the tool name 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 'map coordinates' implies a read-only transformation, the description doesn't specify whether this requires authentication, has rate limits, what the output format looks like, or any error conditions. For a tool with 4 required 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 a single, clear sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly. 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 complexity of genomic coordinate mapping, 4 required parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how coordinates are transformed, or any limitations (e.g., assembly compatibility). For a specialized bioinformatics tool, more context is needed to use it effectively.
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 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain format conventions for 'region' or typical values for 'asm_one'/'asm_two'). With high schema coverage, the baseline score of 3 is appropriate.
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: mapping coordinates between genome assemblies. It specifies the verb 'map' and the resource 'coordinates', making it distinct from sibling tools that focus on homology, ontology, variants, etc. However, it doesn't explicitly differentiate from similar-sounding siblings like map_cdna_to_region or map_translation_to_region, which map different types of genomic features.
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, typical use cases, or how it differs from sibling tools that also perform mapping operations (e.g., map_cdna_to_region). Without this context, an agent might struggle to choose between this and other mapping-related 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. It states the mapping operation but doesn't describe what the mapping entails (e.g., coordinate transformation rules), potential errors, or output format. For a tool with no annotations, 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 directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with no wasted information.
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 (coordinate mapping with 2 parameters), lack of annotations, and no output schema, the description is insufficient. It doesn't explain what the mapping operation produces, how coordinates are transformed, or any behavioral aspects, leaving the agent with incomplete context for proper 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?
The input schema has 100% description coverage, clearly documenting both required parameters ('id' as transcript stable ID and 'region' as cDNA coordinates). The description doesn't add any parameter details beyond what the schema provides, so it meets the baseline of 3 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 verb 'Map' and the resources 'cDNA coordinates to genomic coordinates', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'map_cds_to_region' or 'map_translation_to_region', which appear to perform similar coordinate mapping operations.
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 'map_cds_to_region' or 'map_translation_to_region', nor does it mention prerequisites or exclusions. It simply states what the tool does without contextual usage information.
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 for behavioral disclosure. While 'Map' implies a read-only transformation, the description doesn't specify whether this requires authentication, has rate limits, what happens with invalid inputs, or what format the output takes. For a tool with no annotation coverage, this represents significant gaps in behavioral understanding.
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 states the core functionality without unnecessary words. It's appropriately sized for a straightforward mapping operation and front-loads the essential information.
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 absence of annotations and output schema, plus the existence of similar sibling tools, the description is insufficiently complete. It doesn't explain what 'genomic coordinates' means in practice, how results are returned, or how this differs from related mapping tools. For a tool in a specialized domain with multiple similar alternatives, more context is needed.
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?
With 100% schema description coverage, the input schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain the relationship between 'id' and 'region', provide format examples beyond the schema's single example, or clarify edge cases. 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 verb 'Map' and the resources 'CDS coordinates to genomic coordinates', making the purpose specific and understandable. However, it doesn't distinguish this tool from its sibling 'map_cdna_to_region' or 'map_translation_to_region', which appear to perform similar mapping operations with different input types.
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. With siblings like 'map_cdna_to_region' and 'map_translation_to_region' that likely handle different coordinate types, the absence of usage context leaves the agent guessing about appropriate selection criteria.
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 states the transformation but doesn't disclose behavioral traits such as error handling (e.g., invalid coordinates), output format, whether it's read-only or has side effects, performance considerations, or data sources. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity. 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 no annotations, no output schema, and 2 required parameters, the description is incomplete. It lacks information on what the tool returns (e.g., genomic coordinates format), error conditions, or usage context. For a coordinate-mapping tool with no structured metadata, this leaves critical gaps for an 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 both parameters (id and region). The description adds no additional parameter semantics beyond what's in the schema (e.g., format examples for 'region' are already in 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: mapping protein coordinates to genomic coordinates. It specifies the verb ('map') and the transformation (protein→genomic), which is distinct from sibling tools like map_cdna_to_region or map_cds_to_region that handle other coordinate types. However, it doesn't explicitly differentiate from these siblings beyond the resource name.
