dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Reconzy 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 lists what the tool returns but does not mention side effects, error behavior, or performance characteristics. As a simple metadata query, this is adequate but not fully transparent.
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 wasted words. The key information (what is returned and when to use) is front-loaded and immediately useful.
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 no output schema, the description sufficiently explains the return content (columns, numeric flags, row count, provenance banner). It could be slightly more explicit about the format of the return, but for a schema discovery tool it is complete enough.
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 schema coverage is 100% (empty schema), so there are no parameter details to add. The baseline of 3 applies because the description does not need to explain parameters that do not exist.
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 a specific verb ('call this first to learn the schema') and resource ('columns, numeric flags, row count, provenance banner'). It distinguishes itself from sibling tools like dataset_stats or dataset_provenance by focusing on schema shape.
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 tells the agent to call this tool first to learn the schema, which is strong usage guidance. It does not enumerate alternatives or when-not-to-use, but the directive 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.
Tools are mostly distinct, but dataset_row, dataset_search, and dataset_compare have overlapping row-filtering purposes. Descriptions clarify exact match vs. contains vs. any-of, so confusion is unlikely but possible.
All tools share the dataset_ prefix, but the suffix pattern is inconsistent: some are nouns (columns, provenance, row, stats, top) and some are verbs (compare, search). A more uniform verb_noun or noun_only convention would improve predictability.
Seven tools is a well-scoped set for dataset exploration, covering schema, provenance, lookup, search, comparison, statistics, and top-values queries without redundancy or bloat.
The toolset covers the main dataset exploration operations well, but it lacks explicit distinct-value or grouped-aggregation tools, which could be useful for fuller ad-hoc analysis. It is not severely incomplete.