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List a list's columns for inputs

get_investigation_inputs
Read-only

Lists the columns of a list with example values, for the choice of which column feeds each input.

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": {
      "collection_id": "<collection_id>"
    },
    "name": "get_investigation_inputs"
  }
}

Input Schema

NameRequiredDescriptionDefault
collection_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered by structured data. The description adds that returned entries include example values, which is useful behavioral context, but says nothing about ordering, pagination, or what happens when collection_id points to an empty or non-existent list.

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?

A single sentence that front-loads the resource and includes the purpose without padding. It is efficient, though the compound phrasing "of a list with example values, for the choice of..." is slightly tangled.

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?

For a simple read-only, one-parameter tool with annotations covering safety, the description is serviceable but incomplete: it never clarifies the relationship between the collection_id argument and the "list" it operates on, and with no output schema it gives only a partial hint at the return shape (columns plus example values).

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 description coverage is 0%, so the description must carry the burden of explaining collection_id — and it does not, referring only to "a list." The vocabulary mismatch between "list" in the description and "collection_id" in the schema leaves the single required parameter unexplained.

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 uses a specific verb and resource ("lists the columns of a list with example values") and states the decision it supports (choosing which column feeds each input). It does not differentiate against near siblings such as get_connection_columns or get_investigation_parameters, and the wording "a list" is loose against the tool's own collection_id parameter.

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?

The clause "for the choice of which column feeds each input" implies the usage context, so an agent can infer this is a pre-selection lookup step. However, there is no explicit when-to-use statement, no named alternative, and no indication of what to do if the desired column is absent.

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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