AlphaGenome MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
Multiple tools have overlapping purposes that could cause confusion. For example, 'predict_variant_effect' and 'analyze_gwas_locus' both handle variant analysis, while 'predict_expression_impact', 'predict_splice_impact', 'predict_tf_binding_impact', and 'predict_chromatin_impact' all focus on specific regulatory modalities, making it unclear when to use one over the other. The descriptions help, but the boundaries between tools are often unclear.
Naming Consistency4/5
The naming is mostly consistent with a verb_noun pattern, such as 'analyze_gwas_locus', 'annotate_regulatory_context', and 'predict_variant_effect'. There are minor deviations like 'batch_modality_screen' (which could be 'screen_modality_batch' for consistency) and 'compare_protective_risk' (which is less clear), but overall, the pattern is readable and predictable.
Tool Count3/5
With 20 tools, the count is borderline heavy for a genomic variant analysis server. While the domain is complex, many tools seem redundant or overly specialized, such as having separate tools for each regulatory modality. This could overwhelm agents and might be streamlined into fewer, more general tools.
Completeness5/5
The tool set provides comprehensive coverage for genomic variant analysis, including prediction, annotation, comparison, batch processing, and reporting. It covers all key aspects like regulatory impact, pathogenicity, tissue specificity, and clinical interpretation, with no obvious gaps. The tools support workflows from fine-mapping to clinical reports effectively.
Average 3.3/5 across 20 of 20 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- 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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'comprehensive' and 'detailed regulatory context,' but doesn't disclose critical behavioral traits like whether this is a read-only operation, if it requires specific permissions, rate limits, or what 'all modalities' entails. The example helps but doesn't fully compensate for the lack of 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 appropriately sized with three sentences and an example, front-loaded with the core purpose. Every sentence adds value: the first states the action, the second details the return, the third gives usage context, and the example illustrates input. No wasted words, though it could be slightly more 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 the complexity (regulatory annotation tool with 5 parameters, no annotations, no output schema, and many sibling tools), the description is incomplete. It lacks details on behavioral traits, parameter meanings, and output format, making it inadequate for an agent to fully understand how to invoke and interpret results without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It doesn't explain any of the 5 parameters (chromosome, position, ref, alt, tissue_type) beyond the example, which implies usage but doesn't clarify semantics like what 'tissue_type' does or the format expected. This leaves significant gaps in 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 tool's purpose: 'Provide comprehensive regulatory annotation for a variant' and 'Returns detailed regulatory context including all modalities.' It specifies the verb ('annotate') and resource ('regulatory context'), but doesn't explicitly differentiate from sibling tools like 'predict_chromatin_impact' or 'predict_tissue_specific' which might overlap in regulatory analysis.
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 provides some usage context with 'Perfect for: variant annotation pipelines, comprehensive reports,' which implies when to use it. However, it doesn't explicitly state when NOT to use it or mention alternatives among the many sibling tools, leaving the agent to infer the best choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'side-by-side comparison' but doesn't disclose behavioral traits like what data is returned, format of results, computational requirements, rate limits, or error conditions. The description is minimal and lacks essential operational context for a tool with complex nested parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with four short sentences and an example. It's front-loaded with the core purpose, followed by usage context. However, the example could be integrated more smoothly, and some sentences feel slightly fragmented.
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 (2 nested object parameters, 0% schema coverage, no output schema, no annotations), the description is incomplete. It lacks parameter explanations, behavioral details, and output information. For a tool comparing genetic variants, this leaves significant gaps in understanding how to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It doesn't explain the parameters at all—no mention of 'protective_variant' or 'risk_variant' objects, their required fields (chromosome, position, ref, alt), or what these represent. The example 'Compare APOE protective allele vs risk allele' hints at usage but doesn't clarify parameter structure or semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Compare protective vs risk alleles directly' and 'Side-by-side comparison of alleles with opposite disease associations.' This specifies the verb (compare) and resource (alleles with opposite disease associations). However, it doesn't explicitly differentiate from sibling tools like 'compare_alleles' or 'compare_variants,' which appear to have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage context with 'Perfect for: disease mechanism studies, therapeutic target identification' and an example, which implies when to use this tool. However, it doesn't explicitly state when NOT to use it or mention alternatives among the many sibling tools (e.g., compare_alleles, compare_variants), leaving the agent to infer proper 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 of behavioral disclosure. While it mentions 'comprehensive clinical report' and 'full analysis,' it doesn't describe what the report contains, its format, whether it's a read-only operation, any rate limits, or authentication needs. For a tool with no annotations and potentially complex output, 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 appropriately concise with three sentences and an example. It's front-loaded with the core purpose, followed by elaboration and usage context. However, the example could be more integrated, and there's minor redundancy in 'clinical reports' and 'diagnostic summaries.'
