metadataGet a Utah Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "7n5d-er3z".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "7n5d-er3z". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "7n5d-er3z"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable context about what the tool returns (schema + metadata including columns, types, row count, etc.), going beyond the annotations to inform the agent about the output.
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 immediately states the tool's purpose, lists the output, and provides an example. No wasted words, and the key information is 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?
Given the tool has only one parameter, no output schema, and the description fully explains what metadata is returned (columns, types, row count, category, last-updated), the description is complete enough for an agent to use this tool effectively.
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 description coverage is 100%: the only parameter, resource_id, is already described in the input schema as 'Dataset id'. The description reinforces this with an example. Since the schema already provides meaning, the description adds minimal additional semantic value.
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 a specific verb 'Get' and resource 'dataset's schema + metadata', listing the exact fields (columns, types, row count, category, last-updated) and includes a concrete example resource_id. This clearly distinguishes it from sibling tools like 'datasets' or 'query', as it's focused 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 gives an example of usage (resource_id '7n5d-er3z') but does not explicitly state when to use this tool over alternatives or provide any context on prerequisites or exclusions. While the purpose is obvious, explicit guidelines are missing.
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.
The toolset contains several near-duplicate clusters: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route the same kinds of questions, and the six polymarket tools have heavily overlapping scopes. An agent would frequently struggle to pick the right variant despite the detailed descriptions.
Names consistently use lowercase snake_case with a verb-first or domain-prefixed pattern (ask_pipeworx, resolve_entity, validate_claim, polymarket_arbitrage). Minor deviations like bare nouns (datasets, metadata) are acceptable but not perfectly uniform.
34 tools is far beyond what a Utah Open Data server needs; only 3 tools actually relate to the named domain. The rest form a sprawling general-purpose Pipeworx/prediction-market platform, making the surface feel bloated for its stated purpose.
For the actual Utah Open Data catalog, datasets/query/metadata is a complete read-only surface. But for the broader Pipeworx functionality the set actually delivers, there are odd gaps (no account management beyond subscriptions) and many irrelevant tools, so overall coverage is uneven.