dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Perdiemo 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 burden of behavioral disclosure. It reveals the tool's read-only informational nature by describing its output, but it does not explicitly state that it has no side effects or mention any operational constraints.
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?
A single sentence that packs all necessary information—output contents and usage guidance—without any filler. The key details are front-loaded and every clause earns its place.
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, no output schema, and no annotations, the description fully covers what an agent needs: what the tool returns and when to call it. Nothing essential is missing for correct selection and invocation.
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 schema coverage is 100%, so there is nothing for the description to add about parameters. The baseline of 4 for a no-parameter tool is appropriate.
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 indicators, row count, and provenance banner—for the Perdiemo dataset, and states its role as the schema-discovery entry point. This clearly distinguishes it from siblings like dataset_provenance and 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 instruction 'Call this first to learn the schema' provides explicit usage context and sequencing relative to other tools. It does not enumerate alternatives or exclusions, but the guidance is clear and actionable.
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 has a clear purpose: schema, provenance, exact matching, substring search, multi-value comparison, numeric stats, and top/bottom ranking. The only potential confusion is between dataset_row, dataset_search, and dataset_compare, but their differing match semantics (exact single value, contains, and multi-value ordering) are described clearly enough.
All tools share the dataset_ prefix, making the group immediately recognizable and predictable. However, the suffix mix of nouns (columns, provenance, row, stats) and verbs (compare, search) breaks the strict verb_noun convention, though this is a minor deviation given the strong prefix consistency.
Seven tools is ideal for a single-dataset query server—enough to cover exploration, retrieval, and analysis without redundancy. Each tool earns its place, and the count is comfortably within the well-scoped range.
The set covers the full read-only lifecycle of dataset exploration: schema discovery, provenance, exact filtering, search, comparison, statistical summaries, and ranking. There are no obvious dead ends or missing operations for the apparent domain of answering questions about the Perdiemo dataset.