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Bts

by io.github.pipeworx-io

Query Dataset

query_dataset
Read-onlyIdempotent

Query rows from a data.bts.gov dataset using SoQL. datasetId is the Socrata 4x4 code from search_datasets. SoQL clauses: $select (columns / aggregates like "count(*)" or "avg(air_fare)"), $where (SQL-like filter, e.g. "date > '2024-01-01' AND state='TX'"), $order ("date desc"), $group, $q (full-text across the row). Use dataset_columns first to learn field names. Returns an array of row objects keyed by field name. Default $limit is 50 (Socrata max per page is 50000).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
$qNoFull-text search across the whole row.
$groupNoGroup-by field(s) for aggregation.
$limitNoRows to return (default 50).
$orderNoSort, e.g. "date desc".
$whereNoSQL-like filter, e.g. "date > '2024-01-01'".
$offsetNoPagination offset (default 0).
$selectNoColumns or aggregates, e.g. "date, safety_general_aviation".
datasetIdYesSocrata 4x4 id, e.g. "crem-w557".

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark it as read-only and idempotent. The description adds important behavioral details: default limit of 50, max of 50000, and return structure of row objects keyed by field name.

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 description is concise but packed with useful information. While a single paragraph, it is well-structured and front-loaded with the main purpose, with no wasted words.

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 8 parameters and SoQL complexity, the description provides sufficient context: how to get field names, pagination limits, and example queries in the schema. Output schema absence is mitigated by explaining return structure.

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 covers all 8 parameters with descriptions. The description adds value by explaining the Socrata 4x4 code, SoQL clause syntax, and the default limit, going beyond the 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?

The description clearly states the tool queries rows from a dataset using SoQL, distinguishing it from siblings like search_datasets and dataset_columns.

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?

It advises using dataset_columns first to learn field names and explains the purpose of each SoQL clause, providing clear context for when to use the tool.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes with clear descriptions, but the multiple ask_pipeworx variants and several Polymarket tools could cause initial confusion. An agent reading carefully can differentiate them, but the similarity in themes requires attention.

Naming Consistency3/5

Names use a mix of conventions: verb_noun (ask_pipeworx, compare_entities), noun_noun (entity_profile, dataset_columns), and single verbs (remember, forget). While some subgroups have internal consistency (e.g., polymarket_*), there is no overall predictable pattern.

Tool Count2/5

With 34 tools, the count exceeds the 'too many' threshold of 25. While the server is comprehensive, the large number of highly specific tools (especially for prediction markets) feels overwhelming and could confuse agents.

Completeness4/5

The tool set covers a wide range of capabilities: data querying, entity resolution, company analysis, prediction markets, memory, subscriptions, and validation. Minor gaps exist (e.g., no data writing tools), but for the read-heavy analytical purpose, it is nearly complete.

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