dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Endpointo 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. It discloses the return content (columns, numeric flags, row count, provenance banner) but does not explicitly state it is read-only or safe to call repeatedly. The phrase 'learn the schema' implies a non-mutating operation, but it's not explicit.
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 fluff: the first lists the returned data, the second gives usage guidance. The key information is front-loaded, making it easy to scan.
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 no-parameter introspection tool, it lists the essential returned elements (columns, numeric flags, row count, provenance banner). It doesn't specify the output format, but for a schema-learning call, this is sufficient. The lack of an output schema is mitigated by the explicit list of what to expect.
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 baseline is 4. The description need not explain any parameters, and it doesn't. The schema coverage is trivially 100%.
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 explicitly states the tool returns columns, numeric flags, row count, and provenance banner, and identifies itself as the schema-learning entry point. This clearly distinguishes it from sibling tools like dataset_row (fetch a row) or dataset_stats (compute stats).
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?
'Call this first to learn the schema' gives explicit ordering guidance, positioning it as the initial introspection step. It doesn't mention alternatives or when not to use it, but the directive is clear and actionable.
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 has a clearly distinct purpose: schema, exact match, search, comparison, stats, top values, and provenance. No two tools overlap in functionality; an agent can easily select the right tool based on the query type.
All tool names follow the consistent 'dataset_' prefix followed by a descriptive noun (columns, compare, provenance, row, search, stats, top). The naming pattern is uniform and predictable.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a specific aspect of querying and understanding the dataset without unnecessary redundancy or bloat.
The surface covers schema, metadata, exact lookup, substring search, comparison, statistics, and ordering. It lacks features like distinct value enumeration or grouping, but for a read-only dataset exploration tool, the coverage is strong with only minor potential gaps.