dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Medcontra 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?
With no annotations, the description carries the full disclosure burden. It enumerates exactly what the tool returns — columns, numeric indicators, row count, and provenance banner — and frames the operation as schema discovery, which implies safe read-only behavior. It could be more explicit about output formatting, but the behavioral profile is largely transparent.
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 short sentences with no filler. The output contents are listed efficiently, and the most important guidance — 'Call this first' — is clear and memorable.
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 zero-parameter tool with no annotations and no output schema, the description gives enough context: what the tool returns, for which dataset, and when to call it. The only minor gap is that 'provenance banner' is not explained, but the overall context is adequate.
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 input schema is empty and schema coverage is 100%, so there are no parameter semantics to document. The description adds no duplicated parameter detail and simply reinforces the intended first-step usage, matching the baseline for a zero-parameter tool.
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 a specific purpose: retrieve the columns, numeric flags, row count, and provenance banner for the Medcontra dataset. The phrase 'Call this first to learn the schema' gives the tool a distinct role among the siblings, so an agent can identify when to use it.
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 explicitly tells the agent to call this tool first to learn the schema, which is strong contextual guidance. It does not name alternatives or exclusion cases, but for a zero-parameter schema-introspection tool this is clear enough.
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.
Each tool targets a distinct query pattern: schema, provenance, exact match, substring search, multi-value comparison, aggregations, and sorted top rows. While dataset_row and dataset_compare both do exact column matching, their different purposes (single value vs ordered X vs Y comparisons) are clearly described.
All tools share a consistent dataset_ prefix followed by lowercase snake_case names, making the family recognizable. There is a minor mix of noun-like names (columns, row, stats, provenance) and verb-like names (compare, search, top), but the pattern is still predictable and readable.
Seven tools is well within the ideal range and each tool provides a distinct operation for exploring a dataset. The count feels neither bloated nor thin for the server's stated purpose.
The tool surface covers schema discovery, provenance, exact lookups, substring search, multi-value comparisons, numeric statistics, and top/bottom rows—covering the main ways one would interrogate a dataset. A minor gap is the lack of a general paginated 'all rows' or arbitrary filtering tool, but the provided tools handle most realistic queries.