dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Scopedly 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 present, the description carries the behavioral disclosure burden. It explains what the tool returns but does not explicitly state that it is read-only, side-effect-free, or describe any failure/performance characteristics. For a simple metadata tool this is acceptable but 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?
The description is a single functional sentence with a clear front-loaded action instruction. There is no filler, and each piece of information contributes to the agent's 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?
For a no-parameter schema-introspection tool, the description is largely complete: it lists the returned components and gives sequencing guidance. Because there is no output schema, some detail about the exact shape of the returned structure would be helpful, but the tool's simplicity keeps this from being a major 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 already covers everything needed. Even though the description adds no parameter-specific detail, none is required; the baseline 4 applies.
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 specifies what the tool returns: columns, numeric column flags, row count, and the provenance banner. It also establishes the tool's role as the schema-discovery entry point, distinguishing it from siblings like dataset_row or dataset_search, which operate on data rather than metadata.
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' gives clear, actionable context for when to use the tool. It does not explicitly name exclusions or alternatives, but the schema-first guidance is strong enough to route an agent appropriately.
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 dataset operation, though dataset_row, dataset_search, and dataset_compare all retrieve rows via different matching semantics. The descriptions clearly separate exact equality, substring containment, and multi-value ordering, so an agent can select correctly.
All tool names follow a consistent dataset_<noun> pattern with snake_case throughout. The naming makes the tool family immediately recognizable and predictable.
Seven tools is a well-scoped set for a dataset exploration server. Each tool covers a necessary operation—schema, provenance, lookup, search, comparison, stats, and top rows—without redundancy.
The set covers the core dataset exploration lifecycle well: schema discovery, provenance, exact and fuzzy retrieval, statistics, and ranking. A minor gap is the lack of a general sample/random row tool, but existing operations are sufficient for most dataset questions.