dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Rapportvo 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 provided, the description carries the burden of explaining behavior. It states what information is returned, which is useful, but it does not explicitly state the operation is read-only or side-effect-free. The phrasing strongly implies introspection, but leaves room for ambiguity.
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 concise and front-loaded, leading with the returned contents and adding the usage instruction. Every sentence earns its place, and there is no redundant or filler language.
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 schema-discovery tool with no parameters and no output schema, the description fully covers what is returned, the dataset it applies to, and when to invoke it. An agent has enough information to call it correctly as a first step.
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 parameter documentation is not needed. The description adds no parameter-specific semantics, but the schema already covers this completely with an empty parameter object.
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, provenance banner) for the Rapportvo dataset, and is positioned as the schema-learning entry point. It does not explicitly contrast with siblings like dataset_stats or dataset_provenance, though 'call this first' implies a distinct 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 instruction 'Call this first to learn the schema' provides clear timing guidance relative to other tools. It does not state when not to use it or name alternatives, but for a schema-discovery tool this is sufficient context.
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 query modes, but dataset_columns includes a provenance banner that slightly overlaps with dataset_provenance, and dataset_row/dataset_compare both support exact-value filtering on a column. These boundaries are manageable but not perfectly crisp.
All tools share the dataset_ prefix and snake_case style, which is predictable and consistent. However, the second part mixes verbs (compare, search), nouns (columns, provenance, row), and adjectives/nouns (top, stats), so there is no uniform verb_noun pattern.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct type of data access or metadata need without feeling padded or redundant.
The surface covers schema, provenance, exact lookup, substring search, multi-value comparison, statistical summaries, and top/bottom ranking for the dataset. It lacks generalized arbitrary filtering or full row pagination, but the core read-only dataset workflow is well covered.