dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Exitvo 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 implies a read-only operation by listing the informational outputs, but does not explicitly state that it is safe, has no side effects, or requires no permissions. This is a minor gap given the tool's nature.
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, efficient sentence that front-loads the core outputs and ends with a usage hint. It contains no fluff and every word contributes to understanding.
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?
The tool has no output schema, so the description must convey the response contents. It lists the four data elements, but does not specify the response structure (e.g., JSON keys, data types). For a simple getter, this is sufficient but not fully detailed, and no prerequisites are mentioned.
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 there is nothing for the description to add beyond the schema. The baseline score of 4 applies since no parameter documentation is needed.
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 specific schema information (columns, numeric flags, row count, provenance banner) for the Exitvo dataset. It also positions it as the first call to learn the schema, which helps differentiate it from siblings like dataset_stats or dataset_search, though it does not explicitly name them.
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 direct usage guidance, implying this is the entry point before using other dataset tools. It does not explicitly list alternatives or when not to use it, but the actionable direction 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.
Each tool has a distinct role: schema, provenance, exact row lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The only minor overlap is between dataset_row and dataset_compare, but their descriptions clearly separate single-exact-match from multiple-value in-order filtering.
All tools share the consistent 'dataset_' prefix with short, readable suffixes. Most suffixes are nouns (columns, row, stats, top), while 'compare' and 'search' read as verbs, a small grammatical deviation from an otherwise uniform pattern.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a meaningful query operation—metadata, lookup, search, aggregation, sorting—without redundancy or bloat.
The surface covers core dataset workflows: understanding schema, citing provenance, finding rows by exact match or substring, comparing values, computing statistics, and identifying extremes. A minor gap is the lack of distinct-value listing or pagination, but the main use cases are well supported.