mymedi-ai-mcp-server

Server Quality Checklist

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Latest release: v1.2.1
Disambiguation4/5
Most tools are clearly distinct by their targeted resource and action (e.g., claims_validate vs. code_lookup), but the multiple drug tools (drug_lookup, drug_enrich, drug_rxnorm) have overlapping purposes that could cause confusion despite descriptive names.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., claims_validate, code_lookup, provider_search), making the naming predictable and easy to parse.
Tool Count4/5
With 20 tools, the set is slightly larger than the typical well-scoped range, but the breadth of healthcare domains (claims, codes, drugs, providers, compliance, trials) justifies the count; no tool feels superfluous.
Completeness4/5
The tool surface covers a wide range of medical workflows (code management, drug interactions, provider intelligence, compliance, etc.). Minor gaps like claim submission or patient data management exist, but core informational and validation tasks are well covered.
Average 3.6/5 across 20 of 20 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- Last stable release on April 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must convey behavioral traits. It mentions 'returns warnings and errors' but does not elaborate on the nature of these, potential side effects, or requirements. Important aspects like how context affects validation or whether the tool modifies any state are omitted, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but at the expense of necessary detail. While it avoids fluff, it does not fully earn its place as it omits critical usage and parameter guidance. The structure is adequate but could be improved with additional 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 tool's complexity (3 parameters including a nested object, no output schema, no annotations), the description is insufficient. It fails to explain the validation criteria, how codeType and context influence results, or what the warnings/errors entail. An agent would lack critical information to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33% (only 'code' has a description). The tool description adds no parameter information at all. For a low-coverage schema, the description must compensate, but it fails to explain 'codeType' or 'context', leaving their semantics ambiguous.
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 identifies the tool as validating a medical code, specifying three aspects (correctness, active status, context) and the output (warnings and errors). This distinctly differentiates it from sibling tools like code_lookup or code_crossref, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lacks any guidance on when to use this tool versus alternatives, such as code_lookup or code_suggest. No prerequisites, exclusions, or context for optimal use are provided, making it difficult for an agent to decide when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description mentions data source (OpenFDA) and output types but lacks details on authentication, rate limits, or behavioral side effects. The tool likely performs multiple API calls, but this is not disclosed.
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?
Single sentence that front-loads the purpose ('drug information enrichment via OpenFDA') and lists outputs. Efficient and to the point, though a more structured format could improve readability.
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?
No output schema is provided, yet the description only lists categories (details, indications, interactions, AI analysis) without describing structure or format. For a tool that returns complex data, more detail on the response is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The description does not add extra meaning beyond the schema's explanation of drugName and searchField, but this is adequate since the schema is self-contained.
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 enriches drug information via OpenFDA and lists outputs (details, indications, interactions, AI analysis). It differentiates from siblings like drug_lookup or drug_interactions by combining multiple aspects. However, the verb 'enrich' is slightly vague without further context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs. alternatives like drug_lookup or drug_interactions. The description implies comprehensiveness but does not state scenarios where enrichment is preferred over basic lookup.
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 must fully disclose behavioral traits. It mentions the source and data categories but omits rate limits, data freshness, read-only nature, or error behavior. Minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, then source. No unnecessary words. Efficiently conveys core functionality.
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?
Adequate for a lookup tool with two well-documented parameters and no output schema, but lacks context on limitations, authentication, or what happens on missing data.
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 coverage is 100%, so the baseline is 3. The description adds no additional parameter meaning beyond what the input schema already provides (e.g., min chars, default search field).
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 provides drug information including label data, adverse events, and diagnosis codes. It distinguishes the general lookup function from more specific sibling tools like drug_interactions, but does not explicitly differentiate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like drug_enrich or drug_rxnorm. No exclusions or prerequisites are mentioned, only the data source (OpenFDA).
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 carries the full burden. It mentions the data source (CDC public domain) which is helpful, but does not disclose any behavioral traits like rate limits, data freshness, or behavior on missing results.
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 two sentences, no wasted words. The first sentence states the main purpose, the second provides the source. Well structured and concise.
