metadataGet a Providence Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "vank-fyx9".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "vank-fyx9". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, idempotentHint, and destructiveHint, indicating a safe, idempotent read operation. The description adds value beyond these by specifying the exact output details (columns, types, row count, category, last-updated), giving the agent a precise expectation of what data will be returned without needing to invoke the tool. This is useful behavioral context.
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?
The description is a single, well-structured sentence that front-loads the key action and resource. Every word is informative, including the example and the list of metadata attributes. There is no redundancy or irrelevant information, making it efficient and easy to parse.
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?
Given the tool's simplicity (one required parameter, no output schema), the description is fully complete. It explains what the tool does, what the input is (with an example), and what the output contains. There are no gaps, as the annotations cover safety and idempotency. The description alone is sufficient for an agent to understand and use the tool correctly.
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 has 100% parameter description coverage, with a clear description for 'resource_id'. The description provides an example ('vank-fyx9'), which adds context but does not introduce meaning beyond the schema. Since the schema already documents the parameter adequately, the description's contribution is marginal, warranting a baseline score of 3.
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 the tool retrieves schema and metadata for a Providence Open Data dataset, using the specific verb 'Get' and naming the resource. It lists specific attributes (columns, types, row count, etc.) and provides an example resource_id, leaving no ambiguity about its function. It also differentiates from siblings like 'datasets' by focusing on metadata retrieval.
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?
The description provides clear context and an example, but does not explicitly state when to use this tool versus alternatives. It does not offer exclusion criteria or mention other tools that might be more appropriate for similar tasks, such as 'datasets' tool. This lack of comparative guidance limits its utility for tool selection.
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, but several clusters overlap: ask_pipeworx vs ask_pipeworx_beta are currently functionally identical, polymarket_arbitrage / polymarket_edges / polymarket_kalshi_spread all hunt mispricings via different mechanisms, ai_visibility_check is wrapped by scan_competitor_ai_presence, and discover_tools vs suggest_questions both serve tool discovery. The rich descriptions mitigate but do not eliminate misselection risk.
Names are all snake_case and follow recognizable conventions: verb_noun for actions (compare_entities, resolve_entity, validate_claim), domain-prefixed families (polymarket_*, pipeworx_*, recent_*, ask_pipeworx_*), and a few bare verbs (remember, recall, query). Minor deviations like bet_research (noun_verb) and noun-only names (datasets, metadata) break the pattern, but the overall scheme is predictable.
At 34 tools, this exceeds the 25+ threshold for 'too many' and bundles several distinct domains — general data querying, prediction markets, AI visibility, memory, subscriptions, open data, and npm auditing — into one server. The breadth is defensible for a data platform, but the agent-facing surface is sprawling and would benefit from splitting into focused servers.
The core data workflow is well covered: discover (discover_tools, suggest_questions), resolve (resolve_entity), query (ask_pipeworx), ground (ask_pipeworx_grounded, validate_claim), research (deep_research), compare (compare_entities), profile (entity_profile), and changes (recent_changes). Prediction markets, memory, and subscriptions each have full lifecycles. The main gap is no tool for fetching returned pipeworx:// citation URIs directly, plus a few soft-failing sources.