dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Yearendo 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?
No annotations, so description carries burden. It explicitly lists the output contents (columns, numeric flags, row count, provenance banner) and the purpose of schema discovery, which transparently sets expectations for a read-only introspection tool.
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?
One sentence that packs the core output and usage instruction without redundancy; efficient and well-structured.
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?
Provides enough context for a schema-first workflow: identifies the dataset (Yearendo), lists the key output fields, and instructs to call first. No missing information that would cause misuse.
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?
Tool has zero parameters and empty schema, so description adds no parameter-specific meaning. Baseline 3 applies because coverage is 100% (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?
States it provides columns, numeric flags, row count, and provenance banner, plus explicit instruction to call first for schema – clearly distinguishes from siblings like dataset_stats or dataset_provenance.
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?
Provides explicit usage instruction: 'Call this first to learn the schema.' This tells the agent when to use it, though it doesn't explicitly contrast with alternatives, the priority is clear.
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 clear, distinct purposes: schema, search, stats, provenance, and top/bottom comparisons are unambiguous. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by exact column values, but descriptions clarify that compare handles multiple values in a specific order.
All tools follow a consistent dataset_ prefix with clear, lowercase snake_case names. The naming pattern is predictable and easy to scan, with no mixing of styles or vague generic verbs.
Seven tools is a well-scoped set for a single-dataset server. Each tool covers a distinct common operation—schema, lookup, search, comparison, stats, top values, and provenance—without unnecessary bloat.
The toolkit covers the core read-only operations needed for exploring and querying the Yearendo dataset: schema discovery, exact match, substring search, ordered comparison, numeric stats, ranking, and attribution. Minor gaps like grouped aggregations or combined filters exist, but agents can usually work around them with existing tools.