Server Quality Checklist

Tool Scores

• analyze_safety_profileC
  2.9/5.0
  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 'extracting and comparing adverse events data' and 'provides risk assessment and dose-response relationships', which gives some insight into what the tool does. However, it lacks details on permissions, rate limits, data sources, whether it's read-only or mutative, response format, or error handling—critical for a tool with potential complexity.

  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 concise and front-loaded, stating the core purpose in the first sentence. The second sentence adds value by specifying outputs (risk assessment, dose-response relationships). Both sentences earn their place, with no wasted words, though it could be slightly more structured for clarity.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (analyzing drug safety across trials), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like data sources, permissions, or response format, and while it mentions outputs, it doesn't detail them. For a tool with 4 parameters and potential for rich analysis, more context is needed to guide effective use.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, meaning all parameters are documented in the schema. The description adds no specific parameter semantics beyond what's in the schema (e.g., it doesn't explain 'drug_name' or 'condition' further). However, it implies the scope of analysis (across clinical trials), which loosely relates to parameters but doesn't provide additional value. Baseline 3 is appropriate given high schema coverage.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Analyze safety profile of a drug by extracting and comparing adverse events data across multiple clinical trials.' It specifies the verb (analyze), resource (safety profile of a drug), and method (extracting/comparing adverse events data). However, it doesn't explicitly differentiate from sibling tools like 'compare_adverse_events' or 'search_clinical_trials', which appear related to similar domains.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'compare_adverse_events' or 'search_clinical_trials', nor does it specify prerequisites, contexts where it's preferred, or exclusions. Usage is implied through the description but not explicitly stated.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• compare_adverse_eventsC
  2.9/5.0
  Behavior2/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'compares adverse events' and 'provides baseline reference and evidence' but doesn't specify what format the comparison takes, whether it performs statistical analysis, how it aggregates data across trials, or what the output looks like. For a tool with 4 parameters and no annotations, this leaves significant behavioral gaps.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is appropriately concise with two sentences that directly address purpose and value. The first sentence clearly states what the tool does, and the second explains its utility. There's no wasted verbiage, though it could be slightly more structured with explicit usage guidance.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a tool with 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the comparison output looks like, how results are presented, whether statistical significance is calculated, or how data from multiple trials is synthesized. The description leaves too many behavioral questions unanswered for effective agent use.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'drug_name' relates to 'condition' filtering or what 'limit' controls precisely). Baseline 3 is appropriate when the schema does the heavy lifting.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: comparing adverse events between treatment and control groups for a specific drug across clinical trials. It specifies the verb ('compare') and resource ('adverse events'), but doesn't explicitly differentiate from sibling tools like 'analyze_safety_profile' or 'search_clinical_trials', 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 Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides minimal guidance on when to use this tool, stating it 'provides baseline reference and evidence for drug safety analysis' but offering no explicit when/when-not criteria or alternatives. It doesn't mention how this differs from sibling tools like 'analyze_safety_profile' or when to choose one over the other.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• search_clinical_trialsC
  2.9/5.0
  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 the API version (v2) and that it 'returns study information including protocols, interventions, and outcomes,' but lacks details on rate limits, authentication needs, pagination behavior, error handling, or whether it's read-only or destructive. For a search tool with 9 parameters and no annotations, this is insufficient.

  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 concise and front-loaded, consisting of two clear sentences that state the tool's purpose and what it returns. There's no wasted verbiage, though it could be slightly more structured (e.g., by explicitly listing key use cases).

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (9 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return scope but lacks behavioral details, usage context, and output format explanations. Without annotations or output schema, the agent must infer much from the schema alone, leaving gaps in understanding.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain how parameters interact or provide search syntax). This meets the baseline for high schema coverage but doesn't enhance 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: 'Search clinical trials using ClinicalTrials.gov API v2.' It specifies the action (search) and resource (clinical trials), and mentions the data source (ClinicalTrials.gov API v2). However, it doesn't explicitly differentiate from sibling tools like 'get_study_details' or 'analyze_safety_profile', 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 Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_study_details' (for specific studies) or 'analyze_safety_profile' (for safety analysis), nor does it specify prerequisites or exclusions. This leaves the agent without contextual usage instructions.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• get_study_detailsB
  3.3/5.0
  Behavior2/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations provided, the description carries the full burden of behavioral disclosure. It states it's a read operation ('Get'), but doesn't mention potential errors (e.g., invalid NCT ID), rate limits, authentication needs, or what happens if the study isn't found. This leaves significant gaps for a tool that likely interacts with an external database.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is a single, efficient sentence that front-loads the core purpose ('Get detailed information') without any wasted words. It directly addresses what the tool does and how to identify the resource, making it highly concise and well-structured.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's simplicity (1 parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and parameter context, but lacks details on return values, error handling, or behavioral traits, which are important for a read operation in a clinical context.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The input schema has 100% description coverage, with the parameter 'nct_id' well-documented in the schema. The description adds no additional meaning beyond implying the parameter is required for lookup, so it meets the baseline of 3 without compensating for any gaps.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the verb 'Get' and the resource 'detailed information about a specific clinical trial', specifying the identifier type 'by NCT ID'. It distinguishes from siblings like 'search_clinical_trials' by focusing on retrieval of a single study rather than searching. However, it doesn't explicitly mention what details are included, keeping it at a 4.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines3/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description implies usage when you have a specific NCT ID, contrasting with 'search_clinical_trials' for broader queries. However, it lacks explicit guidance on when not to use it (e.g., for multiple studies) or alternatives beyond the sibling name, making it only implied rather than explicit.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• ae_pipeline_ragA
  3.5/5.0
  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 the tool performs multiple steps 'in one call' to prevent truncation, which is useful context, but lacks critical details like whether it's read-only or mutative, what permissions are needed, rate limits, error handling, or what the output looks like. For a complex pipeline tool with zero annotation coverage, this is a significant gap.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is appropriately sized and front-loaded, with the first sentence stating the core purpose and the second explaining the key benefit ('in one call to prevent LLM response truncation'). Every sentence earns its place, though it could be slightly more concise by combining ideas.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity (a multi-step RAG pipeline), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like safety, performance, or output format, leaving significant gaps for an AI agent to understand how to invoke it correctly and interpret results.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain how parameters interact or provide additional examples). Baseline 3 is appropriate when the schema does the heavy lifting.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose with specific verbs ('fetches, extracts, chunks, retrieves and summarizes') and resource ('clinical trial data'), and distinguishes it from siblings by emphasizing it's an 'Advanced RAG pipeline for adverse events analysis' that handles everything 'in one call to prevent LLM response truncation'—unlike the more focused sibling tools.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  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 ('for adverse events analysis' and 'to prevent LLM response truncation'), implying it's a comprehensive alternative to multiple calls. However, it doesn't explicitly state when not to use it or name specific sibling alternatives for comparison, which would elevate it to a 5.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.