CMS Healthcare Data MCP Server

by SaeMind

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: cache status, data retrieval, sample queries, schema, listing datasets, and executing named queries. No overlap or ambiguity.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern in snake_case (e.g., cache_status, get_data, list_datasets). No mixing of conventions.
Tool Count5/5
6 tools is well-scoped for a healthcare data server, covering discovery, schema, data retrieval, queries, and cache monitoring. Neither too few nor too many.
Completeness4/5
Covers key operations: listing datasets, getting schema, retrieving data, executing pre-built queries, and cache info. Missing clear cache or dataset-level metadata beyond list, but sufficient for its read-only purpose.
Average 3.6/5 across 6 of 6 tools scored. Lowest: 3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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 must disclose behavioral traits. It mentions the return metrics but does not specify if the tool is read-only, side effects, or authentication needs.
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 with two sentences, front-loading the purpose. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no parameter description, the description is incomplete. It lists statistics but lacks details on output format or parameter usage.
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?
The description does not explain the purpose of the only parameter (api_key). With 0% schema coverage, the description fails to add any meaning beyond the schema.
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 returns cache statistics (key count, hit/miss ratio, memory usage) and distinguishes itself from sibling tools that deal with data queries or schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a use case ('useful for understanding whether responses are served from cache vs live data') but does not explicitly mention when to use this tool over siblings 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, so the description alone must disclose behavioral traits. It only states the function returns examples, with no mention of authentication, rate limits, side effects, or whether it is read-only.
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 long, front-loading the core purpose in the first sentence and adding useful details in the second, with 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?
The tool has no output schema, so the description should clarify the return format. It does not specify what the output looks like (e.g., array of strings, objects with fields). Given the simple parameter set, this is a notable gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%; the dataset_id parameter has an enum and description, but api_key has no description. The description adds context about including 'realistic RWE use cases with parameter examples', which provides extra meaning, but does not document the api_key parameter.
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 returns example queries and query templates for a given dataset, with a specific verb 'Return' and resource 'sample queries'. It distinguishes from siblings like get_schema, get_data, and run_query by focusing on query examples.
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 use when seeking example queries for a dataset but does not explicitly state when to use it versus alternatives, nor does it mention any conditions 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, so description bears full burden. It only says 'Execute' with no disclosure of side effects, authentication requirements, rate limits, or whether it is read-only. The api_key parameter implies auth needs but is not mentioned in the description.
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: first sentence states core purpose, second provides context and sibling reference. No redundant or filler content. Front-loaded with action verb 'Execute'.
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, no annotations, and 3 parameters (one nested). Description fails to explain return format, error behavior, pagination details, or authentication needs. Essential context for a query execution tool is missing.
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 covers 67% of parameters with descriptions. The tool description adds no extra meaning to parameters beyond listing template categories. The api_key parameter lacks schema description, and the tool description does not address it. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states 'Execute a pre-approved named query template with parameters' and lists example template categories (HCC grouping, readmission benchmarks, etc.). It distinguishes from siblings like get_data (raw data) and get_sample_queries (listing templates).
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?
Description instructs 'Use get_sample_queries to see available templates,' providing a clear sibling reference. However, it does not explicitly state when to avoid this tool (e.g., for ad-hoc queries) or mention alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It implies a read-only operation by stating it returns schema data, but does not explicitly mention safety or side effects. The name itself suggests read-only, but the description could be more explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences. The first sentence clearly states the purpose, and the second provides usage guidance, making it efficient and well-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 the tool's simplicity (two parameters, no output schema), the description is fairly complete. It explains what is returned ('names, types, descriptions, examples') and when to use it. However, it lacks details about the api_key parameter and output format specifics.
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?
The schema has 50% coverage (dataset_id has description, api_key does not). The description does not add any parameter semantics beyond the schema. It mentions 'specific CMS dataset' but does not clarify the api_key parameter or its purpose.
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 'Return', the resource 'full field schema', and the target 'specific CMS dataset'. It also distinguishes from sibling tools by recommending use before 'get_data'.
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 explicitly says 'Use this before get_data to understand available fields and filter parameters', providing clear context for when to use the tool. However, it does not mention when not to use it or alternatives beyond 'get_data'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides useful behavioral details: caching durations (24h reference, 5min transactional) and JSON return format with data lineage metadata. However, it lacks disclosure on authentication requirements or potential side effects, though 'get_data' implies read-only.
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?
Three sentences effectively cover purpose, filters, and caching/return format. No redundant text, front-loaded with core function.
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 (many parameters, nested object, no output schema) and lack of annotations, the description misses details on constructing the filters object and the required api_key parameter. Caching info is helpful, but pagination and auth are left unclear.
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 67%, and the schema already documents many parameters well. The description adds a general statement about supported filters but no specific parameter meaning beyond what the schema provides. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves filtered records from a CMS dataset, with specific verb and resource. It distinguishes from siblings like 'run_query' by emphasizing filtering capabilities and listing filter types.
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 filtered data retrieval but does not explicitly state when to use this tool versus alternatives like 'run_query' or 'get_schema'. No 'when not to use' or sibling differentiation.
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 discloses the metadata fields returned (source, update frequency, row count, key fields, available filters), which is good. However, without annotations, it does not mention rate limits, authentication behavior (beyond the optional api_key), or any pagination/limitations. It's honest but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first lists the action and metadata, second provides usage guidance. No wasted words; all information is relevant and front-loaded.
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 listing tool with one optional parameter and no output schema, the description provides adequate context by listing the metadata fields and recommending it as a first step. It could mention if there is a maximum number of datasets returned, but it's 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 description coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema's parameter description for api_key. It does not explain when or how to use the api_key parameter.
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 'list all available CMS healthcare datasets with metadata', using a specific verb and resource. It distinguishes from siblings like get_data, get_schema, and run_query which focus on data retrieval or schema, not listing.
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?
It explicitly instructs 'Always call this first to understand what data is available', providing a clear usage context. However, it does not explicitly exclude alternatives or mention specific scenarios where this tool would be inappropriate.
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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