dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the DailyLogDesk 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 full burden of behavioral disclosure. It does disclose the exact information returned, including the provenance banner, but it never explicitly states that the tool is read-only or side-effect free. For a zero-parameter schema inspection tool, this is a minor but notable gap.
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?
Two short sentences with no wasted words. The return contents are front-loaded and the usage guidance is placed clearly in the second sentence.
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 that there is no output schema, the description does a good job naming the key return components: columns, numeric identifiers, row count, and provenance banner. It could be slightly more explicit about the shape of the response, but it is complete enough for an agent to decide whether to call it.
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 and the schema coverage is 100%, so the description has no parameter semantics to add. Per the baseline for parameterless tools, this is adequate.
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 states the tool returns column names, numeric flags, row count, and the provenance banner for the DailyLogDesk dataset, so an agent knows what resource and scope are involved. It does not explicitly differentiate from siblings like dataset_provenance or dataset_stats, but the phrase 'Call this first to learn the schema' gives it a distinct role.
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 context: call this tool first to learn the schema. It does not mention when not to use it or point to alternatives, so it falls short of the highest bar, but the guidance is unambiguous and useful.
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 stated query mode, but several return rows from the same dataset with overlapping semantics (exact match, substring search, multi-value compare, top-N). An agent could sometimes confuse dataset_row and dataset_search, though the descriptions provide enough detail to disambiguate.
All tools follow a consistent dataset_ prefix pattern with short, descriptive operation names. The naming convention is uniform and predictable across the entire set.
Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct access pattern without unnecessary duplication or bloat.
The toolset covers schema discovery, provenance attribution, exact row lookup, substring search, value comparison, numeric statistics, and top/bottom ranking. For a read-only dataset querying server, this is a complete surface with no obvious dead ends.