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 (clinical variant reporting), no annotations, no output schema, and 0% schema description coverage for 5 parameters, the description is incomplete. It doesn't explain the report's content, format, or how parameters like 'tissue_type' affect the analysis, leaving too much undefined for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 5 parameters have descriptions in the schema. The description doesn't explain what 'chromosome,' 'position,' 'ref,' 'alt,' or 'tissue_type' mean or how they should be used, beyond the example showing a format. This fails to compensate for the complete lack of schema documentation, leaving parameters semantically unclear.
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: 'Generate comprehensive clinical report for a variant' with 'full analysis with all modalities and clinical interpretation.' This specifies the verb (generate), resource (clinical report), and scope (variant analysis). However, it doesn't explicitly distinguish this from sibling tools like 'explain_variant_impact' or 'assess_pathogenicity' which might overlap in clinical interpretation.
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 provides some usage context with 'Perfect for: clinical reports, diagnostic summaries' and an example, which implies when to use it. However, it doesn't explicitly state when NOT to use it or mention alternatives among the many sibling tools, leaving the agent to infer appropriate usage scenarios without clear boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the analysis type ('DNase and ATAC-seq predictions') and focus ('chromatin accessibility effects'), but lacks critical behavioral details: what permissions or data access are needed, whether it's computationally intensive, what the output format looks like (no output schema), or any rate limits. For a tool with 5 parameters and 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with four short sentences/points. It's front-loaded with the core purpose ('Focus on chromatin accessibility effects only. Analyzes DNase and ATAC-seq predictions...'), and each sentence adds value (scope, purpose, usage context, example). There's no redundant or wasted text.
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 (5 parameters, 0% schema coverage, no annotations, no output schema, and many sibling tools), the description is incomplete. It covers the 'what' and some 'when', but misses critical details: parameter meanings, behavioral traits (e.g., computational requirements), output format, and differentiation from siblings. For a tool in a crowded namespace with undocumented parameters, this leaves too many gaps for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter documentation. The description doesn't explain any of the 5 parameters (chromosome, position, ref, alt, tissue_type) beyond what's implied in the example. It doesn't clarify what 'tissue_type' expects, the format for ref/alt, or the significance of these inputs. The description fails to compensate for the lack of schema 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 tool's purpose: 'Analyzes DNase and ATAC-seq predictions for chromatin state changes.' It specifies the verb ('analyzes') and resource ('chromatin state changes'), and the first sentence 'Focus on chromatin accessibility effects only' provides additional scope. However, it doesn't explicitly differentiate from sibling tools like 'predict_expression_impact' or 'predict_tf_binding_impact' that might also involve chromatin-related analysis.
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 provides some usage context with 'Perfect for: enhancer variants, regulatory region analysis' and an example, which implies when to use it. However, it doesn't explicitly state when NOT to use it or name alternatives among the many sibling tools (e.g., when to choose this over 'predict_expression_impact' or 'annotate_regulatory_context'). The guidance is helpful but incomplete 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 the full burden of behavioral disclosure. While it mentions the tool analyzes predictions for expression changes, it doesn't disclose important behavioral traits like whether this is a read-only operation, computational requirements, rate limits, authentication needs, or what format the results will be in. The description provides basic purpose but lacks operational 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 four sentences that each serve a purpose: establishing scope, stating the core function, providing usage contexts, and giving an example. It's front-loaded with the most important information ('Focus on gene expression effects only') and avoids unnecessary verbosity.
