dataset_columnsList the columns of a data.bts.gov dataset: field name (used in SoQL $select/$where), human label, and data type (number, text, calendar_date, etc.). Call this before query_dataset so you know which fields exist. datasetId is the Socrata 4x4 code from search_datasets.
| Name | Required | Description | Default |
|---|---|---|---|
| datasetId | Yes | Socrata 4x4 id, e.g. "crem-w557". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare safe read-only behavior. Description adds context about output content (field name, label, type) and intended sequence, enriching transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences: first states purpose and output, second gives usage context. No redundant words, front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, description adequately explains output fields and purpose. Could mention potential limits but acceptable for a simple listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already describes datasetId, but description adds value by specifying it's the Socrata 4x4 code from search_datasets, aiding correct parameter retrieval.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists columns of a dataset, specifying returned information (field name, human label, data type). It also distinguishes itself from siblings by advising to call this before query_dataset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises to call this tool before query_dataset to know available fields, providing clear usage context. Does not mention exclusions but gives concrete guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.