metadataGet a US DOT Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "4zfz-amsd".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "4zfz-amsd". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a safe read-only, idempotent operation. The description adds value by detailing the return fields (columns, types, row count, category, last-updated), providing concrete behavioral expectations beyond 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, front-loaded sentence that clearly states the action, resource, and return items. No wasted words; ideal for quick agent comprehension.
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?
For a simple tool with one parameter, no output schema, and strong annotations, the description is sufficiently complete. It lists the key metadata fields returned, though the exact structure is not detailed. Still adequate for agent invocation.
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?
The input schema already fully documents the single parameter 'resource_id' with an example. The description reiterates the parameter but does not add significant new semantic meaning beyond what the schema provides. Meets baseline for 100% schema coverage.
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?
Clearly states it retrieves schema and metadata (columns, types, row count, category, last-updated) for a US DOT Open Data dataset identified by resource_id. The verb 'Get' and specific resource make the purpose unambiguous and distinguishable from sibling tools like 'query' or 'datasets'.
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?
Implied usage for exploring dataset structure before querying, but no explicit guidance on when to use this tool over alternatives. No mention of prerequisites or exclusions. Adequate but could be improved with usage hints.
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.
Several tools have overlapping or near-identical purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are one base query flow with different flavors, while entity_profile, compare_entities, and recent_changes all overlap on company information. The Polymarket family and the discovery/onboarding tools (discover_tools, suggest_questions, pipeworx_trending) also create boundary confusion despite good descriptions.
The naming is consistently snake_case and mostly readable, but it mixes verb_noun tools (compare_entities, resolve_entity, validate_claim) with noun-phrase tools (entity_profile, polymarket_arbitrage, pipeworx_trending, datasets, metadata). The ask_pipeworx variants use a beta/grounded suffix pattern that is not applied uniformly across the other tool families.
34 tools is well above the 25+ threshold and feels like several servers merged into one: US DOT catalog access, a generic Pipeworx data router, prediction-market tools, memory/subscription management, AI-visibility checks, and meta/discovery utilities. Many tools could be consolidated (the three ask_pipeworx variants, the five Polymarket tools, and the discovery trio).
The core data lifecycle is broadly covered: catalog search, metadata, querying, natural-language lookup, grounded verification, entity resolution, entity profiles, comparison, research, subscriptions, and memory all have working paths. However, the server is named around US DOT data but only datasets/metadata/query are DOT-specific, and the rest is an unrelated general-purpose data and prediction-market toolkit, leaving the stated domain feeling incomplete.