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Field values

get_field_values
Read-only

List the distinct values of a field.

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": "<dataset>",
      "field": "<field>"
    },
    "name": "get_field_values"
  }
}

Input Schema

NameRequiredDescriptionDefault
fieldYes
datasetYesA dataset name.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / dataset / description
      Previous value: -"Dataset name, from list_datasets."New value: +"A dataset name."
  2. First observed

TDQS

C2.7/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds only the word 'distinct', with no disclosure of cardinality limits, ordering, sampling, or how many values are returned — meaningful gaps for a lookup tool without an output schema.

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

Conciseness4/5

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

One front-loaded sentence with no wasted words. The terseness is efficient rather than padded, though the single line leaves little room for the context noted above.

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

Completeness2/5

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

With no output schema and minimal annotations, the description should explain the shape of the result (list of strings? bounded?) and any scope limits. Those are absent, so an agent cannot predict what it gets back.

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?

Schema coverage is 50%: 'dataset' is documented but 'field' is not. The description mentions both only by name and adds no format, qualification, or naming-convention detail that would help the agent resolve an ambiguous field reference.

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 gives a specific verb and resource — 'List the distinct values of a field' — which is clearly different from siblings like get_schema (structure) or note_field (writes). It stops short of naming which sibling performs the adjacent job, so it stays at 4 rather than 5.

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

Usage Guidelines2/5

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

No when-to-use guidance, no prerequisites, and no mention of alternatives such as get_schema or run_query for agents that need values conditionally. The agent must infer the scenario from the name alone.

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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