dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the TermsBird 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?
Given no annotations, the description carries the full burden. It implies a read-only operation by describing the returned metadata, and the 'call first' advice suggests no side effects. It does not explicitly state 'does not modify data', but the nature of the tool makes this obvious enough for a high score.
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 brief and to the point, listing the exact outputs in one sentence and adding a clear usage instruction in the second. Every word adds value.
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 schema-introspection tool, the description covers all essential aspects: the columns, numeric indication, row count, and provenance banner. It also includes the critical 'call first' guidance, making it complete for an agent to understand the tool's role and output.
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 takes zero parameters, so the baseline of 4 applies. The description correctly omits any parameter details since there are none.
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, indicates which are numeric, provides the row count, and shows the provenance banner. It is specific and directly distinguishes this tool from the sibling tools, which focus on searching, comparing, or retrieving individual rows.
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?
Explicitly instructs to 'Call this first to learn the schema', providing clear when-to-use guidance. This tells the agent this is the initial exploratory tool, which is distinct from the other dataset operations.
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 discovery, filtering, provenance, exact lookup, substring search, statistics, and top/bottom sorting. No two tools overlap in function, and the descriptions reinforce the boundaries.
All tools follow the consistent dataset_<verb> pattern with no deviations. The naming convention is uniform and predictable.
7 tools is well-scoped for a dataset querying server. Each tool covers a necessary operation without redundancy or bloat.
The tool set covers schema, row retrieval, search, statistics, sorting, comparison, and provenance. The only minor gap is a straightforward 'get all rows' operation, but the existing tools can likely cover most workflows.