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LiveDataLink

by io.github.blackboxfoundry

cdc_dataset_query

Read-onlyIdempotent

Generic SoQL query against any data.cdc.gov dataset. Use this when none of the curated tools fit. Accepts a 4x4 Socrata ID and a where-clause. SoQL reference: https://dev.socrata.com/docs/queries/

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows (default 50)
orderNoSoQL order clause (e.g. 'date DESC')
whereNoSoQL where clause (e.g. "state='Texas' AND year=2024")
selectNoSoQL select clause (default '*')
datasetYesSocrata 4x4 dataset ID (e.g. 'muzy-jte6')

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds context that this is an arbitrary SoQL query against any CDC dataset, but does not mention response shape, pagination, or rate-limit behavior. With annotations carrying the safety burden, this is adequate but not exemplary.

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 sentences with no wasted words: the generic scope, the usage rule, and the required input format. The SoQL reference link is useful and placed at the end without disrupting the core guidance.

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

Completeness4/5

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

Given the rich schema coverage, strong annotations, and the inherently flexible nature of a generic query tool, the description is largely complete. The SoQL reference helps fill the gap left by no output schema, though it does not describe expected return format or mention how to discover valid dataset IDs.

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 fully documents all five parameters. The description reinforces that dataset is a 4x4 Socrata ID and mentions where-clause support, but adds no meaning beyond the schema's parameter descriptions. Baseline 3 is appropriate.

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: 'Generic SoQL query against any data.cdc.gov dataset.' It also distinguishes itself from siblings by positioning itself as the fallback when 'none of the curated tools fit,' making its role in the tool hierarchy clear.

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 explicitly says to use it when none of the curated tools fit, which is clear contextual guidance. It does not enumerate specific alternative tools or spell out when not to use it, but the generic-vs-curated distinction is enough for an agent to route correctly.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.

Resources