metadataGet a Connecticut Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "28fr-iqnx".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "28fr-iqnx". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide safety traits (readOnly, idempotent, non-destructive). Description adds specific behavioral context: what metadata is returned (columns, types, row count, etc.), which is valuable beyond annotations. No contradictions.
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?
Single sentence that front-loads the action and resource, with no extraneous information. Every word is necessary.
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 tool with no output schema, the description fully explains what the tool does and what it returns, making it complete for agent decision-making.
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 baseline is 3. Description reinforces the parameter meaning and provides an example, adding clarity beyond the schema description.
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?
Description uses specific verb 'Get' with clear resource 'Connecticut Open Data dataset's schema + metadata' and lists returned fields (columns, types, row count, category, last-updated). It distinguishes from siblings like 'datasets' and 'query' by focusing on schema/metadata retrieval.
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?
Description provides clear context for use: when needing schema/metadata of a dataset by resource_id. It does not explicitly state when not to use or name alternatives, but the context is sufficient given sibling tool names.
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.