Genomic-Agent-Discovery
Server Quality Checklist
Latest release: v1.3.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: separate tools for querying different databases (ClinVar, CADD, AlphaMissense, etc.), communication tools, and pharmacogenomics. Overlap is minimal and well-handled.
Naming Consistency4/5Tools follow a consistent verb_noun pattern, but there is a slight inconsistency between 'get_' (e.g., get_acmg_genes) and 'query_' (e.g., query_clinvar) for database lookups, though the distinction appears intentional.
Tool Count3/5With 24 tools, the count is on the higher end of acceptable. While the domain requires many database queries, some tools could potentially be combined, but overall it remains manageable.
Completeness4/5The tool set covers a wide range of genomic queries (multiple databases, pharmacogenomics, HPO, ACMG genes), agent coordination, and prioritization. Minor gaps exist (e.g., no variant annotation upload), but core workflows are well covered.
Average 4/5 across 24 of 24 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
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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 provided, so description carries full burden. It only mentions plain-language explanations, but omits behavior like response format, error handling, or 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no wasted words, front-loaded with key action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 1-param tool with full schema coverage and no output schema, description adequately covers purpose and context about SNPedia. Slight gap on return format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with description for rsid. Description adds no extra meaning beyond schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'look up' and resource 'SNPedia summary for a variant', distinguishing it from siblings like query_clinvar (different database).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use, nor alternatives. Implied usage for SNPedia data, but no guidance on context or comparison to siblings.
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 bears full responsibility for behavioral disclosure. It states the tool returns a class and score but does not detail error behavior (e.g., handling of invalid rsIDs or non-missense variants), rate limits, or authentication requirements. The transparency is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at one sentence with no extraneous information. It efficiently conveys the purpose and output. A slightly more structured format (e.g., stating input, output) would merit a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input schema (1 parameter) and no output schema, the description adequately covers what the tool does and what it returns (class and score). It lacks details like possible class values but is complete enough for an agent to understand basic usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the single parameter 'rsid' as a dbSNP rsID, achieving 100% coverage. The description adds no new semantic meaning about the parameter beyond what is in the schema, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as performing a lookup of AlphaMissense AI pathogenicity predictions for missense variants, specifying it returns class and score. This distinctly separates it from sibling tools like query_cadd or query_clinvar which serve different variant annotation purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when an AlphaMissense prediction is needed but lacks explicit guidance on when to use this tool versus alternatives (e.g., query_cadd, query_clinvar). No prerequisites or exclusion criteria are mentioned, leaving selection to the agent's inference.
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?
No annotations are provided, so the description carries the full burden. It describes the output (pharmacogenes, medications, levels, safety notes) but does not explicitly state that this is a read-only operation, nor does it mention any authorization or data freshness. For a simple lookup table, this is acceptable but could be more explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences. The first sentence defines what the tool returns, and the second provides the use case. There is no extraneous information, and the structure is clear and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, no output schema, no annotations), the description covers the essential aspects: what it returns and when to use it. It does not mention pagination or data size, but for a basic lookup table, the description is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the single optional parameter, which already explains its purpose and format. The tool description does not add any additional parameter semantics beyond that, 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.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves the CPIC gene-drug interaction lookup table with specific fields (pharmacogenes, medications, clinical guidelines, safety notes). It also provides a usage context (identify drug interaction risks). However, it does not explicitly differentiate from sibling tools like get_all_pharmacogenomics or get_pharmacogenomics, which might return overlapping data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a usage scenario ('Use this to identify drug interaction risks for a patient's metabolizer profile'), but it does not provide when not to use it or mention alternative tools. Given the sibling tools with similar purposes (e.g., get_all_pharmacogenomics), this is a gap.