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?
No output schema is provided, so the description should give some idea of return values. It mentions case counts and trends but lacks detail on format or structure. For a simple lookup, this is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with clear descriptions for condition, code, and state. The description adds that the tool returns case counts and trends, but does not add significant meaning beyond the schema.
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 looks up disease surveillance data (case counts, trends) by condition and geography. It distinguishes from siblings like code_lookup or drug_lookup by specifying public health data from CDC. However, the verb 'Look up' is somewhat generic.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description implies it is for epidemiological data but does not explicitly state when it should be chosen over similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully disclose behavioral traits. While it mentions the data source (OpenFDA FAERS, public domain), it does not discuss rate limits, data staleness, scope (adverse event co-reports vs. clinical interactions), or whether the tool is read-only. This lack of transparency 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, consisting of two sentences that front-load the core action and provide essential context. No unnecessary words.
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 (one parameter, no nested objects, no output schema), the description is adequate but incomplete. It lacks details on the output format, any error handling, or usage limitations. While it covers the basic purpose, agents may need additional information to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides a clear description for the 'drugs' parameter (array of 2-5 drug names). The description adds general context about the data source but does not enhance understanding of the parameter beyond what the schema offers. With 100% schema coverage, baseline 3 is appropriate.
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: checking drug-drug interaction signals from FDA adverse event co-reports. It specifies the output (co-reported reactions and signal strength) and distinguishes itself from sibling tools like drug_lookup or drug_enrich by focusing on interactions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, nor any limitations or prerequisites. The description only states what it does, leaving the agent without context for selecting it appropriately.
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?
Without annotations, the description carries full burden for behavioral disclosure. It only lists return data types but does not specify if the tool is read-only, any side effects, data freshness, or authorization requirements. This is insufficient for an agent to understand operational constraints.
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 sentence, front-loaded with the purpose, and includes the key return items. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two required params and no output schema, the description provides adequate high-level context but lacks detail on return format, data sources, or limits. It could briefly note if results are aggregated or raw.
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 coverage is 100% with clear descriptions for both parameters (state code and specialty). The description adds no new insight beyond restating 'for a specific state', so it meets but does not exceed the baseline expected given schema completeness.
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 performs healthcare specialty market analysis for a specific state, returning provider density, competition metrics, and market opportunity data. This verb+resource combination distinguishes it from sibling tools like provider_enrich or provider_search which focus on individual providers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like provider_enrich or claims_validate. It does not mention any when-not-to-use scenarios 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 provided; description does not disclose side effects, authentication, rate limits, or whether results are cached. 'AI-enriched' implies processing but no further detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load core purpose. No superfluous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Though no output schema, description lists key return categories (practice details, specialties, etc.). For a single-parameter tool, this is fairly complete.
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 coverage is 100% with clear parameter description. Description adds no semantic depth beyond the schema. Baseline of 3 is appropriate.
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?
Clearly states it takes an NPI number and returns enriched provider data (practice details, specialties, affiliations, market context). Distinct from sibling tools like provider_search and provider_payments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives such as provider_search. Does not specify prerequisites or when not to use it.
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. The description merely says 'search' without disclosing read-only nature, pagination, or other behaviors.
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?
Two efficient sentences with no wasted words; front-loaded key purpose.
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?
No output schema or annotations. Description lacks detail on combining parameters, defaults, or output format, making it incomplete for a 7-parameter search tool.
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 43%, and the description adds general categories but does not detail each parameter beyond schema. Baseline 3 is appropriate.
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 it searches the NPI provider directory by name, specialty, or location, distinguishing it from sibling tools like provider_enrich.
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?
No explicit when-to-use or when-not-to-use guidance. Context is implied but no alternative or exclusion criteria are given.
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 fully disclose behavior. It mentions output includes confidence scores, but omits crucial details: whether the tool is read-only, if it requires specific authentication, or any side effects. The agent cannot assess safety or side effects from this description alone.
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?
Two sentences, no wasted words. The verb+resource pattern is front-loaded. Every sentence adds unique information: first sentence states action and domain, second sentence lists notable outputs.
