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Unilytics MCP Server

Unilytics MCP Server

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by TeamUnilytics
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JavaScript
Remote

add_tracked_prompts

Idempotent

Add AEO prompts to track for a project and submit question texts for AI answer engines, with optional topics, tags, and prompt types.

Instructions

Add AEO prompts to track for a project (questions sent to AI answer engines).

EXPECTED SHAPE — each prompt is an object with at minimum a `text` field:
    {"text": "best managed file transfer tools", "topic": "MFT",
     "tags": ["mft"], "prompt_type": "category"}
Only `text` is required. Optional: topic, tags (list or comma-string),
prompt_type ("brand"|"category", default "category"), regions, language,
personas, platforms, analysis_types.

A sheet or CSV maps onto this shape column by column (e.g. "Prompt" -> text, "Topic" -> topic,
"Tags" -> tags). Invalid rows come back as a structured error naming each problem.

Args:
    project_id: The project UUID (from list_projects or create_project).
    prompts: List of prompt objects (see shape above).

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": {
      "project_id": "<project_id>",
      "prompts": "<prompts>"
    },
    "name": "add_tracked_prompts"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptsYes
project_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.1.0
    • removedInput schema / properties / config
      Removed value: -{
-  "default": null,
-  "title": "config",
-  "type": "string"
-}
  2. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare that this is non-read-only, idempotent, non-destructive, and not open-world. The description adds meaningful behavior beyond that by saying invalid rows return a structured error naming each problem, though it does not describe the successful response shape.

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?

The description is front-loaded with purpose and then organizes the expected prompt shape, optional fields, CSV mapping, and arguments clearly. It is somewhat long, but the detail is justified by the nested prompt object and the weak schema documentation.

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?

For a two-parameter tool with a nested prompt array, no output schema, and 0% schema description coverage, the description provides nearly everything needed to construct valid calls. The main remaining gap is that it does not describe the successful return value or duplicate-handling behavior beyond the idempotent annotation.

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

Parameters5/5

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 parameter meaning, and it does: it documents the required text field, all optional prompt fields, allowed prompt_type values and default, and the source of project_id. The CSV/sheet column-mapping note adds practical semantics for list inputs.

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?

The description states a specific verb and resource: add AEO prompts to track for a project, with a parenthetical clarifying that these are questions sent to AI answer engines. This clearly distinguishes it from sibling tools such as add_tracked_keywords.

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?

It gives useful prerequisite context by saying project_id comes from list_projects or create_project, and it explains the prompt input shape. However, it does not explicitly say when to use this tool versus alternatives such as aeo_generate_prompts or add_tracked_keywords, so usage guidance is adequate but incomplete.

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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