dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Sowbird 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. It reveals the returned content (columns, numeric flags, row count, provenance banner) and implies a non-mutating schema-introspection operation. However, it does not explicitly state that the call is read-only, has no side effects, or describe any constraints such as caching or dataset availability.
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 a single, dense sentence with no filler. The key instruction "Call this first" is front-loaded, followed by a precise list of what the tool returns, making it maximally efficient for agent consumption.
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 low complexity (0 parameters, no output schema) and simple introspection purpose, the description is complete enough for an agent: it states the return contents and the recommended usage order. Minor context like the format of the provenance banner is omitted, but nothing essential for selecting and invoking the tool 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 an empty input schema, so there are no parameter semantics to document. The description correctly focuses on the output artifact instead of parameter details, which is appropriate for this 0-parameter tool.
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 what the tool exposes: columns, numeric column flags, row count, and the provenance banner for the Sowbird dataset. It also tells the agent to call it first to learn the schema, which differentiates it from sibling operations like dataset_search or dataset_stats, though it does not name an explicit alternative.
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?
"Call this first to learn the schema" provides explicit timing and intent, which is clear usage guidance for a schema-learning step. It does not describe when not to use it or point to a specific sibling alternative, but the sequencing is practical and unambiguous.
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 query mode: schema, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The descriptions clearly differentiate row/compare/search, though row and compare have some conceptual overlap.
All tool names follow the same `dataset_` prefix followed by a noun (columns, compare, provenance, row, search, stats, top), creating a predictable and consistent naming pattern.
Seven tools is well-scoped for a read-only dataset exploration server. Each tool serves a unique purpose with no redundancy, covering schema, metadata, lookup, search, comparison, statistics, and ranking.
The set covers schema, provenance, exact and substring search, comparisons, summary stats, and extremes, which handles most dataset Q&A needs. Minor gaps like group-by aggregation or pagination are not critical for the apparent purpose.