dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Fitouto 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, and it does well by disclosing what the tool returns: column names, numeric indicators, row count, and provenance banner. It does not explicitly state there are no side effects, but the read-only introspection nature is strongly implied.
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 short sentences with no filler. The output contents are listed compactly, and the usage instruction is front-loaded as a direct call to action.
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 no-parameter, schema-introspection tool, the description is complete: it explains what is returned and when to use it. No output schema exists, so the enumerated return items are sufficient.
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 100% schema coverage, so there is no parameter meaning that needs explanation. The description adds value by explaining what the tool will tell you rather than any parameter details.
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 resource ('Fitouto dataset') and the exact outputs: columns, numeric flags, row count, and provenance banner. It also gives the tool a distinct role among siblings by saying 'Call this first to learn the schema.'
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 description gives explicit usage context: call this tool first to learn the dataset schema. It does not enumerate exclusions or compare against siblings, but for an initial schema-introspection tool, the guidance is clear enough.
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 targets a distinct operation: schema inspection, exact row lookup, substring search, value comparison, statistics, top/bottom ranking, and provenance. Although search, row, and compare all retrieve rows, their matching semantics are clearly different and unlikely to be confused.
All tools follow the same predictable `dataset_<operation>` snake_case convention. The names consistently indicate which dataset capability they expose, making the tool surface easy to scan and remember.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a meaningful query mode without unnecessary redundancy or overwhelming the agent.
The server covers the full range of operations needed to explore the Fitouto dataset: schema discovery, provenance, exact-match row retrieval, substring search, comparative queries, numeric statistics, and ranking. There are no obvious dead ends for the apparent read-only querying purpose.