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Glama

Cdc

by io.github.pipeworx-io

Get Dataset

get_dataset
Read-onlyIdempotent

Fetch up to 1000 rows from a CDC Socrata dataset by its four-by-four dataset ID (e.g., "9mfq-cb36"). Returns row array with all columns plus column names extracted from the first row. Use search_datasets first to find the dataset ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesSocrata dataset ID in four-by-four format (e.g., "9mfq-cb36")
limitNoNumber of rows to return (default 50, max 1000)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesData rows with all columns as key-value pairs
columnsYesColumn names from the dataset
row_countYesNumber of rows returned
dataset_idYesSocrata dataset ID

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already provide readOnly, openWorld, idempotent, non-destructive hints. Description adds specifics: up to 1000 rows, return format with row array and column names from first row, and limit defaults.

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, no filler. Essential information front-loaded: main action then prerequisite. Efficient and clear.

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 full schema coverage, annotations, and presence of output schema, the description covers all necessary context: what the tool does, how to identify the dataset, and row limit. No gaps.

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 already describes both parameters (100% coverage). Description adds context: ID format example ('9mfq-cb36') and that first row yields column names, enhancing understanding beyond schema.

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 'Fetch up to 1000 rows from a CDC Socrata dataset by its four-by-four dataset ID', specifying verb, resource, and identifier format. Distinguishes from sibling search_datasets by mentioning it as a prerequisite.

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

Usage Guidelines5/5

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

Explicitly instructs to use search_datasets first to find the dataset ID, providing clear when-to-use guidance. Also mentions default and maximum limit for rows.

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

A3.9/5.0
Disambiguation3/5

Tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research serve similar querying purposes, and there is overlap among prediction market tools (e.g., polymarket_arbitrage, polymarket_edges). However, descriptions help differentiate them, so agents can usually select the correct one.

Naming Consistency3/5

Most tools use snake_case with verbs (ask_, resolve_, validate_), but there are noun-style exceptions (entity_profile, recent_changes, pipeworx_trending) and mixed naming among Polymarket tools. The pattern is readable but not fully consistent.

Tool Count2/5

With 32 tools, the server feels bloated, especially given the name 'Cdc' implies a focus on CDC data, yet many tools cover unrelated domains like prediction markets and company profiles. Several tools could be consolidated or removed to align with a narrower scope.

Completeness3/5

The CDC domain is thinly covered with only search and get for datasets, lacking upload or advanced filtering. Company financials are limited to basic fundamentals from 10-Ks. Prediction markets are well-covered with arbitrage, edges, and fill risk. The server has notable gaps in its core domain.

Resources