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 provided. Description only describes output, not behavioral traits like read-only nature, authentication, or potential limitations. Relies on inference from verb 'look up'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no wasted words. Front-loaded with the primary action, then details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a simple single-parameter tool with no output schema, the description adequately covers what is returned (traits, p-values, etc.). Lacks mention of potential constraints or availability, but sufficient for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter description 'dbSNP rsID'. Description does not add additional semantic value to the parameter beyond mentioning 'variant'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'Look up', resource 'GWAS Catalog associations for a variant', and lists output content. Distinguishes effectively from siblings like query_clinvar or query_snpedia.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage for GWAS data but lacks explicit guidance on when to use this tool versus alternatives, and does not mention any 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, and the description does not disclose limitations, data freshness, authentication needs, or error handling. It only states what it shows, lacking behavioral context beyond the basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, 16 words, front-loading the key purpose. Every word adds value; no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lists the types of data returned (drug-gene interactions, dosing guidelines, clinical annotations), which is sufficient for a single-parameter lookup tool. Lacks mention of failure modes or data format, but overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema describes 'rsid' as 'dbSNP rsID', and the description adds context by referring to 'variant', but adds no new technical detail beyond what the schema already provides. Schema coverage is 100%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Look up PharmGKB pharmacogenomic annotations for a variant' with specific examples of content (drug-gene interactions, dosing guidelines, clinical annotations), distinguishing it from sibling tools like query_clinvar or query_snpedia.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when pharmacogenomic data for a variant is needed, but provides no explicit guidance on when not to use it or how it compares to alternatives (e.g., query_snpedia, query_clinvar).
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?
No annotations provided, so description carries full burden. It discloses that the tool returns a PHRED-scaled score and interpretation of >20. However, it does not mention the full return structure (e.g., raw score, percentiles), potential errors (if rsid not found), or any side effects (e.g., API calls). Basic disclosure but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no extraneous information. Front-loaded with action and core interpretation. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given simple input and no output schema, the description covers purpose and score interpretation. However, it lacks details on error handling (e.g., invalid rsid), rate limits, or confirmation that variant exists in dbSNP. Adequate for a straightforward lookup but could be more robust.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has one parameter (rsid) with schema description 'dbSNP rsID'. Schema coverage is 100%, so baseline 3. Description adds no additional meaning or format details beyond the schema. Does not specify expected format (e.g., 'rs123' vs '123'). Adequate but no added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly specifies the tool looks up CADD score for a variant, defines the score type (PHRED-scaled) and a meaningful threshold (top 1% deleterious). This distinguishes it from sibling tools like query_clinvar (clinical significance) and query_alphamissense (missense pathogenicity).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when CADD score is needed, but does not explicitly state when to use versus alternatives like query_clinvar or query_alphamissense. No exclusion criteria or guidance on limitations (e.g., only works for known dbSNP variants). Adequate but missing explicit context for tool selection.
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, description bears burden. It correctly states a search operation returning clinical evidence, but does not disclose behaviors like rate limits, error handling, or authentication needs. Adequate but no extra detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with main action. Every word adds value; no redundancy or waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter and no output schema, the description is mostly complete. It specifies input type and output nature, though could briefly mention output format for full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description repeats the schema's parameter description without adding new meaning. No additional semantics provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'search' and resource 'CIViC database', and specifies it returns clinical evidence for cancer-relevant variants. It distinguishes from sibling query tools (e.g., query_clinvar, query_cadd) by focusing on cancer variants.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does 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. Implicitly, the focus on cancer variants suggests context, but lacks direct comparison or exclusion criteria found in sibling tools.
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?
Describes the outputs but does not disclose potential issues like network dependency, rate limits, or handling of variants not in ClinVar. Since no annotations are provided, the description bears full burden but is somewhat incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no superfluous words. Purpose stated upfront, then specifics. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter with full schema coverage and no output schema, the description sufficiently explains what the tool does and returns. Lacks minor behavioral details but adequate for a simple lookup.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema provides full coverage (rsid described). Description adds no extra meaning beyond what the schema says. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action ('Look up'), the resource ('ClinVar clinical significance for a variant'), and what it provides ('pathogenicity classifications, associated phenotypes, review status'). Distinct from sibling tools that query other databases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance. Implies usage for variants with rsID, but does not mention alternatives 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 must disclose behavioral traits. It only mentions the batch size limit of 200, but does not describe error handling, whether it is read-only, rate limits, or behavior on partial failures. The description is minimal in behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the core purpose and efficiency benefit. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no output schema and performs a batch lookup of up to 200 items, the description should explain the return format, such as whether it returns a list of genotypes with rsIDs, or error messages for failed lookups. This information is missing, making the tool less predictable for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes the 'rsids' parameter as an array of strings, but the description adds significant context: it is a batch operation for up to 200 rsIDs, and it is more efficient than individual lookups. This goes beyond the schema's technical definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool looks up patient genotypes for multiple rsIDs at once, with a batch limit of 200. It uses specific verb 'look up' and resource 'patient genotypes', and differentiates from the sibling 'query_genotype' by highlighting efficiency for multiple variants.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates this is more efficient than individual lookups when checking many variants, implying it is the preferred tool for batch operations. However, it does not explicitly state when not to use it, such as for a small number of variants, but the sibling tool 'query_genotype' is available as an alternative.