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 genetic variant analysis, 5 parameters with 0% schema coverage, no annotations, and no output schema, the description is insufficiently complete. It explains what the tool does at a high level but doesn't provide enough information about parameters, expected outputs, or behavioral characteristics for an agent to confidently use this tool in practice.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for 5 parameters, the description must compensate but fails to do so. It doesn't mention any of the parameters (chromosome, position, ref, alt, tissue_type) or explain their meaning, leaving the agent with only the schema's technical constraints but no semantic understanding of what these parameters represent in the context of expression impact analysis.
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 analyzes RNA-seq and CAGE predictions for expression changes, which is a specific verb (analyzes) and resource (RNA-seq and CAGE predictions). It distinguishes from siblings by focusing on 'gene expression effects only' and mentions eQTL analysis and expression-related variants. However, it doesn't explicitly differentiate from similar-sounding siblings like 'predict_variant_effect' or 'explain_variant_impact'.
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 provides implied usage guidelines with 'Perfect for: eQTL analysis, expression-related variants' and an example, giving some context about when to use it. However, it doesn't explicitly state when NOT to use it or mention alternatives among the many sibling tools, leaving the agent to infer appropriate usage 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 the full burden. It mentions 'efficient batch analysis' and an example, but doesn't disclose critical behavioral traits: whether this is a read-only or mutation operation, what permissions or authentication might be needed, rate limits, what the output looks like, or any side effects. 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by efficiency note, usage context, and an example. Each sentence adds value without redundancy, making it easy to scan and understand 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, 0% schema description coverage, no output schema, and 2 parameters with complex nested objects (variants), the description is incomplete. It lacks details on behavioral traits, parameter semantics, output format, and how it differs from siblings. For a batch analysis tool in a domain with many similar tools, this leaves too many gaps for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'variants' and 'tissues' but doesn't explain their semantics beyond the example. The schema shows 'variants' as an array of objects with chromosome, position, ref, alt, and 'tissues' as an array of strings, but the description adds minimal meaning (e.g., no details on tissue format or variant requirements). This falls short given the low 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: 'Analyze multiple variants across multiple tissues' and 'Efficient batch analysis of variants × tissues combinations.' This specifies both the action (analyze) and resources (variants, tissues) with the batch context. However, it doesn't explicitly differentiate from sibling tools like 'predict_tissue_specific' or 'compare_variants,' which prevents a perfect score.
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 provides some usage context: 'Perfect for: large-scale tissue-specificity studies' and includes an example. This implies when to use it (for batch analysis in tissue studies) but doesn't explicitly state when not to use it or name alternatives among the many sibling tools, such as when to choose this over 'predict_tissue_specific' or 'compare_variants.'
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 the tool is for 'understanding effects,' which hints at analysis, but doesn't disclose behavioral traits such as whether it performs read-only analysis, requires specific permissions, has rate limits, or what the output format might be. The example adds some context but lacks operational details.
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: the first sentence states the purpose, the second provides usage context, and the third gives an example. Every sentence adds value without redundancy, making it efficient 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 the complexity (4 parameters with 0% schema coverage, no annotations, no output schema), the description is incomplete. It lacks details on parameter meanings, behavioral traits, and output expectations. While concise, it doesn't provide enough context for an agent to fully understand how to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It doesn't explain any parameters beyond the example mentioning 'chr19:44908684' (which hints at chromosome and position) and 'T>C vs T>G vs T>A' (hinting at ref and alts). However, it fails to define what 'chromosome,' 'position,' 'ref,' and 'alts' mean semantically, leaving parameters largely undocumented.
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: 'Compare different alleles at the same position.' It specifies the verb 'compare' and the resource 'alleles,' and distinguishes itself from siblings like 'compare_variants' by focusing on alleles at a single position. However, it doesn't explicitly differentiate from 'compare_variants_same_gene,' which might overlap in scope.
