DrugWise
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation4/5
Tools have distinct purposes, but there is some overlap: orchestrator_check_drug_interactions already queries ClinicalTrials.gov, overlapping with clinical_trials_search_clinical_trials. Also, polypharmacy_risk_check_polypharmacy_risk duplicates the interaction checking logic for multiple medications. Descriptions help differentiate, but boundaries are not perfectly clear.
Naming Consistency3/5Naming uses long descriptive underscores, but prefixes are inconsistent: 'orchestrator_', 'drug_info_', 'drug_alternatives_', 'clinical_trials_', 'polypharmacy_risk_'. Also, 'clinical_trials_search_clinical_trials' is redundant. Overall, pattern is present but mixed.
Tool Count4/5With 5 tools, the server is well-scoped for its purpose. Each tool covers an essential step in the drug interaction workflow. Could potentially be consolidated (e.g., search trials into check interactions), but current count is reasonable.
Completeness4/5Covers core workflow: get info, check interactions, find alternatives, search trials, polypharmacy check. Missing drug-food interactions and allergy checks, but these are tangential. Minor gaps, but agents can still complete main tasks.
Average 3.6/5 across 5 of 5 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 22 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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 should disclose behavioral traits. It does not mention whether the operation is read-only, if interactions are returned sorted or categorized, or any side effects. The description is too minimal to inform the agent about behavioral expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but it lacks sufficient detail. It is front-loaded but fails to earn its place because it omits critical information that could be added without much bloat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool performs a complex check (polypharmacy risk) and has no output schema, the description should explain the output format (e.g., list of interactions, severity). It provides no information about return values or behavior, leaving a significant gap for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for the single parameter. The description adds no meaning about what format medication strings should take (e.g., names, codes), leaving the agent without guidance on how to populate the input correctly.
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 the tool checks every pair of medications in a patient list for interaction risks, specifying the action and resource. It is specific enough to distinguish from some siblings, but does not explicitly differentiate from the similar 'orchestrator_check_drug_interactions' tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, such as the sibling 'orchestrator_check_drug_interactions'. There is no indication of prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It only states the search action but fails to mention safety (read-only), output format, rate limits, or authentication needs. The tool's behavior is largely opaque.
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 a single, efficient sentence that conveys the core purpose without verbosity. However, some essential details are omitted in favor of brevity, preventing a higher score.
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?
For a simple two-parameter tool with no output schema or annotations, the description is insufficient. It does not cover return values, usage boundaries, or integration with sibling tools, leaving significant context gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description only loosely maps 'a drug and current medications' to the parameters. It does not specify formats, constraints, or examples, adding minimal semantic value beyond parameter names.
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 (search), the resource (ClinicalTrials.gov), and the scope (studies related to a drug and current medications). It effectively distinguishes this tool from siblings like check interactions or drug info, as clinical trials research is a distinct use case.
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 guidance is provided on when to use this tool versus its siblings. The description does not mention prerequisites, alternatives, or exclusion criteria, leaving the agent to infer the appropriate context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
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 that the tool queries three sources and returns live evidence with dates, indicating current data. However, it does not mention potential delays, partial failures, or other behavioral traits. The transparency is adequate but not thorough.
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 concise, with four sentences. The first sentence states the purpose, the second gives usage, the third highlights a benefit (current data), and the fourth provides a prerequisite. No unnecessary words.
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 the complexity (three sources, real-time) and no output schema, the description should explain what is returned more thoroughly. It only says 'live evidence with dates,' which is vague. It also doesn't describe the format or structure of the output, leaving gaps.
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?
Schema coverage is 100%, so baseline is 3. The description adds value by advising to use generic names for new_drug and by noting that providing more patient_context yields more specific analysis. This goes beyond the schema descriptions.
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: checking drug interaction safety by querying FDA adverse event reports, PubMed, and ClinicalTrials.gov simultaneously. It distinguishes itself from sibling tools like get_drug_info (mechanism) and polypharmacy_risk (risk assessment).
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 says when to call it: when a doctor wants to prescribe a new drug to a patient already on other medications. It also recommends calling get_drug_info first, providing a clear prerequisite. It does not explicitly mention when not to use or alternatives, but the context is 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?
The description covers what the tool returns but does not explicitly state that it is a read-only operation or disclose any side effects, authorization needs, or rate limits. With no annotations, this is a moderate gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with critical information front-loaded: purpose first, then return contents, then usage advice. Every sentence adds value without redundancy.
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 no output schema, the description adequately explains what the tool returns (brand names, generic names, side effects, etc.) and why each parameter is needed. It also relates to the sibling tool for drug interactions, making the context complete for an AI 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 already provides detailed descriptions for all parameters (100% coverage). The description adds context (e.g., 'needed to ensure the alternative also does not interact' for patient_current_medications) and suggests optional use of drug_class, enhancing clarity 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 explicitly states the tool's purpose: to get safer alternative medications when a dangerous drug interaction is found, distinguishing it from sibling tools like checking interactions or drug info.
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 clearly indicates when to use the tool ('when a dangerous drug interaction is found') and suggests providing the drug class and condition for best results, but does not explicitly exclude other scenarios or mention alternatives.
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 provided, the description carries full burden. It lists the types of information returned (drug class, enzymes, brand names, etc.) and gives usage context. However, it does not mention that the tool is read-only or if it has any side effects, but for a data lookup tool this is sufficient.
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 plus an imperative instruction, all front-loaded with essential information. 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?
The description covers purpose, usage sequence, and scope. Without an output schema, listing the types of information (drug class, enzymes, etc.) provides good completeness. Could be explicit about the exact return structure but is adequate for a lookup tool.
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 one parameter (drug_name) described as 'Drug name — generic or brand name accepted'. The description repeats this but adds context about what the tool does with the name. Baseline 3 since schema already documents the parameter adequately.
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 comprehensive pharmacological information about a drug, listing specific details like drug class, enzyme pathways, brand/generic names, primary use, and common interactions. It distinguishes from sibling tools by specifying it should be called first before check_drug_interactions.
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 'ALWAYS call this tool first before calling check_drug_interactions' and explains why (understanding mechanism helps determine which patient medications to be concerned about).
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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