dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Dispatchzo 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 behavioral transparency burden. It clearly discloses the output contents: columns, numeric indicators, row count, and provenance banner. For a zero-parameter metadata inspection tool, this is sufficient transparency about what the agent will receive.
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 two short sentences with no filler. The output contents are front-loaded in the first sentence, and the usage instruction is in the second, making it easy to parse quickly.
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 parameterless schema-introspection tool, the description is complete: it names the dataset, lists the return components, and tells the agent to call it first. No output schema exists, so describing the return values directly is the right and sufficient move.
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 no parameters and the schema is empty, so there are no parameter semantics to explain. The baseline of 4 for a zero-parameter tool applies, and the description does not need to compensate for any undocumented inputs.
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 identifies what the tool returns: columns, numeric flags, row count, and provenance banner for the Dispatchzo dataset, and frames it as a schema-learning step. It does not explicitly distinguish itself from sibling tools by name, but 'Call this first' positions it as the initial schema-introspection tool.
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?
It gives explicit usage timing: 'Call this first to learn the schema.' This tells an agent when to use it in a workflow, though it does not state when not to use it or name alternative tools such as dataset_stats or dataset_provenance.
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 targets a distinct query need: schema, provenance, exact row lookup, substring search, multi-value ordered comparison, numeric stats, and top/bottom ranking. There is no meaningful overlap that would make an agent uncertain which tool to call.
All tools follow a consistent `dataset_` prefix with concise snake_case names that clearly reflect their function. The naming convention is uniform and predictable across the entire set.
Seven tools is a well-scoped set for a dataset-querying server. Each tool covers a distinct operation and none are redundant or superfluous.
The toolset covers schema discovery, provenance, exact and substring search, comparison, statistics, and ranking—strong coverage for a dataset Q&A server. Minor gaps exist such as no direct way to fetch all rows, list distinct values, or filter on multiple columns simultaneously, but these can usually be worked around with the existing tools.