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 provides some context: 'Useful for understanding effects of different mutations at a hotspot position.' This implies usage for mutation analysis at specific positions, but it doesn't explicitly state when to use this tool versus alternatives like 'compare_variants' or 'compare_variants_same_gene,' nor does it mention exclusions or prerequisites.
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 the tool 'ranks variants by regulatory impact,' it doesn't describe what the ranking criteria are, what format the output takes, whether this is a computationally intensive operation, what happens with invalid inputs, or any rate limits. For a tool with 4 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with four sentences that each add value: stating the core function, explaining the ranking purpose, providing usage context, and giving an example. It's front-loaded with the main purpose and wastes no words. The structure could be slightly improved by integrating the example more naturally, but overall it's efficient.
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 GWAS analysis, 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how the ranking works, what regulatory impact means in this context, or how the parameters interact. For a tool that presumably returns ranked variants with impact scores, the lack of output information is a significant gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for 4 parameters, the description provides no information about what the parameters mean or how they should be used. The description mentions 'variants' and 'GWAS locus' but doesn't explain the relationship between the 'variants' array parameter and the 'chromosome', 'start', and 'end' parameters. The example is too vague to provide parameter guidance.
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: analyzing variants in a GWAS locus to rank them by regulatory impact for fine-mapping and causal variant identification. It specifies the verb 'analyze' and resource 'GWAS locus variants', making it distinct from many sibling tools that focus on prediction, comparison, or annotation rather than comprehensive locus analysis. However, it doesn't explicitly differentiate from all siblings like 'batch_score_variants' or 'explain_variant_impact' which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context with 'Perfect for: GWAS follow-up, fine-mapping, identifying causal variants' and includes an example. This gives good guidance about when to use this tool. However, it doesn't explicitly state when NOT to use it or mention specific alternatives among the sibling tools, which would be needed for a perfect score.
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. While it mentions 'detailed analysis' and provides use cases, it doesn't describe what the tool actually returns, whether it's computationally intensive, what permissions might be required, or any limitations. For a tool with 5 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with 4 sentences that each add value: purpose statement, elaboration, usage contexts, and example. It's front-loaded with the core purpose and efficiently structured. The example is particularly helpful for understanding parameter format.
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 5 parameters with 0% schema coverage, no annotations, no output schema, and a complex domain (genomic analysis), the description is insufficiently complete. It doesn't explain what kind of analysis results to expect, doesn't clarify the optional 'tissue_type' parameter, and leaves too much undefined for a tool with this level of complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for all 5 parameters, the description must compensate but fails to do so. It mentions 'allele-specific effects' and provides an example with chromosome:position:ref>alt format, but doesn't explain what 'tissue_type' parameter does or provide any semantic context for the required chromosome, position, ref, and alt parameters beyond what's implied by the example.
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 as analyzing allele-specific regulatory effects and how each allele affects gene regulation differently. It uses specific verbs ('analyze', 'affects') and identifies the resource (allele-specific effects). However, it doesn't explicitly differentiate from sibling tools like 'compare_alleles' or 'predict_variant_effect' which might have overlapping domains.
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 explicit usage contexts with 'Perfect for: ASE analysis, imprinting studies' which gives clear guidance on when to use this tool. It also includes an example that demonstrates typical usage. However, it doesn't specify when NOT to use this tool or mention alternatives among the many sibling tools available.
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 'analyzes' and is for 'investigating,' which implies a read-only operation, but doesn't clarify if it's a prediction, simulation, or data retrieval tool. It lacks details on permissions, rate limits, output format, or any side effects. For a tool with 5 parameters and 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 appropriately sized and front-loaded, starting with the key constraint 'Focus on splicing-specific effects only.' Each sentence adds value: defining scope, listing analyses, providing usage context, and giving an example. There's no wasted text, though the structure could be slightly more polished for a perfect score.
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 (5 parameters, 0% schema coverage, no output schema, no annotations), the description is incomplete. It covers purpose and usage well but lacks parameter explanations, behavioral details, and output information. For a prediction/analysis tool in a server with many siblings, more context is needed to ensure the agent can use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining parameters. It doesn't mention any parameters explicitly, only implying through the example 'chr6:41129252C>T' which maps to chromosome, position, ref, and alt. It omits tissue_type entirely and provides no details on format, constraints, or meaning. With 5 parameters and low coverage, this is inadequate compensation.
