dataset_infoMetadata for a CMS dataset by datasetId: title, description, total row count, last-modified date, themes/keywords, and downloadable resources (CSV files, data dictionaries). Use after search_datasets to inspect a dataset before pulling rows.
| Name | Required | Description | Default |
|---|---|---|---|
| datasetId | Yes | Dataset UUID from search_datasets. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description's behavioral contribution is limited to listing returned metadata fields. It is consistent but adds little 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?
Two concise sentences, front-loaded with purpose, no wasted words. Every sentence adds value.
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 tool with one parameter and no output schema, the description adequately lists the metadata fields and provides usage context (post-search, pre-pull).
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?
The single parameter datasetId is 100% covered in the schema description. The tool description does not add extra meaning beyond 'Dataset UUID from search_datasets', which is already in the schema.
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 specifies the exact metadata returned (title, description, row count, etc.) and the resource type (CMS dataset), clearly distinguishing it from siblings like search_datasets (which lists datasets) and get_dataset (which retrieves rows).
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?
Explicitly states when to use ('after search_datasets') and what to do before pulling rows, providing direct context with a sibling tool.
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.
Some tools have clearly distinct purposes (remember/recall/forget, subscribe/unsubscribe), but the ask_pipeworx family overlaps heavily — ask_pipeworx_beta is explicitly identical today, and ask_pipeworx_grounded/deep_research are variations on the same routing core. Polymarket tools and comparison/profile tools also have fuzzy boundaries, though detailed descriptions help agents choose.
Most tools follow a lowercase snake_case verb_noun pattern (search_datasets, get_dataset, validate_claim, resolve_entity). A few deviate with bare verbs (remember, forget, recall) or noun-like names (dataset_info, entity_profile, pipeworx_trending), but the overall style is predictable and readable.
34 tools is a heavy surface for one server, and the scope sprawls across CMS open data, general data research, prediction markets, memory storage, and subscription management. Many of these could be split into separate coherent servers, and several meta-routers (ask_pipeworx, deep_research, discover_tools, suggest_questions) overlap in purpose.
Within the broad data-research domain the set is fairly complete: search, retrieval, grounding, comparison, entity resolution, verification, subscriptions, and memory are all covered with no obvious dead ends. However, the server is named 'Cms' yet only three tools actually touch CMS datasets, leaving that narrow purpose under-covered relative to the rest.