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 no output schema, the description partially compensates by mentioning confidence scores but does not detail the structure of extracted entities (e.g., format, nesting, or error scenarios). It is adequate for a simple extraction tool but leaves some gaps for an agent to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds value by explaining what the extraction produces (specific entity types and confidence scores), which clarifies the purpose of the 'entityTypes' filter parameter. This goes beyond the schema's basic descriptions.
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 verb 'Extract' and the resource 'medical named entities from clinical text'. It lists specific entity types (ICD-10, CPT, dates, medications, and 12 entity types) with confidence scores, distinguishing it from sibling tools like code_lookup or drug_lookup which are lookups rather than extractions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description does not state when not to use it or provide references to sibling tools. The context is implied by the extraction focus, but no proactive help for agent selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It mentions the data source (public domain) but offers no details about response format, error handling, rate limits, or safety (e.g., read-only). This leaves the agent guessing about behavioral traits.
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 two sentences plus a source line, front-loaded with the core action and immediately informative. Every part earns its place without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description covers the essential purpose and parameter context. It lacks output format details but provides enough for basic usage.
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% coverage for the single parameter 'code', and the description provides helpful examples (e.g., 'M79.3', '99213'). However, it adds limited extra meaning beyond the schema itself.
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 action: 'Cross-reference a medical code across ICD-10, CPT, and HCPCS systems' with a specific verb and resource, and it distinguishes from siblings like code_lookup and code_validate by emphasizing cross-system grouping.
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 for cross-system code comparison but lacks explicit guidance on when to use this tool versus siblings like code_lookup or code_validate. No alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'AI-powered' and 'ranked with relevance scores,' suggesting non-deterministic behavior, but lacks disclosure on determinism, authentication needs, rate limits, or fallback behavior. For a generative tool, more transparency about accuracy and latency would be beneficial.
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 two sentences, front-loaded with the main action and output. Every word is purposeful; no fluff or unnecessary detail. It achieves high conciseness while covering the essential function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters (1 required), no output schema, and no annotations, the description explains the input and output format ('ranked suggestions with relevance scores'). It could be more explicit about the output array structure or edge cases, but it is largely complete for a straightforward suggestion tool. The sibling context adds pressure to differentiate, which the description does effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description does not add additional meaning beyond the schema; it only rephrases 'clinical description' for 'description' and 'specific code system' for 'codeType.' The schema already provides clear descriptions for all three parameters.
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 function: 'Get AI-powered medical code suggestions from a clinical description. Returns ranked code suggestions with relevance scores.' It specifies a specific verb ('get'), a resource ('medical code suggestions'), and the input source ('clinical description'). Among siblings like code_lookup and code_validate, this tool is distinguished by being AI-powered and returning ranked suggestions.
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 for generating code suggestions from free-text clinical descriptions, but it does not explicitly state when to use this tool versus alternatives like code_lookup (for direct code searches) or code_validate (for verifying codes). No guidance on when not to use it or prerequisites 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 carries the full burden of behavioral disclosure. It mentions the data source and some return fields, which is helpful, but does not explicitly state that the tool is read-only, lacks rate limit or pagination info, and does not describe error handling or data freshness. The description is adequate but leaves behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded key purpose and return fields, no extraneous text. Every word earns its place. Highly concise and structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description covers the essential: search criteria, return fields (NCT ID, phase, enrollment, eligibility), and data source. It is complete enough for an AI agent to understand the tool's basic function. However, it omits details like default status filter (RECRUITING) and limit behavior, which are only in the schema.
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 coverage is 100%, so the baseline is 3. The description adds value by explaining that 'code' is auto-mapped to condition, which is not in the schema. However, it does not further elaborate on parameter dependencies or constraints beyond what the schema already provides. Overall, the description contributes minor additional meaning.
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 uses specific verbs and resources: 'Search active clinical trials by condition, ICD-10 code, or intervention.' It clearly identifies the tool's function and distinguishes it from siblings that handle claims, codes, drugs, etc. No other sibling tool targets clinical trials, so differentiation is inherent.
