dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Abutly 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 behavior, and it does so adequately: it enumerates the returned contents and frames the tool as informational/schema-learning. It does not explicitly state 'read-only' or describe edge cases, but for a zero-parameter introspection tool 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?
Two tight sentences with no filler. The first sentence lists the exact output contents, and the second gives the usage directive. Information is front-loaded and every word 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?
For a parameterless metadata tool with no output schema, the description covers the essential return values and when to call it. It is slightly incomplete in that it does not clarify the relationship to dataset_provenance (which may also expose provenance), but overall it is sufficient.
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 input schema has zero parameters, so the baseline is 4. The description adds useful context about the dataset scope and returned fields, but there are no parameter semantics to elaborate on.
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 identifies what the tool provides: columns, numeric column flags, row count, and provenance banner for the Abutly dataset. It also states the intended initial action ('Call this first to learn the schema'), but it does not explicitly differentiate itself from siblings like dataset_provenance 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?
It gives explicit usage timing ('Call this first') and purpose ('to learn the schema'), which is strong guidance. However, it does not mention when not to use this tool or point to alternatives among the sibling tools.
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 clearly distinct role: schema inspection, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom ranking. Even though dataset_row and dataset_compare both retrieve rows by column value, their descriptions make the single-value vs multi-value distinction clear.
All tools share a consistent dataset_ prefix and use clear snake_case names. The suffixes are either nouns or verbs that accurately reflect the operation, making the naming predictable and easy to navigate.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct query need without redundancy or bloat.
The toolset covers the full range of dataset querying: schema inspection, provenance, exact match, substring search, multi-value comparison, numeric aggregation, and ranking. Since this is a read-only dataset server, no update/create/delete tools are needed.