dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Tickmarko 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. It describes the output contents well and implies a read-only metadata retrieval, though it does not explicitly state the absence of side effects.
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 concise, one sentence listing outputs and the usage cue. Every word adds value.
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 description fully explains what the tool returns and when to use it, despite lacking an output schema. It gives enough context for an agent to invoke it correctly.
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?
There are no parameters, so there is nothing to document. The description correctly covers all input aspects by indicating none are 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 specifies the returned data (columns, numeric flags, row count, provenance banner) but lacks an explicit verb like 'get' or 'list'. It is still unambiguous and distinct from 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?
Explicitly instructs 'Call this first to learn the schema', providing clear when-to-use guidance. It does not mention alternatives but the instruction suffices for a metadata tool.
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 purpose: schema inspection, row retrieval, search, comparison, top values, statistics, and provenance. The descriptions make the differences explicit, so an agent can confidently select the right tool.
All tools follow a consistent 'dataset_' prefix with a descriptive noun or verb, such as dataset_columns, dataset_search, dataset_stats. The naming pattern is uniform and predictable.
With 7 tools for exploring a single dataset, the scope is well-balanced. Each tool addresses a specific need without redundancy or bloat, fitting the server's purpose.
The tool surface covers schema discovery, exact lookup, substring search, value comparison, top/bottom extraction, statistical summaries, and provenance—everything needed for read-only dataset exploration. No obvious gaps exist.