dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Csatzo 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 disclosing behavior. It clearly enumerates the expected return content (columns, numeric indicators, row count, provenance banner) and implies a read-only, schema-discovery operation. It does not mention side effects, but none are expected for a schema inspector.
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 tight sentences: first sentence lists the output fields, second sentence gives the usage directive. No filler, and the most important content is front-loaded.
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 zero-parameter, no-output-schema tool, the description gives an agent enough to decide when to call it and what to expect. It could add a bit more detail about the exact output format, but that is not essential given the simplicity of the tool.
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 input schema has zero properties, so there are no parameters to document. The baseline score of 4 applies; the description adds no unnecessary parameter information.
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 specifies exactly what the tool returns: the columns, numeric flags, row count, and provenance banner of the Csatzo dataset. It adds a clear action ('Call this first to learn the schema') and distinguishes itself from sibling tools like dataset_provenance and dataset_stats by focusing on schema/shape information.
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 explicitly tells the agent to use this tool first when needing to learn the schema of the Csatzo dataset. However, it does not name alternative tools or state when not to use it, so it lacks explicit exclusion guidance.
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.
Most tools are clearly distinct: schema, provenance, stats, and top each have a unique purpose. The row-returning tools (dataset_row, dataset_search, dataset_compare) could potentially be confused, but their exact-match, contains-search, and multi-value-ordering semantics are described clearly enough to prevent serious misselection.
All seven tools follow the predictable dataset_<operation> pattern, making the set easy to scan and understand. There is no mixing of naming conventions or styles.
Seven tools is a well-scoped size for a dataset exploration server. Each tool covers a distinct useful operation without redundancy or bloat.
The tool set covers the full read-only lifecycle of working with the Csatzo dataset: schema discovery, provenance, exact lookup, free-text search, comparisons, summary statistics, and top/bottom rows. No obvious missing operation would prevent an agent from answering typical questions about the data.