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Query Dataset

query_dataset
Read-only

Generic structured query for direct source_id or pack_id access using the same contract as POST /api/v1/query/dataset. Free packs: currency, distributed_manufacturing, floods, nri, owid, un_sdg, un_wpp, volcanoes, world_bank_wdi. Paid packs: earthquakes, hurricanes, tornadoes, tsunamis, wildfires, world_factbook, worldpop (x402 Base USDC).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoOptional sort instructions for row-returning queries.
limitNoMaximum number of rows to return for the requested source or pack.
outputNoOptional output controls such as response format hints.
filtersNoStructured filters including time, region_ids, and compare clauses.
metricsNoMetric ids to return. Use event_count for aggregate counts when supported.
pack_idNoPack identifier from get_catalog. Newly catalog-admitted packs require no MCP schema change.
source_idNoConcrete source id such as 'earthquakes_events', 'volcanoes_events', 'hurricanes_events', or 'un_sdg/01'.
request_idNoOptional caller-supplied request id for tracing and idempotency.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, and the description adds useful behavior beyond that: the endpoint contract, the free/paid distinction, and the x402 Base USDC cost for paid packs. It does not disclose output format or pagination, but the read-only annotation lowers the burden, so this is solid coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The first sentence states the core purpose, and the subsequent pack lists are compact and directly useful for selecting valid identifiers. The enumeration of packs and costs is slightly long but earns its place since there is no enum in the schema.

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?

For a generic query tool with 8 optional parameters, no required fields, and no output schema, the description should explain that at least one of source_id or pack_id is expected and what the response shape looks like. It relies on the external endpoint contract for that, and while the free/paid pack context is helpful, the missing operational prerequisites leave an agent to guess.

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 schema already documents each parameter. The description adds value by listing concrete pack IDs, giving example source_id values, and noting cost, which meaningfully enriches the otherwise generic schema descriptions.

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 opens with a specific verb and resource: 'Generic structured query for direct source_id or pack_id access.' It names the two query targets and ties the behavior to a concrete API contract, distinguishing it from catalog/discovery tools like get_catalog.

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 clearly implies the tool is for querying known source IDs or pack IDs and enumerates which packs are free vs paid. It does not explicitly state when not to use it or mention alternatives like get_catalog or search_disaster_links, so it falls just short of full usage guidance.

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

A4.3/5.0
Disambiguation5/5

Each tool has a clear, distinct role: catalog discovery, pack metadata, tool help, data querying, and link searching. There is no overlap between any two tools; even get_catalog and get_pack differ as list vs. details.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern: get_catalog, get_pack, get_tool_help, query_dataset, search_disaster_links. The verbs and nouns are consistently ordered, making the API predictable.

Tool Count5/5

Five tools is well-scoped for a data access facade. Each tool earns its place: discovery (catalog, pack, help), execution (query), and specialized search (links). No redundancy or bloat.

Completeness5/5

The server covers the full lifecycle of data exploration: discover available packs, inspect pack details, understand tool usage, execute queries, and search for relationships. There are no apparent dead ends—an agent can go from discovery to successful query.