datasetsSearch the Oregon Open Data catalog of open datasets by keyword. Returns each dataset's resource_id, name, description, category and update date — pass the resource_id to query/metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword to search dataset titles/descriptions (e.g. "budget", "crime", "health"). | |
| offset | No | Pagination offset. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "budget"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "crime"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds value by disclosing the exact return fields and the relationship to subsequent tools (resource_id needed for query/metadata). It does not repeat annotation info or contradict it.
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 action and resource, then lists return fields and provides a clear next-step instruction. Every clause earns its place with no redundancy or filler.
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 rich schema (100% parameter coverage) and annotations, the description is complete enough: it explains the tool's purpose, return fields, and how to proceed. It lacks only minor details like total count or pagination behavior, but these are not critical for a simple search tool with offset/limit in the schema.
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%, so the schema fully documents query, limit, and offset. The description adds no additional parameter semantics beyond what the schema already provides. It mentions 'keyword search' but that is already in the query parameter description. Thus, baseline 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 clearly uses a specific verb ('Search') and resource ('Oregon Open Data catalog'), and specifies the return fields (resource_id, name, description, category, update date). It also distinguishes itself from sibling tools like metadata/query by instructing the user to pass the resource_id to those tools, making its role as the discovery step explicit.
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: it is for keyword searching of datasets. It implicitly differentiates from alternatives by telling the agent to pass the resource_id to query/metadata, implying this tool is the correct starting point. However, it does not explicitly state when not to use it or name alternatives beyond the passing instruction.
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.