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?
No annotations are provided, so the description carries the burden. It states what the tool does but does not disclose any behavioral traits such as authentication needs, rate limits, or output format. For a read-only query, this is acceptable but not ideal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the action, and explains the output. Every sentence provides value; there is no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (one parameter, no output schema), the description is largely complete. It explains the tool's function and output. It could mention the nature of the returned data (list of terms) but is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with the parameter 'gene' described as 'Gene symbol'. The description adds context that this gene is used to find HPO terms, which is more about tool purpose than parameter details. It does not add format or constraints beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds HPO terms associated with a gene, and explains what HPO terms are (clinical phenotypes linked to variants). This distinguishes it from sibling query tools like query_clinvar or query_gwas, which focus on different databases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for gene-phenotype associations but does not specify when to use this tool versus alternatives like query_clinvar or query_disease_genes. No explicit when-to-use or when-not-to-use guidance is provided.
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?
No annotations are provided, so the description bears the full burden. It explains that messages appear in a shared chatroom visible to all agents and the human operator, which is good. However, it lacks details like rate limits, message size constraints, or whether messages are persisted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences long with no unnecessary words. It front-loads the core action and then provides usage context and visibility details, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple messaging tool with three parameters and no output schema, the description covers purpose, usage, and visibility adequately. A minor gap is whether messages are stored or ephemeral, but overall it is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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. The description adds minor guidance (include specific variants/genes/findings) but does not significantly enhance meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (send), the resource (message), and specifies recipients (another agent or broadcast to all). It distinguishes from sibling tools like get_messages by focusing on sending rather than reading.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context for when to use the tool (share discoveries, ask questions, alert others) but does not explicitly state when not to use it or mention alternatives. The sibling tools suggest other actions, but no direct exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses internal operations: queries CPIC tables and checks patient genotypes. This provides good behavioral context for an AI agent, going beyond a simple 'get' statement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first states purpose, second explains process. No redundancy, front-loaded. Every sentence adds value, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema provided, and the description omits return format details or error conditions. For a complex tool involving multiple queries, the description is adequate but incomplete for an AI to fully anticipate results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter 'gene' with full schema coverage. The description adds functional context (e.g., 'specific gene', 'allele definitions') but does not add new constraints or format details beyond the schema's example. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Describes a specific verb ('Get') and resource ('pharmacogenomic results for a specific gene'). Details that it queries CPIC tables for allele definitions and drug recommendations, clearly distinguishing from sibling tools like get_all_pharmacogenomics or get_cpic_drugs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly suggests use for a single gene query, but does not explicitly state when to choose this over alternatives or provide exclusions. Sibling tools exist for bulk results or drug-focused queries, but no guidance is given.
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, the description must convey behavior. It discloses deduplication and a limit of 3-5 findings. However, it does not mention permissions, side effects, or what agents receive the finding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no redundancy, front-loaded with purpose. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple tool, but lacks information about output or what happens after publishing. Given no output schema, more detail would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is high (83%), so parameters are already well-documented in the schema. The description adds only the 3-5 limit, not specific parameter guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Share an important finding with other agents.' It specifies the action (publish) and the resource (finding), distinguishing it from sibling query and messaging tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: publish 3-5 most significant findings. It mentions deduplication behavior. However, it does not explicitly state when to avoid using this tool or direct to alternatives like send_message.
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?
No annotations provided, so description carries full burden. Describes cross-referencing and patient carrier check, but lacks details on side effects, permissions, or return format. Adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences minimal and direct, first explaining action, second recommending usage. No superfluous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has 1 parameter, no output schema, no annotations. Description covers purpose and usage but omits return value format, which is important for an aggregating tool. Adequate but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage 100% with parameter 'gene' already described. Description adds no additional meaning to the parameter, meeting baseline but not exceeding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb 'Find ALL known variants' and resource 'gene cross-referencing ClinVar, GWAS, and AlphaMissense'. Clearly distinguishes from siblings like query_clinvar, query_gwas, and query_alphamissense that target individual databases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
States 'best starting point for investigating a specific gene', providing clear when-to-use guidance. Does not explicitly list when not to use or alternatives, but context with sibling tools implies appropriate usage.