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: 'Analyzes splice sites, splice site usage, and splice junctions' and provides a specific example. It distinguishes from siblings by focusing on 'splicing-specific effects only,' which helps differentiate it from tools like predict_expression_impact or predict_variant_effect. However, it doesn't explicitly name a resource or target beyond 'splicing variants,' making it slightly less specific than a perfect score.
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 when to use this tool: 'Focus on splicing-specific effects only' and 'Perfect for: investigating splicing variants, understanding splice alterations.' It implicitly distinguishes from siblings by narrowing to splicing, but it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools, which would be needed for a score of 5.
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. While it mentions the analysis method (ChIP-seq predictions), it doesn't describe what the tool actually returns, whether it's a score, prediction, or annotation. It also doesn't mention computational requirements, limitations, or what 'analyzes' entails operationally. The description is insufficient for a mutation tool with zero 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 appropriately sized with four sentences that each serve a purpose: establishing scope, describing method, providing use cases, and giving an example. It's front-loaded with the core purpose. The only minor inefficiency is the repetition of 'TF binding' in multiple sentences.
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 5-parameter mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, how predictions are made, limitations of ChIP-seq predictions, or the purpose of the tissue_type parameter. Given the complexity and lack of structured documentation, the description should provide more operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for all 5 parameters. The description mentions 'TF binding site variants' and provides an example with chromosome:position:ref>alt format, which hints at chromosome, position, ref, and alt parameters. However, it completely omits the tissue_type parameter and doesn't explain the meaning or format requirements for any parameters beyond the basic example.
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 analyzes transcription factor binding site changes using ChIP-seq predictions, which is a specific verb (analyzes) and resource (TF binding site changes). It distinguishes from siblings by focusing on TF binding effects only, though it doesn't explicitly contrast with similar tools like predict_chromatin_impact or predict_expression_impact.
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 when to use this tool: 'Perfect for: TF binding site variants, regulatory element analysis.' It gives a concrete example and specifies the focus area. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many 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. It mentions 'Efficiently tests multiple variants' and 'Perfect for: targeted regulatory screens', which implies batch processing and specific use cases, but fails to disclose critical behavioral traits such as whether this is a read-only or destructive operation, expected runtime, rate limits, authentication needs, or what the output looks like. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with four sentences: a purpose statement, an efficiency note, usage guidelines, and an example. Each sentence adds value without redundancy. It's front-loaded with the core purpose. There's minor room for improvement in tighter phrasing, but overall it's efficient 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 the complexity of batch screening with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., safety, performance), output format, error handling, or prerequisites. While it covers purpose and usage well, for a tool with 2 parameters and potential regulatory implications, more contextual information is needed to guide an AI agent 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 50% (only 'modality' has a description). The description adds some meaning by implying parameters through context: 'variants' are screened for 'regulatory effects' based on 'modality', and the example mentions 'splicing effects' which aligns with the enum. However, it doesn't explicitly explain the 'variants' parameter structure or provide additional semantics beyond what the schema minimally offers. With moderate schema coverage, the baseline of 3 is appropriate as the description compensates somewhat but not fully.
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: 'Screen variants across specific regulatory modalities' and 'Efficiently tests multiple variants for specific regulatory effects.' It specifies the verb ('screen', 'tests'), resource ('variants'), and scope ('regulatory modalities', 'regulatory effects'), making it distinct from siblings like 'batch_pathogenicity_filter' or 'predict_expression_impact'. However, it doesn't explicitly differentiate from all siblings, such as 'batch_score_variants', which might have overlapping functions.
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 when to use this tool: 'Perfect for: targeted regulatory screens, modality-specific studies' and includes an example: 'Screen 20 variants for splicing effects.' This gives practical guidance on its intended use cases. However, it lacks explicit alternatives or exclusions, such as when not to use it compared to sibling tools like 'predict_splice_impact' or 'batch_tissue_comparison'.
