metadataGet a Washington State Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "qxh8-f4bd".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "qxh8-f4bd". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "qxh8-f4bd"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value beyond the annotations by detailing the return content (columns, types, row count, category, last-updated). The annotations already indicate a safe, idempotent read operation, and the description complements this without 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, well-structured sentence that immediately states the tool's purpose and details. Every part of the description earns its place, with no wasted 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?
Given the tool's simplicity (1 parameter, no output schema), the description adequately covers what the tool does and returns. It could optionally mention if the metadata is always current or any limits, but it is sufficiently complete for an agent to understand its functionality.
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% description coverage for the only parameter, and the description merely repeats the example. No additional semantics or constraints are provided beyond what the schema already offers, so a baseline score of 3 is appropriate.
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 clearly identifies the resource ('Washington State Open Data dataset's schema + metadata'). It lists the exact fields returned (columns, types, row count, category, last-updated) and provides an example resource_id, making the purpose unmistakable and distinct from sibling tools.
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 a clear example of the required parameter and implies when to use (retrieving dataset schema/metadata). However, it does not explicitly state when not to use or mention alternative tools for similar purposes, but the specificity of 'Washington State Open Data' narrows the context sufficiently.
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 detailed descriptions, but there is overlap between ask_pipeworx, ask_pipeworx_grounded, and deep_research, all serving data retrieval. Also, prediction market tools (e.g., bet_research, polymarket_arbitrage) are closely related, causing potential ambiguity.
Naming conventions are inconsistent: mostly snake_case (ask_pipeworx, entity_profile) but includes camelCase (ai_visibility_check). No clear pattern, mixing verb_noun and other structures.
33 tools is excessive for a server named 'Washington State Open Data'; only 3 tools (datasets, metadata, query) are directly relevant, while the rest are unrelated Pipeworx/Polymarket tools. The count does not match the server's stated scope.
The tool set severely misaligns with the server name: it covers general data and prediction markets rather than Washington State Open Data. Only basic query and metadata tools exist for the stated domain, leaving many common dataset operations (e.g., CRUD) missing and providing entirely irrelevant functionality.