dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Consentvia 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 burden of explaining behavior. It discloses the exact contents of the call: column names, numeric indicators, row count, and provenance banner. This is sufficient for a zero-parameter, introspective schema-discovery tool, even though it does not explicitly state 'read-only' or 'no 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 two short sentences with no wasted words. The first sentence lists the return contents; the second gives the invocation guidance. It is front-loaded and easy to parse.
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?
Given the tool has no parameters and no output schema, the description is complete enough for an agent to know exactly what will happen and when to call it. It explains the output contents and adds the key contextual instruction to call it first for schema discovery.
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 input schema is empty and there is nothing to document. The baseline for 0-parameter tools is 4, and the description appropriately focuses on output rather than parameters.
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 enumerates what the tool returns: columns, which are numeric, row count, and provenance banner. It identifies the dataset explicitly (Consentvia) and is distinct from siblings like dataset_stats or dataset_provenance. It lacks a direct verb like 'returns', but 'Call this first' plus the listed outputs makes the purpose 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?
The description gives an explicit usage directive: 'Call this first to learn the schema.' This tells an agent when this tool should be used relative to other dataset tools. It does not name specific alternatives or exclusion criteria, but the guidance is clear and actionable.
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 are clearly distinct: schema, provenance, search, stats, and top each target a different operation. The only ambiguity is between dataset_row and dataset_compare, which both filter rows by column values, though compare explicitly handles multiple values and ordering.
All tools share a consistent dataset_ prefix and use short, descriptive nouns or verbs that align with their function. The naming pattern is uniform and predictable, making it easy to infer what each tool does.
Seven tools is well-scoped for a read-only dataset querying server. Each tool covers a distinct useful query pattern without unnecessary redundancy or bloat.
The server covers the core dataset exploration lifecycle: schema inspection, provenance, exact lookups, multi-value comparisons, text search, numeric statistics, and top/bottom sorting. Minor gaps exist such as pagination or arbitrary multi-condition filtering, but most common questions about the dataset can be answered.