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 translation ID), exclusions, or comparisons to siblings like overlap_translation or map_assembly. Usage is implied by the name but not explained.
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 states what the tool does but lacks behavioral details such as output format, pagination, rate limits, authentication needs, or error handling. 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 a single, clear sentence with no wasted words. It's front-loaded with the core purpose, making it highly efficient and easy to parse.
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 insufficient for a tool with 3 parameters and complex genomic operations. It lacks details on return values, error cases, and behavioral traits, making it incomplete 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 fully documents parameters like 'species', 'region', and 'feature' with descriptions and defaults. The description adds no additional semantic context beyond implying region-based filtering, meeting 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 verb ('Get') and resource ('features that overlap a given genomic region'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_variants_for_region' or 'overlap_id', which might have overlapping functionality for genomic regions.
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. With sibling tools like 'get_variants_for_region' and 'overlap_id' that also handle genomic regions, there's no indication of context, prerequisites, or exclusions for selecting this tool.
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 only states what the tool does without behavioral details. It doesn't disclose whether this is a read-only operation, potential rate limits, authentication needs, or what the output format might be (especially critical without an output schema).
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 phrase) and front-loaded with the core purpose. Every word earns its place, with no wasted text or unnecessary elaboration.
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?
For a tool with 2 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain return values, error conditions, or behavioral constraints. The context signals indicate this is a query tool, but the description lacks completeness 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 parameters are well-documented in the schema itself. The description doesn't add any meaningful parameter semantics beyond what's in the schema (e.g., explaining what 'translation stable ID' means or the significance of different feature types). 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 action ('Get') and target ('features overlapping a translation'), with the parenthetical '(protein)' clarifying the biological context. It distinguishes itself from siblings like 'overlap_region' or 'overlap_id' by focusing specifically on translation features, 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 'overlap_region' or 'get_variants_for_region'. The description implies it's for protein-related features, but lacks explicit context, prerequisites, or exclusions for 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 full burden but offers minimal behavioral insight. It doesn't disclose if this is a read-only operation, how results are returned (e.g., list format, pagination), or any constraints like rate limits or authentication needs, leaving 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 with no wasted words, making it easy to parse. It's appropriately sized for a simple search tool, though this conciseness comes at the cost of detail in other dimensions.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of terms, error handling) or behavioral aspects, which is insufficient for a search tool with multiple siblings and no structured context.
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 ('name' and 'ontology') adequately. The description adds no additional meaning beyond the schema, such as examples of term names or ontology usage, resulting in a baseline score of 3.
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 for') and resource ('ontology terms by name'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'search_taxonomy_by_name' or 'get_ontology_by_id', which would require more specificity to earn a 5.
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. With siblings like 'get_ontology_by_id' and 'search_taxonomy_by_name', the description lacks context for selection, such as whether this is for partial name matching or specific ontology types.
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 homology data but lacks details on permissions, rate limits, data sources, or response format. This is insufficient for a tool with potential complexity in biological data retrieval.
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 function without unnecessary words. It is front-loaded and earns its place by clearly conveying the core purpose.
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 (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks behavioral context and usage guidance, leaving gaps for an AI agent to infer proper invocation.
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 parameters. The description adds no additional meaning beyond the schema, such as explaining homology types or species naming conventions. Baseline 3 is appropriate as the schema handles 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: retrieving homologous genes/proteins across species. It specifies the resource (genes/proteins) and the operation (get homologous ones), but doesn't distinguish this from sibling tools like get_ontology_ancestors or get_sequence, which serve different biological queries.
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. While the description implies it's for homology searches, it doesn't specify scenarios, prerequisites, or exclusions, such as when to prefer this over other gene-related tools like get_xrefs_by_gene or lookup_gene_by_id.
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 ancestors but does not mention any behavioral traits such as whether it returns all ancestors or only direct ones, if it includes the input term, error handling for invalid IDs, or performance considerations like rate limits.
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, clear sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without unnecessary elaboration.
