datasetsSearch the Connecticut 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that it searches by keyword and returns specific fields, which aligns with read-only behavior. It provides extra context about return format and follow-up tool usage, adding value beyond annotations.
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 two sentences, front-loaded with the action and resource. Every sentence adds value: first states what it does, second explains the output and next step. 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?
Despite no output schema, the description lists the returned fields (resource_id, name, description, category, update date) and how to proceed with the results. This is sufficient for an agent to understand the tool's output and integration point.
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 each parameter described. The description adds no further meaning beyond the schema; it only mentions searching 'by keyword', which matches the query parameter. The baseline of 3 is appropriate since the schema already documents the parameters adequately.
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's action ('Search') and resource ('Connecticut Open Data catalog of open datasets'). It distinguishes itself from siblings by focusing on datasets specifically, with no confusion from similar sibling names.
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 explains what the tool returns and how to use the results (pass resource_id to query/metadata). It implicitly conveys when to use it (keyword search for datasets), but does not explicitly state when not to use it or mention alternatives, though none are apparent among siblings.
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.
Multiple tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) have overlapping research purposes. Additionally, many Polymarket and Pipeworx-specific tools are unrelated to the Connecticut Open Data server name, causing confusion.
Naming is highly inconsistent: some use snake_case (ask_pipeworx, resolve_entity), others use longer descriptive phrases (polymarket_fill_risk, scan_competitor_ai_presence), with no clear pattern.
The server claims to be about Connecticut Open Data but includes 34 tools, only 3 of which (datasets, metadata, query) are relevant. The vast majority are unrelated Pipeworx/Prediction Market tools, making the size inappropriate.
For Connecticut Open Data coverage, only basic dataset search, metadata, and query tools exist. Missing common operations like data upload, schema modification, or API key management for the open data portal.