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cdc-health-mcp-server

by io.github.cyanheads

Cdc Get Dataset Schema

cdc_get_dataset_schema
Read-only

Fetch the column schema for a CDC dataset — names, data types, descriptions, row count, and last-updated timestamp. Returns the first 100 columns by default; wide datasets continue via column_offset. Get dataset IDs from cdc_discover_datasets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoCDC Socrata host to fetch the dataset from. "data.cdc.gov" (default) and "chronicdata.cdc.gov" front the same catalog, so a four-by-four ID resolves on either and the default works whichever host the dataset was found on.data.cdc.gov
datasetIdYesFour-by-four dataset identifier (e.g., "bi63-dtpu"). Obtain from cdc_discover_datasets.
column_limitNoColumns to return in this call (default 100, max 500). Every dataset under the default arrives whole; past it the response reports totalCount and a nextOffset to pass back as column_offset. Raise this to pull a wide schema in one call.
column_offsetNoIndex of the first column to return, for continuing past a previous call (default 0). Columns keep the order the dataset declares, so column_offset plus column_limit walks the schema without gaps or repeats. An offset at or past the column count returns an empty window rather than an error.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
capNoThe column_limit that bounded this response.
nameNoDataset display name from the catalog (e.g., "Provisional COVID-19 Deaths by Sex and Age").
errorNoPresent when the call failed. Absent on success.
shownNoNumber of columns returned in this response.
noticeNoGuidance when the response is a subset of the schema — which columns it covers, how to reach the rest, or that column_offset ran past the end.
columnsNoThe requested window of dataset columns, with full types and descriptions. Bounded by column_limit/column_offset; the enrichment fields say how the window sits in the whole schema.
rowCountNoTotal number of rows when reported by upstream; omitted when unknown.
truncatedNoTrue when the returned columns are a subset of the schema. Absent means every column of the dataset is in this response.
updatedAtNoLast data update timestamp when provided.
nextOffsetNoValue to pass as column_offset on the next call to continue after the last column returned. Present only when columns remain beyond this window.
totalCountNoTotal columns in the dataset schema, before column_limit/column_offset.
descriptionNoDataset description when provided.

TDQS

A4.3/5.0
Behavior4/5

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

Even with readOnlyHint=true, the description discloses useful behavior beyond the annotation: it returns the first 100 columns by default and supports continuation via column_offset. It also mentions row count and timestamp details. It does not go into edge-case behavior, but the annotation already covers the safe, read-only nature.

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 tight sentences with no wasted words. The what (fetch schema) is front-loaded, supported by return contents, then the pagination caveat and ID source are given in a logical order.

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?

For a read-only, single-resource metadata tool, the description plus richly annotated schema covers what the tool does, how to paginate, where to get the required datasetId, and what the response includes. The output schema exists as well, so nothing is missing for correct invocation.

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 the baseline is 3. The description adds a little cross-parameter context by connecting pagination to wide datasets, but the individual parameter descriptions already thoroughly explain defaults, offsets, and limits, so the description itself adds limited new 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 states a specific verb and resource: it fetches the column schema for a CDC dataset, and enumerates exactly what is returned (names, data types, descriptions, row count, last-updated timestamp). This clearly differentiates it from the sibling tools, especially cdc_query_dataset, which is about querying data rather than metadata.

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 provides clear usage context by saying dataset IDs come from cdc_discover_datasets, which establishes the correct workflow. It does not explicitly name cdc_query_dataset as the alternative when row-level data is needed, but the schema-fetching purpose makes the choice reasonably unambiguous.

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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