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?
No annotations are provided, so the description must cover behavioral aspects. It states the return fields but does not disclose whether the operation is read-only, requires authentication, or handles errors. The behavior is simple and partially transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long with no unnecessary words. It front-loads the core purpose and follows with a usage suggestion. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with no parameters and no output schema, the description adequately specifies the resource and return content. However, it does not mention whether the list is complete or paginated, which is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema coverage is 100%. The description does not need to add parameter semantics. Baseline for 0 parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the action ('Get'), the resource ('ACMG Secondary Findings v3.2 gene list'), the size ('73 genes'), and the return fields ('gene name, associated condition, and inheritance pattern'). It effectively distinguishes from sibling tools like get_all_pharmacogenomics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a useful context ('Use this to systematically check all medically actionable genes') but lacks explicit guidance on when not to use or alternatives. No comparison to sibling tools is given.
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?
No annotations are provided, so the description carries the full burden. It mentions that results are ranked by association score but does not explain what the score means, result limits, or data source update frequency. This is adequate but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the core function, and contains no unnecessary words. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter, no output schema, and no annotations, the description is fairly complete. It identifies the data source, the ranking, and the use case. It could optionally mention return format (e.g., gene symbols), but not essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, and the parameter 'disease' already has a description. The description adds examples ('e.g. breast cancer, diabetes') but does not provide additional semantic nuance beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Find', the resource 'genes associated with a disease or condition', and the specific data source 'DisGeNET'. It distinguishes from sibling tools like query_gene or query_gwas by focusing on disease-gene associations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear usage hint: 'Useful to discover which genes to investigate for a particular health concern.' It does not explicitly state when not to use it or mention alternatives, but for a simple one-parameter tool, this is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses the return format (chromosome, position, alleles) and provides examples. However, it does not mention potential failures or edge cases for missing rsIDs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words, front-loading the action and return format efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter query tool with no output schema, the description adequately explains purpose and return values. It lacks guidance on error handling or performance but is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the 'rsid' parameter with an example. The description adds minimal extra meaning by contextualizing the parameter, but does not introduce new constraints or nuances.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Look up', the resource 'patient's genotype', and the specific input 'specific rsID'. It also lists the return fields, making it distinct from sibling tools like 'query_gene'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when needing genotype for an rsID but does not explicitly state when not to use or mention alternatives like batch query, leaving usage context vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description explains the process: retrieves genotypes at key variant positions and derives star alleles using CPIC definitions. It adds behavioral context beyond the empty schema, though it omits explicit read-only or safety statements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, 33 words, directly stating purpose and method with no unnecessary information. Front-loaded with the main action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description explains what is returned (genotypes and derived star alleles). It covers the main outcome but could mention response format or structure to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters and 100% schema coverage, so the description does not need to add parameter info. The baseline for no parameters is 4, and the description provides no extra parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a complete pharmacogenomics panel across all 34 CPIC pharmacogenes, specifying verb 'get' and resource 'pharmacogenomics panel'. It distinguishes from sibling 'get_pharmacogenomics' by emphasizing comprehensiveness.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It describes the tool as 'the comprehensive starting point for pharmacogenomic analysis', implying initial use, but does not explicitly compare with sibling 'get_pharmacogenomics' or provide when-not-to-use guidance.
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?
No annotations provided, so description carries full burden. It implies a read operation with no side effects, but omits details like whether findings are filtered (e.g., by phase, time) or if pagination applies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with action, every sentence adds unique value. No waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, no output schema, and no annotations, the description covers purpose and usage well. Minor gap: no mention of output format or scope (e.g., 'all findings ever').
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Zero parameters and 100% schema coverage; description adds no parameter info (unnecessary). Baseline for 0-parameter tools is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Read') and resource ('findings published by other agents'), clearly distinguishing it from sibling tools like 'get_acmg_genes' or 'publish_finding'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: understand patterns, avoid duplication, build on discoveries. Missing explicit 'when not to use' or direct alternatives, but context from siblings makes it clear.
