dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Runwayze 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 behavioral disclosure burden. It reasonably implies a read-only schema-introspection operation and enumerates the returned information. It does not explicitly state 'read-only' or side-effect absence, but the tool's nature makes that low risk.
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, front-loaded sentence that lists the essential outputs and the recommended call order. Every phrase earns its place and there is no filler.
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 zero-parameter, no-output-schema introspection tool, the description is complete: it states what data is returned, scopes it to the specific dataset, and tells the agent when to invoke it. Nothing needed to call the tool correctly is missing.
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 is empty with zero parameters, so the baseline is 4. The description adds the relevant dataset context ('Runwayze dataset') and what the agent will learn, which is sufficient compensation given there are no parameters to explain.
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 states exactly what the tool returns: columns, numeric flags, row count, and provenance banner for the Runwayze dataset. It also conveys the intended call sequence ('Call this first to learn the schema'), clearly distinguishing it from the sibling tools.
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 timing guidance by saying to call this first to learn the schema, which is clear context for use. It does not name alternatives or when-not-to-use cases, so it stops short of the strongest possible usage guidance.
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 match, substring search, multi-value comparison, numeric stats, and top-N. There is some overlap between the row retrieval tools, but the descriptions clarify the matching semantics enough to avoid serious confusion.
All tools share a consistent dataset_ prefix and are concise, but they mix noun-style names (dataset_columns, dataset_provenance, dataset_row, dataset_stats) with verb/adjective-style names (dataset_compare, dataset_search, dataset_top). The pattern is still predictable and readable.
Seven tools is well-scoped for a read-only dataset query server. Each tool covers a distinct query mode or metadata need, so none feel redundant or excessive.
The tool set covers schema discovery, provenance, exact lookup, fuzzy search, multi-value comparison, numeric aggregations, and top/bottom ranking. A minor gap is the lack of a generic sample/all-rows or distinct-value exploration tool, but the core question-answering workflows are well supported.