dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Hreflangly 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 provided, the description carries the burden of disclosing behavior. It does specify the output contents (columns, numeric flags, row count, provenance banner) and implies a read-only schema inspection. However, it does not explicitly state that the call has no side effects, nor does it describe error conditions or the exact format of the response.
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 compact and efficient, using two sentences to convey the tool's purpose, output contents, and recommended invocation order. Every clause adds value, and the key usage directive is placed at the end as a clear call to action.
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 parameterless schema-introspection tool, the description is complete: it tells the agent what to expect in the response and when to call it. There is no output schema, but the listed output components are sufficient for understanding the tool's role in the workflow.
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 rubric sets a baseline of 4. The description adds no parameter-specific detail because none is needed; the input schema is empty and 100% covered by the schema definition itself.
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 identifies the specific resource (Hreflangly dataset) and the exact information returned: columns, numeric flags, row count, and provenance banner. It also explicitly frames the tool as the schema-learning step, distinguishing it from sibling tools that handle searching, statistics, or comparison.
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 explicit usage guidance: 'Call this first to learn the schema.' This tells the agent when to invoke it relative to other operations. It does not enumerate alternatives or exclusions, but the primary sequencing advice 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 have clearly distinct purposes: schema, provenance, exact lookup, substring search, stats, top values, and multi-value comparison. The only mild ambiguity is between dataset_row and dataset_compare, since both return rows filtered by column values, and between dataset_columns and dataset_provenance, since both mention provenance.
All tool names consistently use the dataset_ prefix and snake_case, which makes the set feel cohesive. However, the suffix style is mixed: some are nouns like columns, provenance, row, and stats, while others are verbs like compare and search.
Seven tools is well within the ideal range for a dataset-focused server. Each tool covers a distinct analytical need without redundancy, so the count feels intentional and appropriately scoped.
The toolset covers schema discovery, provenance, exact row lookup, free-text search, targeted comparison, numeric statistics, and extreme-value ranking. For a read-only dataset querying server, this is a complete and practical surface with no obvious dead ends.