dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Monthendly 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. It discloses what information is returned and that it is an initial schema-learning call, but it does not explicitly state that the operation is read-only or describe any side effects, cost, or response format. The 0-parameter, metadata-read nature makes this gap less severe.
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 sentences with no filler. The output contents are listed first, and the actionable instruction 'Call this first' is placed in the second sentence. Every word earns its place.
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?
With zero parameters and no output schema, the description adequately covers the returned information and the intended call order. The only minor gap is that it does not specify the exact representation of numeric flags or provenance banner, but for a simple schema-discovery tool this is not critical.
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 and schema coverage is effectively complete, so the baseline is 4. The description adds value by enumerating the output fields (columns, numeric indicators, row count, provenance banner), which helps the agent know what to expect.
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 names the exact deliverable: columns, numeric flags, row count, and provenance banner for the Monthendly dataset. It also positions the tool as the schema-discovery entry point, which distinguishes it from sibling tools like dataset_row or dataset_stats.
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?
It explicitly says 'Call this first to learn the schema,' giving clear when-to-use guidance. However, it does not mention when not to use it or name alternatives, and the provenance banner overlap with dataset_provenance is not addressed.
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 distinct purpose (schema, provenance, exact match, substring search, multi-value compare, stats, top/bottom), and descriptions clearly differentiate them. The only mild overlap is between dataset_row and dataset_compare, but the exact-match vs multi-value distinction is explicit enough to avoid serious confusion.
All tools share a consistent 'dataset_' prefix in snake_case, which creates a clear family identity. However, suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the pattern is not perfectly uniform verb_noun.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct query pattern without redundancy or bloat, and the count sits comfortably within the ideal 3–15 range.
The tool surface covers the core needs for working with a dataset: schema discovery, provenance, exact lookup, full-text search, comparisons, numeric aggregates, and top/bottom ordering. Minor gaps exist, such as no dedicated count-by-filter or multi-column filtering, but agents can work around these using existing tools.