dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Sacristo 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 enumerates the returned information (columns, numeric flags, row count, provenance banner), which is useful, but does not mention any potential side effects, performance implications, or error conditions. This is adequate for a read-only metadata tool but not rich.
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?
Two sentences with no wasted words. The core content (what it returns) is front-loaded, and the usage directive is appended efficiently. This is a model of conciseness.
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 tool, the description covers what it returns and when to call it. It does not describe the exact output format or data types, but given the simplicity of the tool and the lack of an output schema, it is sufficient for an agent to understand its role.
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. The description does not need to explain parameters; instead it focuses on the output, which adds value beyond 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 clear purpose: it returns columns, numeric flags, row count, and provenance banner. The phrase 'Call this first to learn the schema' adds a usage directive that helps distinguish it as the entry point, though it does not explicitly name alternatives.
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?
It explicitly instructs to call this tool first to learn the schema, providing a clear context for when to use it. However, it does not mention when not to use it or name sibling tools as alternatives, leaving some inference to the 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 target clearly distinct operations—schema, provenance, search, stats, top, and exact lookup. Minor overlap exists between dataset_compare and dataset_row, both filtering rows by column values, but the descriptions clarify that compare handles multiple values while row is for a single exact match.
All tools follow a uniform dataset_<operation> pattern with consistent snake_case naming. The suffixes are a mix of nouns and verbs, but the prefix and format are fully predictable.
Seven tools is well-scoped for a single-dataset querying server. Each tool covers a distinct query need without redundancy.
The toolset covers schema discovery, provenance, exact and fuzzy matching, multi-value comparison, numeric statistics, and top/bottom rankings—good coverage for a read-only dataset exploration server. A possible minor gap is lack of a distinct-values or category-listing tool, but common analytical questions appear addressable.