dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Orgplanly 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?
No annotations are provided, so the description carries the burden of behavioral disclosure. It indicates what the call returns and implies a non-mutating schema-discovery operation, but it does not explicitly state read-only behavior or clarify the relationship between the 'provenance banner' and the dataset_provenance sibling.
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?
One concise sentence that front-loads the key return values (columns, numeric flags, row count) and ends with an actionable instruction. No filler, though the phrase 'provenance banner' is slightly jargon-heavy.
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 schema-discovery tool, the description names the main output components and tells the agent when to invoke it. There is no output schema, so exact formatting is unspecified, but the description provides enough to support a correct first call.
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 and no parameter explanation is needed. The description adds useful context about what the returned metadata contains.
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 a metadata/schema-introspection tool for a specific dataset ('Orgplanly dataset') and lists what it returns: columns, numeric flags, row count, and provenance banner. It does not explicitly differentiate from sibling tools like dataset_provenance or dataset_stats, but 'Call this first to learn the schema' establishes its role.
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 explicit usage timing: 'Call this first to learn the schema,' which is actionable guidance for an agent. It does not mention when not to use it or name alternative sibling tools, but the sequencing instruction is clear enough.
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 maps to a distinct query mode—schema, provenance, exact match, substring search, ordered multi-value comparison, numeric stats, and top/bottom ranking—so an agent can generally choose based on question type. The only mild overlap is between dataset_row and dataset_compare for single-value lookups, but the wording clarifies exact equality versus ordered multi-value matching.
All seven tools share the dataset_ prefix and consistent snake_case, making the family instantly recognizable. The suffix varies between noun-like and verb-like forms, but the pattern remains predictable and readable across the whole set.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a necessary operation without redundancy, and none feel superfluous or missing.
The toolset covers the full range of common data exploration needs for the Orgplanly dataset: schema discovery, provenance attribution, exact lookups, substring search, comparisons, numeric summaries, and ranked extremes. For a read-only dataset server, there are no obvious dead ends or significant gaps.