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Glama

Cms

by io.github.pipeworx-io

Search Datasets

search_datasets
Read-onlyIdempotent

Find CMS dataset IDs + titles by keyword. CMS publishes Medicare/Medicaid open data (provider data, spending, enrollment, drug pricing, quality measures, hospitals, nursing homes, ACOs, etc.). Searches the CMS DCAT catalog client-side over each dataset title/description/keywords. Returns the datasetId (UUID) you pass to get_dataset and dataset_info.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax datasets to return (default 25, max 100).
keywordYesSearch term, e.g. "hospital", "drug spending", "nursing home", "enrollment".

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses that search is performed 'client-side over each dataset title/description/keywords' and returns a UUID (datasetId). Adds context beyond annotations (readOnly, idempotent), 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four concise sentences, front-loaded with the core purpose. Each sentence adds value (catalog scope, search mechanism, usage guidance). No redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but description specifies return includes datasetId (UUID) and implies titles. Sufficient for an agent to understand the output shape, though exactly what fields are returned could be more explicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions. The main description adds the 'client-side' context but does not significantly enhance parameter 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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb ('Find') and resource ('CMS dataset IDs + titles by keyword'). It distinguishes from siblings like 'get_dataset' by specifying that it returns dataset IDs to pass to downstream tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tells the agent to pass the returned datasetId to 'get_dataset' and 'dataset_info', giving clear next steps. Lacks explicit when-not-to-use or comparisons with other search-like siblings, but the purpose is well-defined.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources