dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the PerDiemDesk 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 behavioral burden. It discloses the exact output contents (columns, numeric flags, row count, provenance banner), implying a read-only metadata operation. It omits potential edge cases or cost notes, but for a schema-inspection tool this is adequate.
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 sentences with no filler: the first enumerates the outputs, the second gives the usage directive. Information is front-loaded and every word earns its place.
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 parameterless, simple metadata tool with no output schema, the description fully covers what an agent needs: what it returns and when to call it. There are no missing parameters or return-format requirements to document.
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?
There are zero parameters, so the input schema is trivially covered at 100%. Per the baseline for 0-parameter tools, a score of 4 is appropriate; the description adds no parameter-specific info because none is needed.
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 the columns, which are numeric, row count, and provenance banner for the PerDiemDesk dataset. It uses specific resource and content terms, and the instruction 'Call this first to learn the schema' distinguishes it from sibling data-access tools.
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 gives an explicit usage directive: 'Call this first to learn the schema,' positioning it as the entry point before other dataset tools. It does not name alternatives, but the timing guidance is clear and practical.
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 rows, substring search, stats, top values, and multi-value comparisons—so an agent can generally choose correctly. Some overlap exists between dataset_row and dataset_compare (both filter rows by column values), and dataset_search overlaps with dataset_row on substring matches, but the descriptions are clear enough to resolve the ambiguity.
All tool names follow the same dataset_<operation> pattern, with clear nouns like columns, row, search, stats, top, compare, and provenance. The naming is uniform and predictable, with no mixed conventions.
Seven tools is a well-scoped count for a single-dataset exploration server. Each tool covers a distinct query need without redundancy or unnecessary bloat.
The tool surface fully covers the read-only dataset workflow: schema inspection, provenance, exact lookup, substring search, statistical summaries, extreme-value ranking, and side-by-side comparisons. No obvious gaps would prevent an agent from answering typical questions about this dataset.