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create_study

Create user interview studies to gather research insights, generate shareable interview links, and optionally include visual stimuli like images or Figma prototypes for participants.

Instructions

Creates a user interview study and returns an interview_link to share with participants. Starts with 1 interview slot. Optionally include study_media to show an image or Figma prototype during the interview.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
key_research_goalYes
business_contextYes
additional_context_promptNo
languageNo
duration_minutesNo
metadataNo
study_mediaNoVisual stimulus shown during all interview questions (web participants only)

Implementation Reference

  • The `create_study` tool is registered using `server.tool` and includes both the schema definition and the async handler function that calls the Usercall API.
    server.tool(
      "create_study",
      "Creates a user interview study and returns an interview_link to share with participants. Starts with 1 interview slot. Optionally include study_media to show an image or Figma prototype during the interview.",
      {
        key_research_goal: z.string(),
        business_context: z.string(),
        additional_context_prompt: z.string().optional(),
        language: z.enum(["auto", "en"]).optional(),
        duration_minutes: z.number().int().positive().optional(),
        metadata: z.record(z.string(), z.unknown()).optional(),
        study_media: z
          .object({
            type: z
              .enum(["image", "prototype"])
              .describe(
                "Media type: 'image' for direct image URLs (.png, .jpg, .gif, .webp) or 'prototype' for Figma prototype URLs",
              ),
            url: z
              .string()
              .url()
              .describe("Public URL to the image or Figma prototype"),
            description: z
              .string()
              .max(500)
              .optional()
              .describe("Alt text / context shown to participants"),
          })
          .optional()
          .describe(
            "Visual stimulus shown during all interview questions (web participants only)",
          ),
      },
      async (input) => {
        const payload = await callUsercallApi("/api/v1/agent/studies", {
          method: "POST",
          body: JSON.stringify({ ...input, target_interviews: 1 }),
        });
    
        const note = input.study_media
          ? "Study created with 1 interview slot and media attachment. Share the interview_link with 1 participant (media visible on web only). Use update_study to add more slots."
          : "Study created with 1 interview slot. Share the interview_link with 1 participant. Use update_study to add more slots.";
    
        return result(appendNote(payload, note));
      },
    );
Behavior4/5

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

No annotations provided, so description carries full disclosure burden. Successfully discloses return value ('interview_link'), side effect ('Starts with 1 interview slot'), and optional media behavior. Missing: auth requirements, rate limits, or error conditions (e.g., duplicate study names).

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 efficient sentences. First establishes core operation and return value; second covers default slot count and key optional feature. No redundant phrases or tautology despite null title.

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?

Explains return value (interview_link) compensating for missing output schema, and covers the nested study_media object. However, for 7 parameters with 86% lacking schema descriptions, description omits explanation of required parameters' semantics and relationships between context / research goals.

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 14% (only study_media described at top level). Description mentions study_media functionality but fails to compensate for 6 undocumented parameters including required fields (key_research_goal, business_context). Does not explain what content belongs in research goals vs business context.

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?

Specific verb ('Creates') + specific resource ('user interview study') clearly identifies the operation. Implicitly distinguishes from siblings delete_study/update_study through the creation verb, and from get_* tools by specifying it returns an interview_link.

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?

Implies creation use case through verb choice and mentions it 'Starts with 1 interview slot,' suggesting initialization behavior. However, lacks explicit guidance on when to use update_study instead, or prerequisites for creation (e.g., workspace requirements).

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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