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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 RemitDeck 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

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It states what information is returned, which is useful, but it does not explicitly say whether the operation is read-only, whether there are side effects, or how results are structured. Adequate but not rich.

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?

A single, tightly written sentence that front-loads the returned fields and then gives a decisive usage directive. Every word earns its place; there is zero waste.

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

Completeness5/5

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

For a no-parameter schema-introspection tool with no output schema, the description fully explains what the agent will receive and when to call it. Nothing an agent needs to invoke it correctly is missing.

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, so the schema already carries no burden. The baseline of 4 applies, and the description appropriately adds no parameter-level detail because none is needed.

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 the tool's output: columns, numeric flags, row count, and provenance banner for the RemitDeck dataset. It lacks an explicit verb but is unambiguous about the resource and what it returns, and 'Call this first' distinguishes it from the sibling tools.

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 explicitly instructs the agent to call this tool first to learn the schema, providing clear context for when to use it. It does not mention exclusions or alternatives, but the directive is strong and actionable.

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

dataset_row, dataset_search, and dataset_compare all return rows and can overlap when querying by a simple value, but each has a distinct mode: exact match, substring, and multi-value ordered comparison. Schema and provenance tools are clearly separated from row-level queries.

Naming Consistency5/5

All tools use the same dataset_ prefix and snake_case style, creating a predictable and recognizable family. The second element mixes nouns and verbs, but the pattern is consistent enough to cause no confusion.

Tool Count5/5

Seven tools is well-scoped for a read-only dataset exploration server. Each tool covers a distinct need (schema, provenance, filtering, comparison, statistics, ranking) without unnecessary bloat.

Completeness4/5

The surface covers schema discovery, provenance, exact/substring filtering, multi-value comparison, numeric stats, and top/bottom ranking. Missing features like group-by or distinct-value queries are minor gaps that can often be worked around with existing tools.

Resources