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 explicit guidance on when to use this tool versus alternatives like drug_lookup or code_lookup. It states the data source (ClinicalTrials.gov) but does not specify contexts where this tool is preferred or when other tools should be used. This lack of comparative guidance hinders 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, the description provides only basic behavioral context: it returns description, category, active status, and related codes. It does not disclose error handling, partial match behavior, or any read-only guarantee. The information 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the action and lists return fields. It is efficient with no wasted words. However, it could be slightly more structured (e.g., bullet points for return fields) but is still concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with no output schema, the description adequately covers the purpose, accepted codes, and return fields. It lacks details on edge cases or error handling but is sufficient for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the baseline is 3. The description adds no additional parameter semantics beyond the schema; it mentions code string and code systems but those are already in the schema. No value added.
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 looks up medical codes by code string, lists the code systems (ICD-10, CPT, HCPCS), and specifies return fields (description, category, active status, related codes). This distinguishes it from siblings like code_suggest or code_validate, making the purpose unambiguous.
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 the tool is for basic code lookups but does not explicitly state when to use it versus siblings like code_crossref or code_reimbursement. No exclusions or contextual triggers are mentioned, so guidance is limited.
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 states the tool 'returns current status, dates, and expiration info,' which implies a safe, read-only operation. However, it does not mention any authentication requirements, rate limits, or data freshness, leaving some behavioral aspects unclear.
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 sentence of 12 words, efficiently conveying the purpose and return details. It is front-loaded and contains no superfluous information.
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 no output schema, the description partially explains return values (status, dates, expiration). However, it does not specify that at least one of the two optional parameters is required or describe error handling. For a simple lookup, it is minimally sufficient but lacks full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (both parameters have descriptions). The description adds that it returns status, dates, and expiration info, but does not add new meaning beyond what the schema provides for the parameters. Baseline 3 applies.
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 checks the status of a prior authorization request and specifies the return values (status, dates, expiration info). It distinguishes itself from sibling tools like claims_validate by focusing specifically on prior authorization status.
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 does not explicitly provide when-to-use or when-not-to-use guidance, nor does it mention alternatives. The purpose is implied by the name and description, but no exclusion criteria or context for selection among 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 provided; description covers return values (likelihood, confidence, days, factors) but does not mention side effects, auth needs, or rate limits. Adequate for a read-like prediction tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences efficiently deliver purpose and returns with no redundancy. Front-loaded with action verb and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers key return elements but lacks caveats (e.g., model limitations, data recency). Adequate for a straightforward prediction tool given schema richness.
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 80% (4 of 5 parameters described). The description adds no parameter-level details beyond schema, so meets baseline without improvement.
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 uses a specific verb 'Predict' and identifies the resource 'prior authorization approval probability', clearly distinguishing from sibling tools like pa_status (status check) or claims_validate.
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?
Implied usage for predicting approval probability, but no explicit guidance on when to use vs alternatives (e.g., pa_status) or prerequisites like procedure code presence.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description partially compensates by stating it is a lookup (read-only) and describing the output. However, it does not mention potential behavioral aspects such as rate limits, data year, or data refresh frequency. The description is adequate but not exhaustive.
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 concise with two sentences, no redundant information, and front-loads the core purpose. It efficiently conveys essential information without wasting words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the key aspects: what the tool does, what it returns, and the data source. Given there is no output schema and no annotations, it provides a reasonable level of completeness for a simple lookup tool. However, it could be more complete by mentioning data year or pagination.
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 single parameter 'npi' is already well-described in the schema as a 10-digit NPI number. The description adds no extra meaning beyond the schema, so it meets the baseline for full schema coverage but does not enhance understanding.
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: looking up payments to a physician from pharmaceutical and device companies. It specifies the data source (Sunshine Act, CMS Open Payments) and what it returns (total payments, breakdown by type, top paying companies). This distinctively differentiates it from sibling tools like provider_enrich or provider_search.
