dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Dividvo 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 full burden of behavioral disclosure. It accurately describes a read-only metadata query returning schema information and a provenance banner, with no side effects implied. It lacks an explicit 'does not modify data' statement, but the informational nature is clear from the description.
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 sentence that front-loads the returned items before adding the usage directive. Every word contributes meaning, with no filler or repetition.
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 simple, parameterless metadata tool with no output schema, the description fully covers what an agent needs: the exact return contents and the recommended invocation order relative to sibling tools. There is no missing operational detail.
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 schema already fully covers parameter semantics. The description does not need to compensate for missing parameter documentation. It adds value by explaining what the output will contain, which is above the baseline for a parameterless tool.
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 enumerates the four concrete pieces of information returned: columns, which are numeric, row count, and provenance banner. It names the specific resource (Dividvo dataset) and distinctively positions itself as the schema-learning tool, differentiating it from the sibling dataset_* tools.
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' explicitly indicates when to use this tool and implies it should precede other dataset operations. It does not name alternatives or exclusions, but the 'first' directive provides sufficient usage context for an agent to select it appropriately.
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 clearly different purpose: schema, provenance, exact match, search, stats, ranking, and comparison. dataset_row and dataset_compare overlap for single-value equality, but the descriptions make the ordered multi-value use case clear.
All tools follow the same dataset_ prefix with a descriptive noun or verb, forming a highly predictable naming pattern. There is no mixing of conventions or vague generic names.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct query mode without redundancy or unnecessary bloat.
The set covers schema discovery, provenance, exact filtering, substring search, numeric statistics, top/bottom ranking, and comparisons. Minor gaps exist such as pagination or listing all rows, but agents can accomplish most dataset tasks with these tools.