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lzinga

US Government Open Data MCP

cdc_query

Read-only

Query CDC public health datasets including mortality, life expectancy, COVID, and birth indicators. Use SODA syntax to filter, select, and sort data for custom analysis.

Instructions

Custom query against any CDC dataset using SODA syntax. Datasets: bi63-dtpu (death 1999–2017), w9j2-ggv5 (life expectancy), 489q-934x (mortality rates), swc5-untb (PLACES county), dxpw-cm5u (PLACES city), pwn4-m3yp (COVID), r8kw-7aab (weekly deaths), s2qv-b27b (disability), xbxb-epbu (drug overdose), hn4x-zwk7 (nutrition/obesity), 6rkc-nb2q (historical death rates), 76vv-a7x8 (birth indicators)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesDataset ID, e.g. 'bi63-dtpu'
whereNoSODA $where clause: "year = '2021' AND state = 'New York'"
selectNoSODA $select: 'year, state, deaths'
orderNoSODA $order: 'year DESC'
groupNoSODA $group: 'year'
limitNoMax rows (default 1000)
Behavior2/5

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

The description does not disclose behavioral traits beyond annotations (readOnlyHint=true). It lacks details on response format, pagination, or rate limits, which would be valuable for a query tool.

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?

Two sentences: first states purpose and syntax, second lists datasets. Front-loaded and efficient with no wasted words.

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

Completeness3/5

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

No output schema, so description could hint at return format but does not. Dataset list is helpful, but the tool's complexity (6 params, custom queries) warrants more context.

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 schema already explains each parameter. The description adds the dataset list but no further parameter detail beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool performs custom queries using SODA syntax on CDC datasets and lists specific ones. However, it does not explicitly distinguish itself from sibling tools that focus on individual datasets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus the many CDC-specific sibling tools. It does not mention that dedicated tools may be simpler for common lookups.

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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