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?
No annotations provided, so description must cover behavioral traits. It describes a read-only retrieval of all searches, which is non-destructive, but does not disclose return format, pagination, or authentication needs. Adequate for a simple list tool but lacks detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that convey purpose and usage without redundancy. Every token adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and no output schema, the description covers the essential purpose and usage. However, it could hint at the return type or ordering. Still, it is mostly complete for a simple list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has zero parameters and schema coverage is 100% (empty schema). Description adds no parameter-specific info, but baseline for 0 parameters is 4 as no further detail is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool's function: retrieve all web searches across all agents, using a specific verb and resource. It distinguishes itself from sibling tools (e.g., log_web_search, specific query tools) by focusing on aggregated search history.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises using the tool to avoid duplicating research, implying a when-to-use scenario. Does not explicitly state when not to use or name alternatives, but the context and sibling tool names (e.g., log_web_search) provide sufficient inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It implies a read-only operation by 'Check...for messages' and 'Returns...', which is clear for a simple retrieval tool. It lacks details on side effects or rate limits, but the guidance on frequency partially compensates.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences. The purpose is front-loaded, and every sentence adds value—purpose and usage guidance. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema), the description fully covers what it does and how often to use it. It is complete for the level of complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema description coverage is 100% (vacuously). Per guidelines, baseline is 4 for 0-param tools. The description adds no parameter info, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Check' and resource 'agent chatroom', and specifies it returns messages addressed to the agent or broadcast. This distinguishes it from sibling tools like send_message.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises calling every 5-10 tool uses to stay coordinated, providing clear timing guidance. It does not mention when not to use or compare alternatives, but the frequency advice is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that the tool returns a warning if another agent already searched for something similar, which is the key behavioral aspect. It does not mention side effects, auth, or rate limits, but for a logging tool this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no extraneous words. The description is front-loaded with the action and immediately provides usage context. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (one required param, no nested objects), the description covers purpose, usage guidance, and return behavior. No output schema is needed as the return is a warning message, which is described. The description is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema already describes the param. The description does not add new information about the parameter beyond saying it's a web search to be performed. Baseline score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool logs a web search. The verb 'Log' and resource 'web search' are specific. It distinguishes from sibling tools, which are about querying databases or retrieving data, by focusing on logging an action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Call BEFORE every web search to avoid duplicate research across agents.' This provides clear guidance on when to use the tool and hints at when not to (if a warning is returned, you may reconsider the search).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses what the tool returns (variant count, chromosome distribution, sex from X heterozygosity, basic stats) and indicates it is a read operation. However, it does not explicitly state that it is non-destructive or mention any side effects, though given the output, it is likely safe.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the verb and resource, and no unnecessary words. Each sentence adds value: the first defines the output, the second gives usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description explains the return values reasonably well. It covers the key stats but omits potential error cases or prerequisites (if any). For a simple 0-parameter tool, it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the description does not need to add parameter meaning. Per guidelines, zero parameters baselines at 4, and the description provides sufficient context for the tool's operation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets a high-level summary of genotype data, listing specific elements (variant count, chromosome distribution, sex determination). This distinguishes it from sibling tools like 'query_genotype' which likely provide detailed genotype data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs 'Call this first to understand the scope of data available,' providing a clear usage context and implying it should precede other queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool runs Exomiser, requires installation, and may return nothing if dependencies are missing. This is good, though it doesn't detail whether the tool modifies any data or requires specific permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with purpose, and each sentence adds essential context: purpose, usage, and fallback. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (runs external software), the description covers core aspects: purpose, input, dependency, and behavior. While no output schema exists, the description hints at return type ('rank which variants'). A more explicit description of return format would be ideal, but it's sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers 100% of parameters, but the description adds value by explaining that HPO terms describe symptoms, that they can be obtained from query_hpo, and clarifies the inheritance mode enum values. This exceeds the schema's basic documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs Exomiser to prioritize variants by phenotype match using HPO terms. It uses specific verbs and resources ('run Exomiser', 'prioritize variants') and distinguishes from sibling tools by being the gold-standard for rare disease variant prioritization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (for rare disease variant prioritization with HPO terms) and provides clear guidance on alternatives ('use other query tools instead') if Exomiser is not available. Also mentions installation prerequisite.
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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