dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the WallChartBook 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 discloses the output contents (columns, numeric indicators, row count, provenance banner) and implies a read-only nature. It doesn't mention potential side effects or limitations, but for a schema-introspection call this is sufficient.
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 entire description is one sentence that front-loads the essential output and the usage directive. No wasted words; it is appropriately sized for its purpose.
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 the tool has no output schema and no parameters, the description must explain what it returns and when to use it. It covers both. It could specify the exact structure of the return (e.g., a list or object), but for an entry-point schema tool, the description is sufficiently complete for an agent to call it 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?
There are zero parameters, and schema description coverage is trivially 100%. The baseline for 0 params is 4. The description adds no parameter-specific meaning, but none is needed. It also clarifies the dataset scope, which is valuable.
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 states the verb (learn/get) and the resource (columns, numeric flags, row count, provenance banner of the WallChartBook dataset). It explicitly differentiates from siblings by positioning itself as the entry-point schema tool, so an agent can immediately tell it apart from dataset_row 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?
Provides explicit guidance: 'Call this first to learn the schema.' This tells the agent when to use it. It doesn't list when not to use it or name alternatives, but the directive is strong and contextually clear given the sibling set.
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, but dataset_row and dataset_compare overlap for single-value equality checks, and dataset_search's substring match can also return exact matches. The descriptions largely resolve this, so the ambiguity is minor.
All tool names use the same dataset_ prefix and lowercase snake_case, which is predictable and clean. However, the suffix mixes nouns (columns, row, stats, provenance) with verbs (compare, search) and the adjective-like 'top', so it is not a fully consistent verb_noun pattern.
Seven tools is an appropriate size for a dataset-query server; each tool covers a distinct query pattern (schema, exact lookup, search, comparison, stats, ranking, provenance) without redundancy or bloat.
The server covers the core read-only workflows for the dataset: schema discovery, row retrieval, text search, comparisons, numeric summaries, top/bottom rankings, and citation metadata. It lacks a way to list all rows or get distinct column values, but these are workable gaps rather than dead ends.