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 efficiency for large lists and gives an example, but doesn't disclose critical behavioral traits such as performance characteristics (e.g., speed, limitations), error handling, or what 'pathogenicity' means in this context (e.g., based on a specific algorithm or database). 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 appropriately sized with four sentences: a purpose statement, efficiency note, usage examples, and an illustrative example. Each sentence adds value, and it's front-loaded with the core purpose. Minor improvements could include more structured formatting, but it's efficient overall.
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 (filtering variants with a threshold), no annotations, no output schema, and 50% schema coverage, the description is moderately complete. It covers purpose and usage but lacks details on behavior, parameter semantics (especially for 'variants'), and output format. This leaves gaps for an AI agent to invoke it correctly without additional 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 50% (only 'threshold' has a description). The description adds some value by implying 'variants' are filtered based on pathogenicity, but doesn't explain the structure or semantics of the 'variants' array beyond what the schema provides (e.g., what pathogenicity scores are associated with each variant). It partially compensates for the coverage gap but not fully.
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: 'Filter variants by pathogenicity threshold' and 'Efficiently identifies pathogenic variants from large lists.' It specifies the verb ('filter'), resource ('variants'), and key constraint ('pathogenicity threshold'). However, it doesn't explicitly differentiate from sibling tools like 'assess_pathogenicity' or 'batch_score_variants', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context with 'Perfect for: VCF filtering, prioritizing clinical variants' and an example, giving practical scenarios. However, it lacks explicit guidance on when to use this tool versus alternatives like 'assess_pathogenicity' or 'batch_score_variants', which could be relevant for similar tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions ranking by impact but doesn't disclose what criteria are used for ranking, whether this requires specific data sources, what the output format looks like, or any limitations. For a comparison/ranking 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 efficiently structured with a clear purpose statement, usage context, and example. All sentences add value, though the example could be more informative. It's appropriately sized for the tool's complexity.
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 comparison tool with no annotations and no output schema, the description provides adequate basic context about purpose and usage but lacks details about behavioral traits, ranking methodology, and output format. Given the 2 parameters with partial schema coverage, the description is minimally complete but leaves important 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 50% (only gene_name has a description). The description doesn't add any parameter-specific information beyond what's implied by the tool name. It doesn't explain the expected format of variants, what chromosome values are valid, or clarify that gene_name is optional despite being described as 'for context.' Baseline 3 is appropriate given the schema does some work but gaps remain.
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: 'Compare multiple variants within the same gene' and 'Ranks variants by impact within a single gene context.' It specifies the verb (compare/rank) and resource (variants within a gene), but doesn't explicitly differentiate from the sibling tool 'compare_variants' which appears to be a more general comparison tool.
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 when to use this tool: 'Perfect for: gene-level analysis, compound heterozygote analysis' and includes an example. However, it doesn't explicitly state when NOT to use it or mention alternatives like the sibling 'compare_variants' tool for cross-gene comparisons.
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 full burden. It discloses the return format (pathogenicity score, clinical classification, evidence breakdown) which is helpful, but doesn't mention computational requirements, rate limits, authentication needs, or whether this is a read-only operation versus a write operation that might store results.
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 well-structured with clear sections (purpose, returns, usage contexts, example). It's appropriately sized at 6 sentences, though the 'Perfect for' section could be slightly more concise. Every sentence adds value without 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?
For a tool with 5 parameters, no annotations, and no output schema, the description provides good purpose and usage context but lacks details about computational behavior, error conditions, and the specific format of the 'evidence breakdown' return value. The example helps but doesn't fully compensate for the missing behavioral transparency.
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 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting for parameter documentation.
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 as 'Comprehensive pathogenicity assessment of a genetic variant' with specific actions ('evaluates variant across all regulatory modalities', 'provides clinical classification'). It distinguishes itself from siblings like 'predict_splice_impact' or 'predict_expression_impact' by offering a comprehensive assessment rather than focused predictions.
