datasetsSearch the Texas 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 read-only, idempotent, non-destructive behavior. The description adds value by specifying the catalog source and return format, which are not in annotations. No contradictions. For a read-only search tool, this is sufficient transparency.
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?
Two sentences, front-loaded with action, no extraneous words. Every sentence serves a purpose: stating the function and summarizing output usage.
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 no output schema, the description explains return fields and the next step (pass resource_id to query/metadata). It could mention pagination behavior, but offset is defined in the schema. For a search tool with strong annotations, this is fairly complete.
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 clear parameter descriptions. The tool description confirms the query is a keyword search and mentions defaults, but does not add significant new meaning beyond the schema. 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 states the tool searches the Texas Open Data catalog by keyword, specifies returned fields (resource_id, name, description, category, update date), and explains how to use the result. This is a specific verb-resource pair that distinguishes it from siblings like 'query' or 'search_within'.
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 tells when to use the tool (keyword search for datasets) and hints at the next step (pass resource_id to query/metadata), but does not explicitly state when not to use it or compare to alternatives. However, the sibling list includes many unrelated tools, so the differentiation is implicit.
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.
Tools are individually well-described, but there is overlapping functionality among ask_pipeworx, ask_pipeworx_grounded, and deep_research, as well as multiple prediction market tools. The detailed descriptions help, but the number of similar tools creates some ambiguity.
Tool names follow a consistent snake_case pattern with descriptive prefixes (e.g., ask_pipeworx, entity_profile, polymarket_edges). There are minor deviations like 'deep_research' vs 'bet_research' but overall the naming is predictable and clear.
The server is named 'Texas Open Data' but only 3 of 33 tools (datasets, metadata, query) are directly related to Texas open data. The remaining tools cover a much broader domain (Pipeworx ecosystem), making the tool count inappropriate for the stated purpose.
For the stated purpose of Texas Open Data, the tool surface is incomplete—only basic query and metadata capabilities are provided, lacking data management, update, or delete operations. As a general pipeworx server it might be more complete, but the name suggests Texas-specific data.