metadataGet a Vermont Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "jgqy-2smf".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "jgqy-2smf". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "jgqy-2smf"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, providing a strong safety profile. The description adds value beyond these by detailing the specific metadata returned (columns, types, row count, etc.), which informs the agent about the output structure. There is no contradiction.
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, front-loaded sentence with an inline example. Every part is functional: verb, resource, specific elements, and example. No unnecessary words.
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?
Without an output schema, the description fully covers the return information: schema + metadata including columns, types, row count, category, and last-updated. It also specifies the resource_id format, making the tool's usage complete for an AI agent.
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 coverage is 100%, with a clear description of the resource_id parameter. The description adds an example and context (e.g., Vermont Open Data), but since the schema already explains the parameter adequately, the description does not significantly augment semantics beyond baseline.
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 uses the verb 'Get' and specifies the resource as 'Vermont Open Data dataset's schema + metadata', listing concrete elements like columns, types, row count, category, and last-updated. It provides an example resource_id, making the purpose clear and distinct from sibling tools (e.g., datasets likely lists datasets, while metadata retrieves details for one).
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 implies usage when schema or metadata of a specific dataset is needed, but does not explicitly state when to use this tool over alternatives or when not to use it. No exclusion or sibling comparison is provided, though the context is clear enough for an AI agent.
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 clearly distinct purposes, but the ask_pipeworx trio (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the polymarket cluster (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) could cause confusion without careful reading. However, the detailed descriptions effectively differentiate each tool's specific role.
All tool names follow consistent snake_case convention with a verb-noun pattern (e.g., compare_entities, recent_changes, resolve_entity). There are no mixed conventions or chaotic naming, making the set predictable and easy to navigate.
At 34 tools, the set is extensive but justified by the server's broad scope covering Vermont open data, Pipeworx data platform, prediction markets, and utility features. Each tool earns its place, though the count is slightly high compared to typical servers.
The tool surface covers data retrieval, entity analysis, prediction markets, memory, subscriptions, feedback, and claim validation. Minor gaps exist (e.g., no direct compliance tools), but the set is comprehensive for its stated purpose of data integration and analysis.