dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Eposvo 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, the description must carry the behavioral burden. It discloses the returned content (columns, numeric flags, row count, provenance banner), which implies a read-only metadata inspection, but it does not explicitly state that it has no side effects, requires no special permissions, or what happens on failure. The disclosure is adequate but leaves some implicit assumptions.
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 short sentences: the first front-loads the exact information returned, and the second gives a clear call-order instruction. There is no redundant phrasing or filler.
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 metadata tool with no output schema, the description adequately covers what the agent will receive and when to call it. It is complete enough to invoke correctly, though it could slightly benefit from stating that it is a read-only operation or hinting at the output shape.
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 input schema is empty and has 0 parameters, so there are no parameter semantics for the description to clarify. The description reinforces that invocation is simple and unconditional, matching the baseline of 4 for a zero-parameter tool.
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 enumerates what the tool returns—columns, numeric indicators, row count, and provenance banner for the Eposvo dataset—so an agent can tell this is a schema/shape discovery tool. It lacks an explicit action verb like 'get' or 'list', and it does not directly contrast with sibling tools, but 'Call this first to learn the schema' makes its purpose unambiguous.
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?
'Call this first to learn the schema' gives explicit guidance about when to invoke this tool relative to others. It does not name what alternative to use when schema knowledge is not needed, but the ordering and purpose are clear.
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, list comparison, summary stats, and top/bottom rows. There is mild overlap between dataset_compare and dataset_row since both return exact matches, but their use cases are described distinctly enough.
All tool names follow a consistent dataset_<operation> pattern with lowercase snake_case. While the suffixes mix nouns and verbs, the pattern is uniform and predictable across the entire set.
Seven tools is well-scoped for a single-dataset querying server. Each tool covers a distinct common query pattern without unnecessary bloat or duplication.
The tools provide complete read-only coverage for exploring and reporting on a dataset: schema discovery, provenance, exact lookup, substring search, multi-value comparisons, numeric summaries, and top/bottom ranking. No major query pattern needed for this domain is missing.