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 hierarchical data), lack of annotations, and no output schema, the description is minimally adequate. It covers the basic purpose but lacks details on behavior, output format, or error handling, which would be needed for full completeness in this context.
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 'Ontology term ID'. The description adds no additional semantic context beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the schema handles 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 action ('Get') and resource ('ancestor terms for an ontology term'), making the purpose understandable. However, it does not explicitly differentiate from sibling tools like 'get_ontology_descendants' or 'get_ontology_by_id', which would require more specificity about scope or output.
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. For example, it does not clarify if this should be used instead of 'get_ontology_descendants' for hierarchical navigation or 'get_ontology_by_id' for term details, leaving usage context implied rather than explicit.
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 only states the basic action without disclosing behavioral traits such as read-only nature, error handling, rate limits, or response format. It adds minimal context beyond the obvious.
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 purpose without unnecessary words. Every part earns its place, 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 low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate but lacks details on behavior and usage context. It doesn't fully compensate for the absence of annotations or output schema, leaving gaps in 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%, with the single parameter 'id' documented in the schema. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints, meeting the baseline for high 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 verb ('Get') and resource ('ontology term information'), specifying retrieval by ID. It distinguishes from siblings like 'search_ontology_by_name' by focusing on ID-based lookup, 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_ontology_by_name' or 'get_ontology_ancestors'. The description implies usage for ID-based queries but lacks explicit context 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 for behavioral disclosure. While 'Get' implies a read-only operation, the description doesn't address important behavioral aspects like rate limits, authentication requirements, response format, error conditions, or whether this is a simple lookup versus a complex query. For a tool with no annotation coverage, this represents a significant gap.
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 communicates the core purpose without unnecessary words. It's appropriately sized for a straightforward lookup tool and gets directly to the point with zero wasted verbiage.
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?
For a read-only lookup tool with good schema coverage but no output schema, the description is minimally adequate. It states what the tool does but lacks important context about when to use it versus siblings, behavioral constraints, and what the output contains. The absence of annotations and output schema means the agent must infer much about how this tool behaves in practice.
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 all three parameters adequately. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 reflects that the schema does the heavy lifting for parameter documentation, though the description could have provided context about how these parameters interact or typical usage patterns.
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 cross-references') and resource ('for a gene symbol'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'get_xrefs_by_gene' or 'get_xrefs_by_name', which appear to serve similar cross-referencing functions but with different input types.
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. With siblings like 'get_xrefs_by_gene' and 'get_xrefs_by_name' that likely retrieve similar data, there's no indication of what distinguishes this tool (symbol-based lookup) from those alternatives, leaving the agent to guess based on parameter 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action 'Get features that overlap', implying a read operation, but doesn't disclose any behavioral traits such as rate limits, authentication needs, error handling, or what 'overlap' means in practice (e.g., genomic coordinates, functional overlap). 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 a single, efficient sentence that front-loads the core purpose without any wasted words. It directly states what the tool does, making it easy to parse and understand quickly. Every word earns its place, and there's no redundancy or unnecessary elaboration.
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 (a read operation with 2 parameters, one with an enum), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose but lacks details on behavior, output format, or usage context. For a tool with no output schema, it should ideally hint at what 'features' are returned, but it doesn't, leaving the agent to rely on the schema alone for parameters without guidance on results.
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 clear descriptions for both parameters: 'id' as an 'Ensembl stable ID' and 'feature' as a 'Type of feature to retrieve' with an enum list. The description adds no additional meaning beyond the schema, such as explaining the relationship between 'id' and 'feature' or providing examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
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 'features that overlap a specific feature by ID', which is specific and actionable. It distinguishes from siblings like 'overlap_region' and 'overlap_translation' by focusing on ID-based overlap rather than region or translation-based overlap. However, it doesn't explicitly differentiate from all siblings, such as 'get_xrefs_by_gene' or 'lookup_gene_by_id', which also involve ID-based queries but for different purposes.