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 the tool is for retrieving payment data but does not provide explicit guidance on when to use it versus alternatives, nor any prerequisites or exclusions. The context of being public domain is helpful, but without stating when not to use this tool, the agent may lack decision support.
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 the full burden. It discloses the source (CMS PFS RVU 2026) and output (RVU values, estimated payment amounts). It implies a read-only operation ('Look up') but does not explicitly state idempotency or limitations.
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?
Two sentences, no wasted words. Front-loaded with purpose, followed by source and output details. Each sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 2 parameters and no output schema. The description sufficiently explains the output and data source. It could mention error handling or precision, but it is generally complete for a simple lookup tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters documented. The description adds no new parameter details beyond what the schema already provides (including auto-detection for codeType). Baseline 3 is appropriate.
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 looks up Medicare reimbursement rates for a medical code, with specific verb 'look up' and resource 'Medicare reimbursement rates'. It distinguishes from sibling tools like code_lookup (general) and code_crossref (cross-referencing) by focusing on reimbursement.
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 does not explicitly state when to use this tool versus alternatives like code_lookup or code_suggest. While the purpose is clear, there is no guidance on exclusions or prerequisites.
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 disclose behavior. It states it checks and provides fix suggestions, implying non-destructive operation. But it lacks details on idempotency, side effects, or required permissions, leaving gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single concise sentence front-loading the purpose and features, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given nested objects and no output schema or annotations, the description provides adequate context for what the tool does and when to use it. However, return value format and error handling are not addressed, leaving some incompleteness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters, but description adds meaning by explaining that the claim object is validated for errors, missing fields, and code mismatches with fix suggestions, going beyond raw schema structure.
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?
Description clearly states it validates claims before submission, checking errors, missing fields, code mismatches, and providing suggestions. This distinguishes it from sibling tools like code_validate (code-level) and compliance_audit (broader regulatory checks).
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?
Explicitly says 'before you submit to the payer,' indicating when to use. However, it does not mention when not to use or directly reference alternatives, though the sibling context provides differentiation.
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 present, so the description carries full burden. It indicates a read-only scanning operation (no mention of side effects) and specifies returned data (findings, severity, score, recommendations). Could explicitly state it is non-destructive, but current text is sufficiently clear for a read-only audit.
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?
Two sentences, no redundancy, front-loaded with the key qualifier 'HIPAA'. Every word adds meaning, and the structure is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's purpose, input parameters (partially via schema context), and output format. No output schema exists, but the return values are described. It lacks details on default auditType, but the schema covers that. Adequate for an AI agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing baseline 3. The description adds value by elaborating on the 'data' parameter (emphasizing PHI patterns like SSN, MRN, DOB) and mentioning output details, which supplement the schema's generic descriptions.
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 verb ('scans'), resource ('data for PHI exposure'), and provides specific outputs (severity, score, recommendations). It distinguishes from siblings like claims_validate and code_validate by focusing on HIPAA compliance auditing.
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 for HIPAA compliance audits but does not explicitly state when to use this tool versus alternatives (e.g., claims_validate for claim validation). No exclusions or alternate tool references are provided.
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 exist, so the description carries full burden. It discloses that the tool returns RxCUI and optionally checks interactions with severity ratings, and cites the public domain source. No destructive behavior or auth needs are mentioned, but this is acceptable for a read-only lookup tool.
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 two concise sentences. The first sentence states the primary purpose, the second adds an optional feature and source. No fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description covers the key return value (RxCUI) and interaction output (severity ratings). It lacks details on output format or a usage example, but is sufficient for an agent to understand the tool's capability given the rich sibling context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and both parameters have clear descriptions. The description adds value by mentioning that interaction checks include 'severity ratings', which is not in the schema. This provides meaningful context beyond the parameter descriptions.
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 verb 'look up' and resource 'drug in NIH RxNorm', and mentions the optional interaction check. It distinguishes from siblings like drug_lookup and drug_interactions by combining both functions.
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 for normalized terminology and interaction checks, but does not explicitly state when to use this tool vs alternatives among the many sibling tools. No exclusion criteria or comparative guidance is provided.
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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