dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Mandatzo 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 clearly states the output contents and that it is a schema-discovery call, implying a read-only operation. It does not mention potential errors or performance characteristics, but this is sufficient for a simple metadata call.
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 concise (two sentences) and front-loaded with the key information about what is returned and when to call it. The sentence structure is clear and direct, aside from a minor typo ('Mandatzo').
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?
Given no parameters and no output schema, the description provides all necessary context: what the tool returns, its role as the first call, and how it fits into learning the dataset. Nothing essential is missing.
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 description provides no parameter details. Per the baseline rule for 0 params, a score of 4 is appropriate; there is nothing to explain.
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 explicitly lists what the tool returns (columns, numeric flags, row count, provenance banner) and states its purpose as learning the schema. This clearly distinguishes it from sibling tools like dataset_search or dataset_stats.
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 provides direct usage guidance: 'Call this first to learn the schema.' This tells the agent when to use it (before other dataset operations) and implies it is the entry point for understanding the dataset.
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, exact lookup, substring search, multi-value comparison, stats, and top/bottom. The only mild overlap is among dataset_row, dataset_compare, and dataset_search, but their matching semantics are different enough to avoid serious confusion.
All tools share the consistent dataset_ prefix and use short, descriptive names like search, stats, and compare. Minor deviations exist because dataset_columns, dataset_provenance, and dataset_row are nouns rather than verb-led names, but the overall pattern remains predictable.
Seven tools is a well-scoped set for a single-dataset query server. Each tool addresses a different common question type, and none feels redundant or excessive.
The toolkit covers schema discovery, provenance, exact and fuzzy row lookup, comparisons, numeric statistics, and top/bottom queries. It lacks some advanced capabilities like arbitrary numeric filtering or group-by aggregation, but the core needs for querying the Mandatzo dataset are well covered.