dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the ProbeLedger 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 burden and it does disclose the returned content in reasonable detail. However, it does not explicitly state that the call is read-only, has no side effects, or describe any error/empty-dataset behavior. This is acceptable for a schema metadata tool but not richly 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 compact sentences, with the concrete output content front-loaded and the usage directive placed second. Every clause contributes information and there is no repetition of the title or name.
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 metadata tool, the description covers the main outputs and the recommended usage order. It does not describe the exact result shape or whether the provenance banner is plain text, but the simplicity of the tool keeps the gap small.
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 zero parameters and a 100% schema coverage baseline of 4, so the description is not responsible for explaining parameter meaning. The description still adds value by clarifying what the output contains, which is more than the empty schema provides.
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 names a specific resource (ProbeLedger dataset) and the exact information returned: columns, numeric flags, row count, and provenance banner. It also frames its role as the schema-learning entry point, which clearly distinguishes it from sibling exploration 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 instruction 'Call this first to learn the schema' gives explicit placement in the workflow, implying it should precede tools like dataset_search or dataset_stats. It does not name alternatives or state when not to use it, but the 'first' directive provides enough guidance for an agent.
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 are distinct (schema, provenance, stats, top, compare), but dataset_row, dataset_search, and dataset_compare all return rows with subtly different matching semantics, which could confuse an agent. The descriptions help, but the boundaries between exact match, substring search, and ordered value comparison are not immediately obvious.
All tools share the consistent 'dataset_' prefix and use lowercase snake_case, which creates a clear family identity. However, the suffixes mix nouns (columns, row, stats, provenance) with verbs (compare, search, top), so the pattern is not perfectly uniform.
Seven tools is a well-scoped number for a single-focused dataset exploration server. Each tool addresses a distinct query need without redundancy or bloat.
The toolkit covers schema discovery, statistics, filtering, ranking, comparisons, and provenance, which covers most dataset exploration workflows. A minor gap is the lack of a way to retrieve arbitrary rows or the full dataset without a matching condition.