cdc-health-mcp-server
Server Details
Search and query CDC public health data — mortality, vaccinations, surveillance, behavioral risk.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- cyanheads/cdc-health-mcp-server
- GitHub Stars
- 3
- Server Listing
- cdc-health-mcp-server
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Tool Definition Quality
Average 4.3/5 across 3 of 3 tools scored.
Each tool serves a distinct purpose: discovering datasets, retrieving schema, and querying data. There is no overlap or ambiguity in their roles.
All tool names follow a consistent 'cdc_verb_noun' pattern (cdc_discover_datasets, cdc_get_dataset_schema, cdc_query_dataset), making them predictable and easy to understand.
Three tools is an appropriate count for a focused data exploration server, covering discovery, schema inspection, and querying without unnecessary extras.
The toolset covers the essential workflow (find dataset, get schema, query data), but is missing advanced filtering or metadata browsing capabilities, which are minor gaps.
Available Tools
3 toolscdc_discover_datasetsCdc Discover DatasetsARead-onlyInspect
Search the CDC dataset catalog by keyword, category, or tag. Returns dataset IDs, names, descriptions, column lists, and update timestamps.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Filter by domain tags (e.g., ["covid19", "surveillance"]). | |
| limit | No | Results to return (default 10, max 100). | |
| query | No | Full-text search across dataset names and descriptions (e.g., "diabetes mortality", "lead exposure children"). | |
| offset | No | Pagination offset for browsing beyond first page (max 9999). | |
| category | No | Filter by domain category (e.g., "NNDSS", "Vaccinations", "Behavioral Risk Factors"). |
Output Schema
| Name | Required | Description |
|---|---|---|
| notice | No | Guidance when no datasets matched — echoes the applied filters and suggests how to broaden the search. |
| datasets | Yes | Matching datasets. |
| totalCount | Yes | Total matching datasets in the catalog (for pagination). |
| appliedFilters | Yes | Filters applied to this query; absent fields indicate no filter on that dimension. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations confirm readOnlyHint=true; description adds return value details. No extra behavioral traits (e.g., pagination details) beyond schema, but sufficient given annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with clear structure: action, search dimensions, return fields. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers core purpose and return fields. Pagination via offset/limit is in schema. Could mention combining parameters, but output schema exists and 5 params are well-documented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter descriptions. Description summarizes search by keyword/tag/category but does not add new semantics beyond schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches the CDC dataset catalog by keyword, category, or tag, and lists returned fields (IDs, names, descriptions, column lists, timestamps). It is distinct from siblings which get schema or query data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly clear from sibling context: use this to discover datasets before drilling into schema or data. Could be more explicit about when not to use (e.g., for known datasets), but adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
cdc_get_dataset_schemaCdc Get Dataset SchemaARead-onlyInspect
Fetch the full column schema for a CDC dataset — names, data types, descriptions, row count, and last-updated timestamp. Get dataset IDs from cdc_discover_datasets.
| Name | Required | Description | Default |
|---|---|---|---|
| datasetId | Yes | Four-by-four dataset identifier (e.g., "bi63-dtpu"). Obtain from cdc_discover_datasets. |
Output Schema
| Name | Required | Description |
|---|---|---|
| name | Yes | Dataset display name from the catalog (e.g., "Provisional COVID-19 Deaths by Sex and Age"). |
| columns | Yes | Dataset columns with types and descriptions. |
| rowCount | No | Total number of rows when reported by upstream; omitted when unknown. |
| updatedAt | No | Last data update timestamp when provided. |
| description | No | Dataset description when provided. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description aligns with the readOnlyHint annotation and adds detailed behavioral context, such as the schema elements returned (names, data types, descriptions, row count, timestamp). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that states the purpose and a brief directive, with no wasted words. It is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the description does not need to explain return values. It covers the essential purpose and prerequisite, making it complete for this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'datasetId' is well-documented in the schema with pattern and description. The main description adds minimal extra meaning beyond referencing cdc_discover_datasets, but schema coverage is 100%, so the baseline is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the verb 'Fetch' and the resource 'full column schema for a CDC dataset,' listing specific elements like names, data types, and row count. This distinguishes it from sibling tools like cdc_discover_datasets and cdc_query_dataset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells users to obtain dataset IDs from cdc_discover_datasets, providing clear context for when to use this tool. However, it does not explicitly state when not to use it, but the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
cdc_query_datasetCdc Query DatasetARead-onlyInspect
Execute a SoQL query against any CDC dataset. Supports filtering, aggregation, sorting, full-text search, and field selection. Use cdc_discover_datasets to find dataset IDs and cdc_get_dataset_schema to inspect columns before querying.
| Name | Required | Description | Default |
|---|---|---|---|
| group | No | SoQL GROUP BY clause. Requires aggregate functions in select. | |
| limit | No | Max rows to return (default 100, max 5000). | |
| order | No | SoQL ORDER BY clause. Field name with optional ASC/DESC: "total_deaths DESC". | |
| where | No | SoQL WHERE clause. Strings must be single-quoted: "state='California' AND year=2020". If a column name matches a SoQL keyword (group, select, where, order, limit, offset, having, search), wrap it in backticks: "`group`='By Year'". | |
| having | No | SoQL HAVING clause. Filters aggregated results. | |
| offset | No | Row offset for pagination. | |
| search | No | Full-text search across all text columns. For precise filtering use the where parameter instead. | |
| select | No | SoQL SELECT clause — column names, aliases, or aggregates: "state, sum(deaths) as total_deaths". Omit for all columns. To enumerate distinct values of a column, set select to "{column}, count(*) as count" with group="{column}" and order="count DESC". | |
| datasetId | Yes | Four-by-four dataset identifier (e.g., "bi63-dtpu"). Obtain from cdc_discover_datasets. |
Output Schema
| Name | Required | Description |
|---|---|---|
| cap | No | The requested limit that bounded this response. |
| rows | Yes | Result rows with requested fields. Most values are strings (including numbers/dates); geo columns return GeoJSON objects. |
| shown | No | Number of rows returned in this response. |
| notice | No | Guidance when no rows matched or results were truncated — how to verify filters, paginate, or broaden the query. |
| rowCount | Yes | Number of rows returned in this response. |
| truncated | No | True when the result row count hit the requested limit and may be incomplete. |
| effectiveQuery | Yes | Assembled SoQL query string sent to the Socrata API. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint: true, which the description does not contradict. The description adds behavioral context through parameter examples (e.g., SoQL keyword escaping) but does not elaborate on safety or side effects beyond what annotations already convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the core purpose. Every sentence adds value, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters, an output schema, and sibling tools, the description is complete. It covers the query workflow, provides parameter examples, and references necessary upstream tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are already documented. The description adds value with examples (e.g., select parameter for distinct values, where parameter with backticks) and clarifies the intent behind parameters like search vs. where.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool executes SoQL queries against CDC datasets. It uses specific verbs ('Execute', 'Supports') and distinguishes from siblings by referencing cdc_discover_datasets and cdc_get_dataset_schema as prerequisites.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions using sibling tools for discovery and schema inspection, implying when to use this tool. It lists supported operations (filtering, aggregation, etc.) but does not explicitly state 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.
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