dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Fax Line Index 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 full burden. It discloses the output contents (columns, numeric flags, row count, provenance banner) and implies a read-only nature via 'learn the schema'. It does not state side effects or performance, but for a schema-introspection tool this is adequate.
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?
A single, dense sentence that front-loads the output contents and usage guidance. Every element earns its place with no fluff.
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 with no parameters and no output schema, the description fully specifies what the agent will receive. It is complete for its intended role as a schema-discovery tool.
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 baseline of 4 applies. No parameter descriptions are needed or provided; the description adds no parameter-specific meaning because there are none.
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 a specific verb (call) and resource (Fax Line Index dataset), and enumerates exactly what it returns: columns, numeric flags, row count, and provenance banner. This clearly distinguishes it from siblings like dataset_provenance, which focuses solely on provenance, and dataset_row, which returns data rows.
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 to 'call this first to learn the schema', establishing it as the entry point for understanding the dataset structure. This provides clear precedence over sibling tools, though it does not enumerate exclusions.
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, provenance, search, exact match, stats, top, compare, describe, fields, and submit. No overlap or ambiguity between dataset exploration and enquiry submission tools.
All tools follow a consistent snake_case pattern with clear prefixes (dataset_ and enquiry_) and descriptive suffixes (columns, search, stats, submit). Naming is uniform and predictable.
10 tools is well-scoped for a server that handles dataset exploration (7 tools) and an enquiry workflow (3 tools). Each tool earns its place and covers necessary operations without redundancy.
The dataset tools cover schema, provenance, search, exact match, statistics, top values, and comparison, while the enquiry tools cover description, field schema, and submission with confirmation. No obvious gaps for the stated purpose.