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 explicit usage contexts with 'Perfect for: clinical variant interpretation, pathogenicity prediction, diagnostic sequencing' and includes an example. However, it doesn't specify when NOT to use this tool versus alternatives like 'batch_score_variants' for multiple variants or 'explain_variant_impact' for detailed mechanistic explanations.
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 of behavioral disclosure. It does well by specifying the batch capacity ('up to 100 variants simultaneously'), ranking behavior ('ranks them by regulatory impact'), and listing the four scoring metrics. However, it doesn't mention important behavioral aspects like execution time, rate limits, authentication requirements, or what happens when variants exceed the limit. The description adds useful context but doesn't fully compensate for the lack of annotations.
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 well-structured and appropriately sized. It starts with the core purpose, adds supporting details about technology and metrics, provides usage context, and ends with an example. Most sentences earn their place, though the 'Powered by Google DeepMind's AlphaGenome model' line feels slightly promotional rather than strictly necessary for tool selection.
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 (batch processing of genetic variants with multiple scoring metrics) and the absence of both annotations and output schema, the description does a decent job but has gaps. It explains what the tool does and when to use it, but doesn't describe the output format, error conditions, or important behavioral constraints. For a tool with no output schema, more information about return values 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 documents all four parameters thoroughly. The description adds some value by explaining the scoring metrics ('rna_seq: Gene expression changes', etc.) and mentioning the 100-variant limit, but doesn't provide additional parameter semantics beyond what's in the schema. 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 tool's purpose: 'Score and prioritize multiple genetic variants using AlphaGenome AI.' It specifies the verb ('score and prioritize'), resource ('genetic variants'), and technology ('AlphaGenome AI'). The description distinguishes this batch processing tool from sibling tools that appear to focus on individual variants or different analyses (e.g., 'predict_splice_impact', 'explain_variant_impact').
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 when to use this tool: 'Perfect for: GWAS post-analysis, VCF filtering, variant prioritization.' It gives an example use case: 'Score these 50 variants and show me the top 10 by regulatory impact.' However, it doesn't explicitly state when NOT to use this tool or mention specific alternatives among the sibling tools, which prevents a perfect score.
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 full burden. It discloses the tool's behavior by specifying what it returns (impact levels, expression/splicing changes, severity comparison) and includes an example. However, it doesn't mention computational requirements, rate limits, error conditions, or whether this is a read-only vs. mutation operation. The behavioral disclosure is adequate but incomplete for a tool with zero 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 efficiently structured with clear sections: purpose statement, what it returns, usage context, and example. Every sentence adds value with zero waste. The front-loaded purpose statement immediately communicates the tool's function, followed by supporting details in a logical flow.
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 (2 nested parameters, no output schema, no annotations), the description provides adequate purpose and usage context but lacks critical parameter semantics. The absence of output schema means the description should ideally explain return values more thoroughly, though it does list what's returned. For a comparison tool with complex genetic variant inputs, the description is minimally complete but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. While it mentions comparing 'two variants', it provides no semantic details about the variant1/variant2 parameters beyond their existence. The description doesn't explain what constitutes a variant, the required format, or the meaning of chromosome/position/ref/alt fields. With 2 complex nested parameters completely undocumented in schema descriptions, this represents a significant gap.
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: 'Compare two variants side-by-side' with specific focus on 'regulatory impacts between two variants'. It distinguishes from siblings like 'compare_variants_same_gene' by not limiting to same-gene comparisons and from 'compare_alleles' by focusing on regulatory impacts rather than general allele comparison. The verb 'compare' and resource 'variants' are specific and well-defined.
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 usage context with 'Perfect for: comparing candidate variants, understanding relative severity' and includes an example. However, it doesn't explicitly state when NOT to use this tool versus alternatives like 'compare_variants_same_gene' or 'assess_pathogenicity', nor does it mention prerequisites or dependencies. The guidance is helpful but lacks explicit exclusions or alternative recommendations.
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 provides 'human-readable explanation' and 'plain language' output, which gives some behavioral context about the output format. However, it doesn't disclose important behavioral traits like whether this is a read-only operation, computational requirements, potential limitations, or error handling for invalid inputs.
