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SurveysAI Qualitative Analysis – interview and focus group analysis

List analysis deliverables

qualitative_list_deliverables
Read-onlyIdempotent

Lists the deliverables of one analysis (reports, codebook, evidence matrix and other outputs) with their status and available formats. Requires an API key with the runs:read scope. Use it after qualitative_get_run shows deliverables as ready, then fetch the content with qualitative_get_deliverable. It does not return file content.

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": {
      "run_id": "<run_id>"
    },
    "name": "qualitative_list_deliverables"
  }
}

Input Schema

NameRequiredDescriptionDefault
run_idYesThe analysis id (`run_id`) from qualitative_list_runs or qualitative_start_analysis.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds non-obvious behavioral facts the annotations cannot convey: the runs:read scope requirement and the fact that content is not returned, only metadata and formats. It stops short of describing result size or ordering, which keeps it from a 5.

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?

Three compact sentences: what it returns, what it requires, then the workflow and boundary. The exclusion of file content is placed last as a clear boundary rather than buried, and no sentence is redundant.

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?

There is no output schema, so the description carries the return-shape burden and does so by naming status and available formats as the returned fields. Combined with the auth scope, the readiness precondition and the content boundary, an agent has everything needed to call it correctly and route to the follow-up tool.

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

Parameters3/5

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

Schema description coverage is 100% and the single run_id parameter is fully documented in the schema, including its origin (qualitative_list_runs or qualitative_start_analysis). The description only implies that the id scopes to 'one analysis,' so the schema does the heavy lifting and baseline 3 applies.

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

Purpose5/5

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

States a specific verb and resource ('Lists the deliverables of one analysis'), enumerates representative contents (reports, codebook, evidence matrix), and explicitly bounds scope with 'It does not return file content,' which separates it from qualitative_get_deliverable.

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

Usage Guidelines5/5

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

Gives an explicit ordering rule: call after qualitative_get_run shows deliverables ready, then retrieve content via qualitative_get_deliverable. Both the precondition and the next-step alternative are named, leaving nothing to infer.

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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