cdc-health-mcp-server
Server Details
MCP server for discovering and querying CDC public health datasets via the Socrata SODA API.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- cyanheads/cdc-health-mcp-server
- GitHub Stars
- 1
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Tool Definition Quality
Average 4.4/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose in the CDC data workflow: discovery, schema inspection, and query execution. The descriptions explicitly guide usage order, eliminating any ambiguity between tools.
All three tools follow a perfect 'cdc_verb_noun' pattern with consistent snake_case naming. The verb choices (discover, get, query) accurately reflect their distinct operations.
Three tools is ideal for this focused domain—it covers the essential workflow of finding datasets, understanding their structure, and querying data without being overwhelming or insufficient.
The toolset provides complete coverage for the CDC data access domain: discovery (browse), inspection (schema), and execution (query). There are no obvious gaps for core data retrieval operations.
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 |
|---|---|---|
| datasets | Yes | Matching datasets. |
| totalCount | Yes | Total matching datasets (for pagination). |
| appliedFilters | Yes | Filters that were 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?
The annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds some behavioral context by mentioning the search functionality (by keyword, category, or tag) and the workflow role, but it doesn't disclose additional traits like rate limits, authentication needs, or error handling. With annotations covering the safety profile, the description provides moderate value without contradictions.
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 front-loaded with the core purpose and returns, followed by a clear usage guideline. Both sentences are essential, with no wasted words, making it highly efficient and well-structured for quick understanding by an AI agent.
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 the tool's complexity (a search/discovery tool with 5 parameters), high schema description coverage (100%), annotations (readOnlyHint), and the presence of an output schema, the description is complete enough. It effectively explains the tool's role in the workflow and what it returns, without needing to detail parameters or output values, as those are covered elsewhere.
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 input schema has 100% description coverage, so parameters are well-documented in the schema itself. The description adds minimal semantic value beyond the schema, as it only mentions searching 'by keyword, category, or tag' without detailing parameter interactions or constraints. This meets the baseline for high schema coverage, where the description doesn't need to compensate heavily.
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 specific action ('Search the CDC dataset catalog') and resource ('dataset catalog'), and distinguishes it from sibling tools by explicitly mentioning its role in the workflow ('Use this first to find the right dataset before querying'). It provides a comprehensive list of what it returns (dataset IDs, names, descriptions, column lists, and update timestamps), making the purpose highly specific and well-differentiated.
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 provides usage guidance by stating 'Use this first to find the right dataset before querying,' which clearly positions this tool as a discovery step before using sibling tools like 'cdc_query_dataset.' It also implies when to use it (for searching/filtering) versus when not to (for direct querying), offering clear contextual direction without being misleading.
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 annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds valuable context beyond annotations by specifying what information is fetched (e.g., row count, last-updated timestamp) and its purpose for query preparation, enhancing behavioral understanding without contradiction.
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 front-loaded with the core purpose, followed by a concise usage guideline. Both sentences are essential, with no wasted words, making it highly efficient and well-structured for quick comprehension.
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 the tool's low complexity (1 parameter), rich annotations (readOnlyHint), and the presence of an output schema, the description is complete. It adequately explains the tool's purpose and usage without needing to detail return values or behavioral nuances covered elsewhere.
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 description coverage is 100%, with the parameter fully documented in the input schema. The description does not add additional parameter details beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage.
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 specific action ('Fetch'), resource ('full column schema for a CDC dataset'), and scope ('names, data types, descriptions, row count, and last-updated timestamp'). It distinguishes from sibling tools by specifying this is for schema retrieval before querying, unlike discovery or query execution tools.
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 states when to use this tool ('Essential before writing SoQL queries against unfamiliar datasets'), providing clear context for its application. It implies an alternative (using cdc_query_dataset after schema retrieval) and references a prerequisite (obtaining datasetId from cdc_discover_datasets in the schema).
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". | |
| 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"). |
Output Schema
| Name | Required | Description |
|---|---|---|
| rows | Yes | Result rows with requested fields. Most values are strings (including numbers/dates); geo columns return GeoJSON objects. |
| query | Yes | Assembled SoQL query string sent to Socrata. |
| rowCount | Yes | Number of rows returned in this response. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, which the description does not contradict. The description adds some behavioral context beyond annotations, such as the tool's support for filtering, aggregation, sorting, full-text search, and field selection, and provides a usage tip. However, it lacks details on rate limits, authentication needs, or error handling, which would enhance transparency.
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 front-loaded with the core purpose and usage guidelines, followed by a helpful tip. Both sentences earn their place by providing essential information and practical advice without redundancy, making it efficiently structured and appropriately sized for the tool's complexity.
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 the presence of annotations (readOnlyHint), a rich input schema with 100% coverage, and an output schema (implied by context signals), the description is complete enough. It covers purpose, usage guidelines, and includes a practical tip, addressing the tool's role in the context of its siblings without needing to explain return values or parameter details already handled by structured data.
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?
With schema description coverage at 100%, the input schema already documents all parameters thoroughly. The description does not add significant meaning beyond the schema, as it only mentions general capabilities (e.g., 'Supports filtering, aggregation, sorting') without detailing parameter-specific semantics. The tip provides a practical example but does not compensate for the high schema coverage, aligning with the baseline score.
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 specific action ('Execute a SoQL query against any CDC dataset') and resource ('CDC dataset'), distinguishing it from siblings by mentioning their complementary purposes (finding dataset IDs and inspecting columns). It explicitly names the sibling tools, making the differentiation unambiguous.
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 provides explicit guidance on when to use this tool ('Execute a SoQL query') and when to use alternatives, naming the sibling tools ('cdc_discover_datasets to find dataset IDs and cdc_get_dataset_schema to inspect columns before querying'). It also includes a tip for a specific use case (enumerating column values), offering practical advice.
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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