dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Tachovo 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 burden of behavioral disclosure. It explains what information will be returned and that it should be called first, but it does not explicitly state that the operation is read-only or describe how the output is structured.
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 a single, dense sentence with no filler. It front-loads the important output scope and ends with a useful invocation hint.
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-argument schema-introspection tool without an output schema, listing the exact pieces of information returned is sufficient. An agent has what it needs to call the tool and interpret its result.
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 there is no parameter ambiguity. The no-parameter baseline of 4 applies; no description-level compensation 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 identifies the exact resource (Tachovo dataset) and the specific outputs: columns, numeric flags, row count, and provenance banner. It is not a tautology and gives a clear purpose, though it does not explicitly name sibling tools to differentiate from.
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 clear usage context and sequencing. It does not mention when not to use it or explicitly contrast it with sibling tools, but the guidance is direct enough 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.
Each tool has a distinct purpose: schema, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The only possible confusion is between dataset_row and dataset_compare, since both match column values exactly, but the descriptions clarify single vs. multiple values.
All tool names share the dataset_ prefix and follow a consistent noun/feature pattern: columns, compare, provenance, row, search, stats, top. The convention is uniform and predictable.
Seven tools is well-scoped for exploring a single dataset. Each tool covers a distinct query mode or metadata need without redundancy or bloat.
The surface covers schema discovery, provenance attribution, exact matching, free-text search, multi-value comparisons, numeric statistics, and extremes. For a read-only dataset access server, this is a complete and practical toolkit with no obvious dead ends.