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 similar tools like 'overlap_region' or 'lookup_gene_by_id', nor does it specify prerequisites or exclusions. The agent must infer usage from the name and schema 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool returns 'detailed consequence predictions' but lacks critical behavioral details such as computational cost, rate limits, authentication requirements, error handling, or whether it's a read-only or mutating operation. For a complex tool with 31 parameters, 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 appropriately sized with two sentences: the first states the purpose and tool, the second specifies the return content. It's front-loaded with the core function and avoids unnecessary details. However, it could be slightly more structured by explicitly separating purpose from output details.
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 (31 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like performance, limitations, or error cases, and lacks output format details despite no output schema. For a prediction tool with many options, more context on usage and results is needed.
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%, meaning all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain parameter interactions, default behaviors, or provide examples. However, since schema coverage is high, 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 specific action ('Predict variant consequences'), target resource ('for a genomic region'), and tool used ('using the Variant Effect Predictor (VEP)'). It distinguishes itself from sibling tools like 'get_variants_for_region' by focusing on consequence prediction rather than variant retrieval, and from 'overlap_region' by specifying VEP analysis.
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 explicit guidance on when to use this tool versus alternatives is provided. The description mentions what it does but doesn't indicate scenarios where it's preferred over sibling tools like 'get_variants_for_region' or 'overlap_region', nor does it mention prerequisites or exclusions. Usage is implied rather than 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. It states what the tool does but lacks behavioral details such as output format (e.g., FASTA, plain text), error handling, rate limits, or authentication needs. For a tool with no annotations, 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 the DNA/RNA/protein sequence'). There is no wasted text, making it highly concise and well-structured for quick 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 no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., sequence format, metadata), potential side effects, or usage constraints. For a tool with 2 parameters and biological data retrieval, more context is needed to ensure proper agent invocation.
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 parameters (id and type with enum). The description adds minimal value beyond the schema, mentioning 'region or feature' which loosely relates to 'id', but doesn't clarify parameter interactions or provide examples. 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and the resource ('DNA/RNA/protein sequence'), specifying it's for a 'given region or feature'. It distinguishes itself from siblings like get_homology or get_variants_for_region by focusing on sequence retrieval rather than homology, variants, or other genomic 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?
No explicit guidance on when to use this tool versus alternatives is provided. While the description implies it's for sequence retrieval, it doesn't specify scenarios (e.g., for gene annotation vs. protein analysis) or mention related tools like map_cdna_to_region that might overlap in functionality, leaving usage context unclear.
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 only states the basic lookup function. It doesn't disclose behavioral traits like whether this is a read-only operation, what data is returned, error conditions, rate limits, or authentication requirements. The description is minimal beyond the basic purpose.
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?
Single sentence with zero waste - every word contributes essential information. Front-loaded with the core purpose, followed by specific identifier details and example. Perfectly sized for this simple lookup 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?
For a tool with no annotations and no output schema, the description is too minimal. It doesn't explain what information is returned about the gene, whether the lookup is case-sensitive, what happens with invalid IDs, or how the 'expand' parameter affects results. The description should provide more context given the lack of structured metadata.
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 both parameters. The description mentions the 'id' parameter with an example but doesn't add meaning beyond what the schema provides. No credit for repeating schema information, but no penalty since 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 action ('Look up') and resource ('a gene'), specifies the identifier type ('Ensembl stable ID'), and provides a concrete example. It distinguishes from sibling tools like 'lookup_gene_by_symbol' by specifying ID-based lookup rather than symbol-based.
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 when you have an Ensembl stable ID, but doesn't explicitly state when to use this vs. alternatives like 'lookup_gene_by_symbol' or 'get_xrefs_by_gene'. It provides context for the input but lacks explicit guidance on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![ensembl-mcp MCP server](https://glama.ai/mcp/servers/munch-group/ensembl-mcp/badges/card.svg)](https://glama.ai/mcp/servers/munch-group/ensembl-mcp)

Score Badge

Copy to your README.md:

[![ensembl-mcp MCP server](https://glama.ai/mcp/servers/munch-group/ensembl-mcp/badges/score.svg)](https://glama.ai/mcp/servers/munch-group/ensembl-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/munch-group/ensembl-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server