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 and well-structured: purpose statement, elaboration, usage guidelines, and concrete example in just four sentences. Every sentence adds value, and the information is front-loaded with the core purpose first.
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 5 parameters with 0% schema coverage, no annotations, and no output schema, the description provides adequate context about the tool's purpose and usage but lacks sufficient detail about parameters, behavioral characteristics, and expected output format. The example helps but doesn't fully compensate for the missing structured information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. The example mentions 'chr9:12345678A>C' which hints at chromosome, position, ref, and alt parameters, but doesn't explain the tissue_type parameter or provide semantic meaning for any parameters beyond the basic example. The description adds minimal value beyond what the bare schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provide human-readable explanation of variant impact' and 'Translates technical predictions into plain language.' It specifies the verb ('explain'), resource ('variant impact'), and distinguishes from sibling tools by focusing on explanation rather than analysis, prediction, or comparison.
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 provides usage guidelines: 'Perfect for: patient reports, non-technical summaries.' It gives a clear context for when to use this tool (human-readable explanations for non-experts) versus when to use sibling tools like predict_* or analyze_* tools for technical analysis.
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 full burden. It explains what the tool does (predicts regulatory impact) and lists specific analyses, but doesn't disclose behavioral traits like computational requirements, runtime, accuracy limitations, data sources, or error conditions. The description adds value but doesn't provide comprehensive behavioral context for a complex AI prediction tool.
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 well-structured and appropriately sized. It starts with the core purpose, explains the technology, lists specific analyses, provides usage context, and includes an example. Most sentences earn their place, though the 'Powered by Google DeepMind's AlphaGenome model' line could be integrated more seamlessly with the opening sentence.
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 complex AI prediction tool with 6 parameters and no output schema or annotations, the description provides good purpose and usage context but lacks details about output format, limitations, or behavioral characteristics. The example helps, but more completeness would be needed for a higher score given the tool's complexity and lack of structured output documentation.
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 6 parameters thoroughly. The description mentions 'single nucleotide change' which aligns with the ref/alt parameters, and lists analysis types that map to output_types enum values, but doesn't add significant meaning beyond what's in the schema descriptions. 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.
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: 'Predict the regulatory impact of a genetic variant using AlphaGenome AI' with specific analyses listed (gene expression, splicing, TF binding, etc.). It distinguishes from siblings by focusing on single nucleotide variant prediction using a specific AI model (AlphaGenome), unlike tools like 'analyze_gwas_locus' or 'batch_score_variants'.
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 when to use it: 'Perfect for: variant interpretation, GWAS follow-up, clinical genomics research' and includes an example. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools, which would be needed for a score of 5.
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 full burden. It discloses key behavioral traits: it's a prediction/comparison tool (not a read or write operation), mentions default tissues and customizability, and describes what it returns ('impact levels and expression changes for each tissue'). However, it doesn't mention computational requirements, rate limits, or potential limitations of the predictions.
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 well-structured with clear sections: purpose, functionality, defaults, returns, use cases, and example. Each sentence adds value. It could be slightly more concise by combining some sentences, but overall it's efficiently written without wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a prediction tool with no annotations and no output schema, the description provides good context: clear purpose, usage guidelines, behavioral transparency about what it returns, and parameter context. The main gap is lack of output format details (what 'impact levels' and 'expression changes' look like structurally), but given the complexity is moderate and schema coverage is complete, this is reasonably comprehensive.
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 5 parameters thoroughly. The description adds minimal value beyond the schema: it mentions default tissues for the 'tissues' parameter and provides context about what the tool does with these parameters. This meets the baseline 3 when schema coverage is high.
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: 'Predict variant effects across multiple tissues' and 'Compares regulatory impact in different tissues to identify tissue-specific effects.' It uses specific verbs ('predict', 'compares', 'identify') and distinguishes from siblings by focusing on tissue-specific comparison rather than general variant analysis or other specific impacts like splicing or TF binding.
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 when to use this tool: 'Perfect for: understanding tissue-specific disease mechanisms, prioritizing relevant tissues.' It includes an example use case. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools for different scenarios.
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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