dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Rollupvo 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 full burden. It discloses the data returned (columns, numeric flags, row count, provenance banner) and implies a read-only operation. However, it does not mention potential failure modes, cost, or whether this is a lightweight metadata call, leaving some behavioral context unspecified.
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?
A single sentence front-loads the core purpose ('The columns...') and ends with an actionable usage directive. No filler or redundant wording; every element earns its place.
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 explains the return values and when to call it, covering both major agent needs. It could elaborate on the format or content of the provenance banner, but the essentials are present.
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 parameters, so the baseline is 4. The description adds value by clarifying what the tool returns, even though no parameter explanations are needed. It fully compensates for the empty schema.
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 states a specific verb-resource relationship: it returns columns, numeric flags, row count, and provenance banner for the Rollupvo dataset. It also provides a clear use case ('Call this first to learn the schema'), which distinguishes its purpose from search/stats tools, though it does not explicitly name siblings.
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?
'Call this first to learn the schema' is an explicit instruction about when to use this tool, positioning it as the initial schema-discovery step. It does not mention when not to use it or name alternatives, but the usage context is clear enough for an agent.
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 have clearly distinct purposes: schema, provenance, stats, search, and top-N are easy to separate. Dataset_row and dataset_compare both filter rows by column values, though dataset_compare is specifically for ordered multi-value comparisons and dataset_row is exact single-value lookup.
All tools share the consistent dataset_ prefix and snake_case style, making the set feel predictable. The second part mixes nouns and verbs slightly (columns, compare, row, search, stats, top), but the overall pattern is still coherent.
Seven tools is well-scoped for a single dataset exploration server. Each tool covers a distinct query need without redundancy or excessive granularity.
The server covers schema discovery, provenance, exact lookup, substring search, comparisons, numeric stats, and top/bottom ordering. Minor gaps like distinct-value enumeration or arbitrary sampling exist, but the core read-only exploration surface is well covered.