dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Patientvo dataset. Call this first to learn the schema.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description enumerates the returned data elements (columns, numeric indicators, row count, provenance banner) and implies a read-only schema inspection, providing adequate transparency without 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 a single focused sentence with an actionable instruction, containing no redundant or extraneous content.
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?
Although there is no output schema, the description names the key output categories and explains why to call the tool first, giving sufficient context for a simple schema-introspection endpoint.
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 tool has no parameters, so parameter documentation is not needed; the description fully covers the input space by omission.
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 returns columns, numeric flags, row count, and provenance banner for the Patientvo dataset, and explicitly instructs to call it first to learn the schema.
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?
It gives explicit guidance to call this tool first to learn the schema, which is sufficient for a zero-parameter introductory schema 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.
Most tools have clearly distinct purposes: schema, search, exact row lookup, multi-value comparison, stats, top/bottom, and provenance. Some slight overlap exists between dataset_row and dataset_compare for single-value filters, but the descriptions generally make the intended use clear.
All tools share a consistent dataset_ prefix and use snake_case, which makes them predictable. The suffixes are a mix of result nouns and action verbs, but this does not create confusion because the prefix dominates the naming pattern.
Seven tools is a well-scoped set for a single-dataset query server. Each tool serves a distinct analytical need without unnecessary redundancy, making the surface easy to navigate.
The tool set covers schema exploration, provenance, exact lookup, text search, group comparison, summary stats, and top/bottom ranking. Minor gaps like pagination or distinct-value listing are workable around with existing tools, so the core domain is well covered.