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Search Datasets

search_datasets
Read-onlyIdempotent

Search datasets published on the U.S. Bureau of Transportation Statistics open-data portal (data.bts.gov). Returns each dataset's Socrata 4x4 id (e.g. "crem-w557"), name, and description. Use the id with dataset_columns and query_dataset. BTS covers aviation (passenger counts, on-time performance, air fares, airline financials), freight movement, transit ridership, border crossings, and transportation safety. Example queries: "aviation", "border crossing", "transit", "freight".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesFull-text search query, e.g. "aviation" or "border crossing".
limitNoMax datasets to return (default 20).
offsetNoPagination offset (default 0).

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, openWorldHint=true, and destructiveHint=false. The description adds no behavioral contradictions and confirms read-only search behavior. It adds context about the data portal and result format, which is useful but not critical given the annotations.

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 two concise sentences plus a list of topics and examples. It front-loads the core purpose. The list of topics is slightly verbose but adds valuable context. Could be slightly tighter, but overall well-structured.

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?

For a search tool with no output schema, the description fully explains what is returned (id, name, description) and provides domain context, example queries, and integration guidance. It is complete enough for an agent to use effectively without additional information.

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%; all three parameters (`q`, `limit`, `offset`) are documented in the schema. The description adds no new parameter-level details beyond schema. It adds context about the portal and id format, but that is not parameter-specific. Baseline score of 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 clearly states the tool searches datasets on a specific portal (data.bts.gov) and returns a structured result (id, name, description). It distinguishes itself from sibling tools by mentioning how the id can be used with `dataset_columns` and `query_dataset`, and gives domain coverage (aviation, freight, etc.).

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 provides explicit guidance on using the returned ids with `dataset_columns` and `query_dataset`, and gives example queries and topics. It does not explicitly state when not to use the tool (e.g., for non-BTS data), but the context is sufficiently clear for an agent.

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.