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Dataset columns and shape

dataset_columns

The columns, which of them are numeric, the row count and the provenance banner of the Cmmsly dataset. Call this first to learn the schema.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the behavioral disclosure burden. It implies a read-only, inspection-oriented operation and lists what will be returned. However, it does not explicitly state that the tool has no side effects, does not modify data, or note any permissions or limitations, leaving some safety behaviors implicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. The first sentence enumerates the exact returned information, and the second provides actionable placement in the workflow. Every sentence contributes value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless metadata-discovery tool with no output schema, the description covers the key return values and tells the agent when to invoke it. It is slightly incomplete in not explaining what a 'provenance banner' is or how the returned columns are ordered, but these are minor gaps for a simple schema-inspection tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and the schema is empty, so there is nothing additional the description needs to explain about inputs. The baseline of 4 applies because no parameter semantics are required.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies what the tool provides: columns, numeric flags, row count, and provenance banner for the Cmmsly dataset. It is not a tautology and the 'Call this first to learn the schema' phrase gives it an actionable purpose, though it uses a noun phrase rather than a strong verb like 'returns' or 'gets'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit sequencing guidance: call this first to learn the schema. This helps an agent know when to use it during exploration. It does not explicitly name alternative sibling tools or explain when not to use them, but the 'first' instruction is clear enough for an initial discovery tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation4/5

The seven tools are mostly distinct: schema, provenance, exact-match row lookup, substring search, value-list comparison, numeric stats, and top-N ranking each serve a clear purpose. dataset_row and dataset_compare both filter rows by column values, and dataset_stats/dataset_top both operate on numeric columns, so there is minor potential for confusion, but the descriptions disambiguate them adequately.

Naming Consistency5/5

All tools share a consistent dataset_ prefix and use lowercase snake_case throughout. The second segment is a concise operation or concept (columns, compare, provenance, row, search, stats, top), providing a predictable naming pattern.

Tool Count5/5

Seven tools is well-scoped for a single-dataset exploration server. Each tool addresses a distinct query type without redundancy or bloat.

Completeness5/5

The surface covers the full range of common dataset questions: schema discovery, provenance/citation, exact and fuzzy row retrieval, value comparison, numeric statistics, and ranking. No significant dead ends or missing operations are evident for the stated purpose.

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