dataset_topThe highest (or lowest) rows of the Tachovo dataset by a numeric column — "which is the most/least X".
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| column | Yes | ||
| ascending | No | true for the lowest first; default highest first |
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 burden of disclosing behavior. It conveys that the tool returns a subset of rows sorted by a numeric column and can be ordered highest or lowest. However, it does not explicitly state that the operation is read-only, describe the output structure, or address ties and non-numeric columns.
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 a single, front-loaded sentence that immediately states the behavior and includes a memorable query exemplar. It contains no filler and does not repeat schema details, earning full credit for conciseness and structure.
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 simple ranking tool, the description plus schema covers the core invocation needs: column, limit, and ascending. But with no output schema and no annotations, some operational details remain unspecified, such as the exact return format, default limit behavior, and error cases, leaving it slightly incomplete.
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?
Schema description coverage is only 33%, so the description should compensate. It adds meaning for 'column' by specifying it must be numeric and for 'ascending' via 'highest (or lowest)'. The 'limit' parameter is not elaborated, but its name and schema min/max make it reasonably clear, leaving the added value partial.
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 what the tool does: it returns the highest or lowest rows of the dataset ranked by a numeric column. The example 'which is the most/least X' makes the tool's purpose unambiguous and distinguishes it from siblings like dataset_stats or 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 'which is the most/least X' gives a clear usage context for when to select this tool. It does not explicitly mention alternatives or exclusions, but the ranking-by-numeric-column framing is enough to separate it from the statistical, search, and row-lookup siblings.
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 lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The only possible confusion is between dataset_row and dataset_compare, since both match column values exactly, but the descriptions clarify single vs. multiple values.
All tool names share the dataset_ prefix and follow a consistent noun/feature pattern: columns, compare, provenance, row, search, stats, top. The convention is uniform and predictable.
Seven tools is well-scoped for exploring a single dataset. Each tool covers a distinct query mode or metadata need without redundancy or bloat.
The surface covers schema discovery, provenance attribution, exact matching, free-text search, multi-value comparisons, numeric statistics, and extremes. For a read-only dataset access server, this is a complete and practical toolkit with no obvious dead ends.