Open-End Response Analyser – coding of open-ended survey answers

Create an analysis project

open_end_create_project

Creates an empty analysis project with a name and optionally the research question and context that guide the coding, and the output language of category names. Next step: open_end_add_responses. Creates nothing billable. Requires OAuth or an API key with projects:write.

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": {
      "name": "<name>"
    },
    "name": "open_end_create_project"
  }
}

Input Schema

Name	Required	Description
`name`	Yes
`description`	No
`idempotency_key`	No	Optional retry key (e.g. a UUID). Repeating the call with the same key and arguments returns the original result instead of writing again. Without it, identical calls within about 10 minutes are deduplicated.
`output_language`	No	Language code for category names, e.g. 'en', 'cs', 'de'. Defaults to the responses' language.
`research_context`	No
`research_question`	No	The survey question the responses answer.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 10, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, and openWorldHint=false, so the description's job is to add context beyond that — and it does: "Creates nothing billable" and "Requires OAuth or an API key with projects:write" are operationally important facts not present in the annotations. It omits what is returned (project identifier) and how duplicate project names are handled.

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?

Front-loaded with the core action, followed by three short, high-value sentences covering workflow, cost, and auth. No filler, though the first sentence is dense enough that the optional-field list could be split for readability.

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

Completeness4/5

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

With six parameters, no output schema, and a mutation that has downstream steps, the definition covers purpose, sequencing, cost, and authentication adequately. The remaining gap is the return value — without an output schema, stating that it returns the new project's ID would complete the picture for an agent that needs to chain into open_end_add_responses.

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?

Schema coverage is 50%, so the description must compensate, and it does for the semantically ambiguous fields: it clarifies that the research question and context "guide the coding" and that output_language governs "category names." The idempotency_key and plain description parameters are left entirely to the schema, which is acceptable since the schema documents them well.

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 ("Creates an empty analysis project") and enumerates the optional inputs it accepts (research question, context, output language). It also names the follow-up tool, so the agent can place it in the workflow without inspecting siblings.

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?

"Next step: open_end_add_responses" gives an explicit ordering constraint that tells the agent this is the entry point of the pipeline. It does not state when-not to use it (e.g. if a project already exists, use open_end_list_projects), so it stops short of full routing guidance.

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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