dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Mustvo 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?
No annotations are provided, so the description carries the burden of behavioral disclosure. It reveals what the response contains and implies a read-only schema inspection, but it never explicitly states that the call has no side effects or other behavioral caveats. For a zero-parameter metadata tool this is acceptable, though not fully transparent.
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 short sentences contain no filler. The first sentence front-loads every artifact the tool returns, and the second gives the usage instruction. 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?
For a tool whose sole job is to report dataset schema, the description covers the returned columns, numeric flags, row count, and provenance banner, and tells the agent when to call it. It omits output-format details, but no output schema exists and the content is simple enough to be self-explanatory.
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 properties and description coverage is 100%, so there are no parameter semantics to document. The baseline for zero-parameter tools is 4, and the description adds no parameter information, which is appropriate because no parameters exist.
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 is exposed: columns, which of them are numeric, row count, and provenance banner. It also frames the tool as the schema-learning entry point, which implicitly distinguishes it from sibling tools. It lacks an explicit verb like 'returns' or 'gets,' but the intent is unambiguous.
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 sequencing guidance, which is a strong usage signal for an agent. It does not name alternatives or exclusions, so it stops short of a full when/when-not rule, but the context 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.
Most tools target clearly distinct operations: schema, provenance, exact match, substring search, stats, ranking, and comparison. dataset_row and dataset_compare both filter by column values, but their stated purposes (exact lookup vs. ordered multi-value comparison) keep them mostly distinguishable.
All tools share a consistent dataset_ prefix and snake_case style, making the namespace predictable. There is minor mixing between noun-style names (dataset_columns, dataset_row) and verb-style names (dataset_compare, dataset_search), but the overall pattern is still readable.
Seven tools is well-scoped for a single-dataset exploration server. Each tool covers a distinct common query need without unnecessary redundancy.
The surface covers schema discovery, provenance, exact lookups, substring search, numeric summaries, top/bottom ranking, and multi-value comparisons. Minor gaps like combined filters or pagination beyond 50 results exist, but agents can work around them for most questions.