dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Keysvo 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?
There are no annotations, so the description carries the burden of disclosing behavior. It implies a read-only metadata operation by listing returned information, but it does not explicitly state that no data is modified or that no side effects occur.
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 sentences and directly states the return contents and when to call. There is no extraneous detail or redundancy.
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 zero-parameter tool with no output schema, the description lists all key outputs and provides usage context. It is sufficient for an agent to decide when and why to invoke this tool.
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 no parameters and the schema description coverage is 100%, so the baseline is high. No parameter explanation is needed, and the description correctly focuses on the output.
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 the dataset's columns, numeric flags, row count, and provenance banner, and it is positioned as the first call to learn the schema. This distinguishes it from sibling tools like dataset_compare and dataset_search.
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 phrase 'Call this first to learn the schema' gives explicit temporal guidance, implying it should be used before other dataset operations. It does not explicitly name alternatives, but the instruction to call first provides sufficient context.
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 targets a distinct query mode: schema, metadata, exact match, substring search, multi-value comparison, statistics, and top/bottom ordering. There is minor overlap between dataset_compare and dataset_row since both filter on column values, but their descriptions clearly separate multi-value ordered lookups from single exact matches.
All tools share the consistent dataset_ prefix and snake_case format, which makes the family easy to recognize. However, the suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the pattern is consistent in prefix but not perfectly uniform in part of speech.
Seven tools is well-scoped for a dataset query server. Each tool covers a distinct useful operation without redundancy or bloat, and the count feels appropriate for the stated purpose.
The toolset covers the full read-only dataset lifecycle: schema discovery, provenance, exact lookup, fuzzy search, value comparison, numeric statistics, and top/bottom ranking. There are no obvious dead ends for common dataset questions, and no missing operations seem necessary for the domain.