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cdc-health-mcp-server

by io.github.cyanheads

Cdc Discover Datasets

cdc_discover_datasets
Read-only

Search the CDC dataset catalog by keyword, category, or tag. Returns IDs, names, truncated descriptions, asset types, column counts, and update timestamps. The catalog also holds charts, maps, stories, files, and links; an entry whose columnCount is 0 is one of those and yields no data from the other tools. Use cdc_get_dataset_schema for the full column list of a chosen dataset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoFilter by domain tags (e.g., ["covid19", "surveillance"]). Tags widen the search instead of narrowing it — a dataset matches when it carries any one of them, so every tag added returns more results, and an unrecognized tag matches nothing and leaves the result set unchanged. Values match the catalog's own tag vocabulary, case-insensitively; the tags field on each result shows which values are in use. To narrow, combine tags with query or category, which intersect with the tag set.
limitNoResults to return (default 10, max 100). offset plus limit must not exceed 10000.
orderNoResult ordering. "dataset_id" (default) sorts deterministically by each dataset's unique catalog ID — required for stable offset pagination, since consecutive pages form a gap-free, duplicate-free traversal. "relevance" returns best-match ranking for keyword search but is not stably paginable across pages, so walking offsets can skip or repeat datasets.dataset_id
queryNoFull-text search across dataset names and descriptions (e.g., "diabetes mortality", "lead exposure children").
domainNoCDC Socrata host to search. "data.cdc.gov" (default) and "chronicdata.cdc.gov" front the same catalog and return the same entries, so switching hosts neither widens nor narrows a search — chronic-disease and small-area collections such as PLACES, the Heart Disease & Stroke Atlas, and Environmental Public Health Tracking are found from either.data.cdc.gov
offsetNoPagination offset for browsing beyond first page (max 9999). offset plus limit must not exceed 10000; both CDC portals hold well under two thousand entries, so offsets near that ceiling page past the end of the catalog.
categoryNoFilter by domain category (e.g., "NNDSS", "Vaccinations", "Behavioral Risk Factors").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
noticeNoGuidance when the page came back empty — how to broaden a search that matched nothing, where to check tag values when a tag filter was applied, or the size of the result set when the offset ran past its end.
datasetsNoMatching datasets.
totalCountNoTotal matching datasets in the catalog (for pagination).
appliedFiltersNoFilters applied to this query; absent fields indicate no filter on that dimension. Query, category, and tags intersect with each other, but multiple tags union.

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only provide readOnlyHint=true; the description adds important context about the catalog containing non-dataset assets and how to interpret columnCount=0. It also discloses that descriptions are truncated. This goes beyond the annotation without contradicting it.

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 focused sentences: the first states purpose and returns, the second clarifies the columnCount nuance, and the third points to the schema tool. Every sentence earns its place, and the key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema and 100% parameter coverage, the description provides sufficient context: it explains the return fields, the meaning of columnCount=0, and the relationship to sibling tools. The user has all necessary information to call this tool 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?

All 7 parameters have thorough schema descriptions (100% coverage). The description only paraphrases the search capability (keyword, category, tag) and adds no additional semantic detail beyond what the schema already provides. 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?

Clearly states the action (search), the resource (CDC dataset catalog), and the method (by keyword, category, or tag). It also describes what is returned and explicitly notes that entries with columnCount 0 are not datasets, distinguishing it from the data-retrieval siblings.

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 says to use cdc_get_dataset_schema for full column lists and notes that columnCount 0 entries yield no data from other tools, implying when to use this discovery tool versus the data tools. It does not explicitly mention when to use cdc_query_dataset, but the intent is clear enough.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: discovering datasets, fetching schemas, querying Socrata data, and querying WONDER data. Even though query_dataset and query_wonder both execute queries, they operate on entirely separate systems and are explicitly differentiated in the descriptions, eliminating any ambiguity.

Naming Consistency5/5

All tools follow a consistent cdc_verb_noun pattern: discover_datasets, get_dataset_schema, query_dataset, query_wonder. The naming is uniform, predictable, and clearly indicates the action and target.

Tool Count4/5

Four tools is lean but sufficient for the server's scope: three tools cover the full Socrata dataset lifecycle (discover, schema, query) and one handles the separate WONDER system. The count is slightly on the low end but not inadequate, and each tool is essential.

Completeness4/5

The tool surface covers the primary workflows for searching, inspecting, and querying CDC datasets and WONDER data. Minor gaps exist, such as no direct way to list all datasets without a search, but the provided tools handle the core use cases without dead ends.

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