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datatomcp

by com.datatomcp
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Distinct values

distinct_values
Read-onlyIdempotent

Most frequent values of a column with counts. Use it to get exact spellings (e.g. "France" vs "FR") before filtering.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>",
      "column": "<column>"
    },
    "name": "distinct_values"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
whereNo
columnYes
versionNoThe dataset version from an earlier result's meta (or one listed in describe_dataset's versions). Pass it when you chain calls (distinct_values, then aggregate): the answer stays on that version even if the data was refreshed in between (meta then has stale: true and current_version). If that version is no longer kept, the call fails with source_changed.
dataset_idYesDataset id from list_datasets, e.g. ds_ab12cd34ef56

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds that results are frequency-ranked and carry counts, but says nothing about how many values come back by default, truncation at the limit cap, or what happens for high-cardinality columns.

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?

Two short sentences, zero waste, with the core output statement front-loaded and the practical tip second. Nothing needs trimming.

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

Completeness3/5

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

There is no output schema, so the description should sketch the return shape; "values with counts" gestures at it but leaves ordering, default limit, and truncation behavior unstated. Given the filter parameter and version-chaining semantics in the schema, a caller still has to open the schema to use this correctly.

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

Parameters2/5

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

With five parameters and only 40% schema description coverage, the description carries real burden here and contributes nothing: no mention of column, limit, where, or version. Two parameters (limit, column) are undocumented in both the schema and the description, and the version chaining guidance exists only in the schema.

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 names a specific operation and output shape: most frequent values of a column, with counts. That is enough to separate it from aggregate or query_rows at a glance, though it does not explicitly contrast itself with those siblings.

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

Usage Guidelines3/5

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

It gives one concrete when-to-use scenario - checking exact spellings like "France" vs "FR" before filtering - which is genuinely helpful. It offers no when-not guidance and never names the sibling tools (aggregate, query_rows) that a caller might pick instead.

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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