metadataGet a Oregon Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "tckn-sxa6".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "tckn-sxa6". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "tckn-sxa6"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value by enumerating the returned metadata fields (columns, types, row count, category, last-updated), complementing the readOnlyHint and idempotentHint annotations. It does not contradict the annotations, and no side effects or error behavior are relevant for a read-only operation.
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 example, conveying all necessary information without wasted words. It is concise 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?
For a simple one-parameter metadata lookup, the description fully covers the purpose, required input, and return contents. Since there is no output schema, the description appropriately carries the burden of explaining what the tool returns.
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 and example for resource_id. The tool description repeats the same example and parameter name, adding no new semantics beyond what the input schema already provides.
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 identifies the action ('Get') and the resource (dataset schema + metadata), and specifies the required input (resource_id). It is distinct from data querying tools by emphasizing schema and metadata, but does not explicitly name or contrast 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?
Usage is implied: you need a specific resource_id to retrieve schema and metadata. However, there is no explicit guidance on when not to use this tool or which alternatives to prefer for data rows or dataset listings.
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 tool set is a chaotic mix of unrelated domains: Oregon Open Data tools (datasets, metadata, query) are buried among dozens of tools for Pipeworx general query, Polymarket betting, memory management, and AI visibility. Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) making it impossible for an agent to distinguish the right tool for a given task without deep inspection.
Naming is wildly inconsistent: snake_case (ai_visibility_check, pipeworx_feedback), camelCase (bet_research, datasets, metadata, query), mixed (ask_pipeworx_grounded, polymarket_arbitrage). No consistent verb_noun or pattern exists, and many names are vague (remember, recall, forget) without connection to the server's assumed domain.
33 tools is excessive for a server ostensibly about Oregon Open Data, which only has 3 relevant tools. The remaining 30 are from other services (Pipeworx, Polymarket, etc.) and do not belong, making the count inappropriate for the server's declared purpose.
For the Oregon Open Data domain, the surface is bare: only search, metadata, and query. Missing operations like upload, update, or delete datasets. The heavy presence of unrelated tools (betting, memory, AI visibility) does not compensate for the gap in the actual domain coverage.