dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Extinvo 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 responsibility of explaining behavior. It clearly discloses what the tool returns (columns, numeric indicators, row count, provenance banner) and implies a read-only introspection role through 'learn the schema.' It does not discuss side effects, but for a zero-parameter schema-info tool this is sufficient.
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, no filler. The content payload is front-loaded, and the actionable guidance 'Call this first' is placed at the end without being obscured.
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 tool with no parameters and no output schema, the description fully explains what an agent will get and when to call it. The four return components are explicitly listed, making the tool self-sufficient.
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, so the baseline is 4. The description adds context by naming the fixed resource, the Extinvo dataset, even though there is no input schema to clarify.
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 states exactly what the tool returns: columns, numeric flags, row count, and provenance banner for the Extinvo dataset. It also gives an explicit instruction to call it first to learn the schema, which clearly distinguishes it from siblings like dataset_stats, dataset_row, and dataset_provenance.
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 clear usage context with 'Call this first to learn the schema,' telling the agent when in the workflow this tool should be invoked. It does not explicitly mention when not to use it or name alternative tools, so it stops short of a 5.
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.
The tools are mostly distinct: columns, provenance, stats, and top each have a single clear role, while dataset_row, dataset_search, and dataset_compare all return rows but differ by exact match, substring containment, and ordered value comparison. The descriptions explain these differences clearly, so misselection is unlikely but still possible.
Every tool follows the same dataset_<operation> pattern with a clear noun or verb suffix. The naming is predictable and the row-returning tools use distinct names (row, search, compare) that match their behavior.
Seven tools is well-scoped for exploring a single dataset. Each tool covers a meaningful operation and none feel redundant or superfluous.
The set provides schema, provenance, exact lookup, free-text search, compare, stats, and top/bottom ranking, which covers the main ways an agent would query this dataset. No obvious dead-end or missing core operation is apparent.