Dataset columns and shape
dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Reqnix dataset. Call this first to learn the schema.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Reqnix 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 of behavioral disclosure. It states what is returned (columns, numeric flags, row count, provenance banner) but does not explicitly mention whether the operation is read-only or has side effects, nor any error conditions. Since it is a schema-discovery tool, read-only behavior is implied but not stated.
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 two sentences, with the primary output list in the first sentence and the usage guidance in the second. It is front-loaded, contains no filler, and every word adds value.
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?
For a simple tool with no parameters and no output schema, the description adequately covers what it returns and when to call it. It does not describe the output structure or format, but that is not critical for an agent to invoke it successfully. The provenance banner concept is mentioned but not explained, which is a minor gap.
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, so the schema provides no parameter documentation. The description adds meaning by explaining what the tool returns, which is the sole source of information about its behavior. This exceeds the baseline for a zero-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 the tool returns the columns, which are numeric, row count, and provenance banner, and explicitly positions it as the first call to learn the schema. It is not a tautology and conveys a specific purpose, though it does not name sibling tools to differentiate itself.
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 phrase 'Call this first to learn the schema' provides explicit usage context, telling the agent when to invoke this tool relative to others. It lacks explicit exclusions or alternative conditions, 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 targets a distinct query mode: schema, exact lookup, substring search, multi-value comparison, statistics, top/bottom ranking, and provenance. There is slight overlap between dataset_row, dataset_compare, and dataset_search, but their descriptions clarify exact, multi-value, and containing matches.
All tool names share the consistent dataset_* prefix and follow a clear pattern of dataset_ plus the operation or target concept. This makes the set predictable and easy to scan.
Seven tools is a well-scoped size for exploring a single dataset. Each tool covers a distinct query or metadata need without redundancy or bloat.
The tools cover the full dataset exploration lifecycle: schema discovery, provenance, exact row lookup, substring search, ordered comparison, numeric statistics, and ranking. There are no obvious dead ends for common questions about this dataset.