dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Sittingly 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 explaining behavior. It discloses what the tool provides (columns, numeric indicators, row count, provenance banner), which suggests a read-only informational operation, but it does not mention side effects, performance, or error behavior.
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 one concise sentence that front-loads the output contents and ends with a clear usage directive. Every word earns its place, with no redundant filler.
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 informational tool with no output schema and no annotations, the description adequately specifies what the agent will receive. It could be more explicit about the exact return format, but it is complete enough to call the tool correctly.
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 and the schema is empty, so there are no parameter semantics to explain. The description compensates by naming the target dataset and clarifying the tool's role, which is sufficient for a no-argument 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 identifies the tool's purpose: to report the columns, numeric flags, row count, and provenance banner of the Sittingly dataset. It is specific about the resource and output content, though it lacks an explicit verb like 'returns' or 'gets'.
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 guidance on when to use the tool. It implies this tool precedes other dataset operations, but it does not explicitly name alternatives or describe when not to use it.
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 discovery, provenance, exact lookup, substring search, stats, and top/bottom ranking are easy to tell apart. However, dataset_row and dataset_compare overlap in that both do exact column-value matching, with compare merely extending row to multiple values, which could cause some selection uncertainty.
All tools share the clean dataset_ prefix and use lowercase snake_case, making the set feel cohesive. The second part mixes nouns (columns, provenance, row, stats, top) with verbs (compare, search), but the pattern is still predictable and readable.
Seven tools is a well-scoped size for a dataset-querying server. Each tool covers a distinct query need without redundancy or bloat, and the count is comfortably within the ideal range.
The server covers the full read-only lifecycle of working with a dataset: schema discovery, provenance attribution, exact lookup, substring search, comparisons, numeric stats, and top/bottom ranking. There are no obvious dead ends for the stated purpose.