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CDC — Chronic Disease Trend Over Years

cdc_chronic.disease.trend
Read-onlyIdempotent

Retrieve multi-year trend data for a specific chronic disease indicator at the national or state level. Returns annual values with confidence intervals so you can track how a metric changes over time (e.g. declining smoking rates, rising obesity, improving diabetes control). Filter by question ID, location (state abbreviation or "US" for national), demographic subgroup, and value type (crude prevalence, age-adjusted prevalence, rate per 100K, number). Data from ~2010 to 2023. Source: CDC CDI Socrata dataset hksd-2xuw. No auth — US Gov public domain.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of annual data points to return (1–50, default 20). Returns most recent years first.
locationNoUS state abbreviation (e.g. "CA", "TX", "FL") or "US" for national trend. Defaults to "US" for national-level data.
question_idNoQuestion ID to retrieve trend data for (e.g. "TOB04" for cigarette smoking, "DIA01" for diabetes, "CVD01" for high blood pressure, "CAN01" for cancer screening). Use cdc_chronic.topics to find valid IDs. Defaults to "TOB04".
value_type_idNoData value type to filter on. CRDPREV=Crude Prevalence (%, unadjusted), AGEADJPREV=Age-adjusted Prevalence (%, adjusted), NMBR=Number (absolute count), CRDRATE=Crude Rate (per 100K), AGEADJRATE=Age-adjusted Rate (per 100K), PCT=Percentage.
stratification_idNoDemographic subgroup for trend. OVR=Overall, SEXF=Female, SEXM=Male, BLK=Black non-Hispanic, HIS=Hispanic, etc. Omit for all subgroups.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context beyond that: it specifies the data source (CDC CDI Socrata dataset hskd-2xuw), the time range (~2010 to 2023), and authentication requirements ('No auth — US Gov public domain'). It also states that returns include confidence intervals, which is a behavioral trait not in annotations. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph that front-loads the core purpose and immediately states what data is returned. Every sentence adds value: purpose, filter dimensions, data source, time range, and auth status. There is no redundant repetition of schema content. It is appropriately sized for a tool with this complexity.

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 tool has an output schema (so return values need not be detailed), annotations covering safety, and a description that covers the data source, time range, filtering options, and examples, the definition is complete. An agent has enough information to decide when to use it and how to call it correctly, including valid question IDs and value type semantics. No critical gaps are apparent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 meaning beyond the schema by providing concrete examples (e.g., 'TOB04' for cigarette smoking, 'DIA01' for diabetes) and paraphrasing value types in plain language ('crude prevalence, age-adjusted prevalence, rate per 100K, number'). It also clarifies that location uses state abbreviation or 'US' for national, which mirrors but reinforces the schema. This extra guidance helps an agent select parameters correctly.

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's purpose: 'Retrieve multi-year trend data for a specific chronic disease indicator at the national or state level.' It specifies the resource (chronic disease indicator), the action (retrieve trend data), and the temporal dimension (multi-year). The mention of returning annual values with confidence intervals and concrete examples (smoking, obesity, diabetes) further clarifies what the tool does and distinguishes it from siblings like cdc_chronic.disease.state_compare or .topics.

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 context for when to use this tool: when you need trend data over years for a chronic disease indicator. It also gives a pointer to a sibling tool: 'Use cdc_chronic.topics to find valid IDs,' which is a helpful prerequisite. However, it does not explicitly state when not to use this tool or mention alternatives like state_compare for state-level comparisons, so it stops short of an explicit when/when-not statement.

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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