prompteye-mcp

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Start a brand analysis run

create_brand_analysis_run

Starts a brand analysis run for the project: PromptEye looks at what the tracked prompts most recently found, works out the topics where a competitor answers better than this brand, and scores how big each gap is.

Instructions

Starting one is instant; the analysis itself takes a little while. The run comes back processing and turns ready once it finishes, or error / corrupted_response if it fails — read it with get_brand_analysis_run until it does.

A run cannot always be started: one already in progress blocks another, and a run that already used the project's current tracking results is not repeated until they change. Call get_brand_analysis_availability first to know whether — and why — one can run; this call is refused for exactly the same reasons. Running an analysis also counts against the workspace's monthly plan quota for brand analyses.

Input Schema

Name	Required	Description	Default
`projectId`	Yes	The project to work on — its id from list_projects. Required on every call, so that parallel conversations never read or write each other's project.

Output Schema

Name	Required	Description
`id`	Yes
`gaps`	Yes	The topics where a competitor answers better than this brand. Empty until ready.
`error`	Yes	Why the run failed. null unless status is error or corrupted_response.
`status`	Yes	processing the moment it is requested, then ready, or error / corrupted_response when it failed — see error.
`createdAt`	Yes	When the run was requested, ISO 8601 in UTC.
`projectId`	Yes
`sentiment`	Yes	How the assistants talk about the brand when they mention it. null until ready.
`totalCost`	Yes	PromptEye's internal AI processing cost in USD, not a charge to the user. null until the run finishes.
`updatedAt`	Yes	When the run last changed, ISO 8601 in UTC.
`maxContextGaps`	Yes	How many gaps this run may report at most.
`usedResultCount`	Yes	How many tracking results fed this run.
`activePromptCount`	Yes	How many active tracked prompts fed this run.

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedv1.0.28Oct 23, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

addedInput schema / properties / projectId

Added value: +{
+  "description": "The project to work on — its id from list_projects. Required on every call, so that parallel conversations never read or write each other's project.",
+  "minLength": 1,
+  "type": "string"
+}

addedInput schema / required
```
Added value: +[
+  "projectId"
+]
```

addedOutput schema / properties / totalCost / description

Added value: +"PromptEye's internal AI processing cost in USD, not a charge to the user. null until the run finishes."

Addedv1.0.22Oct 2, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations only flag non-read-only, non-idempotent, open-world; the description goes much further by describing the async lifecycle (processing → ready / error / corrupted_response), the refusal conditions, and that the run consumes the workspace's monthly plan quota. This is exactly the extra context annotations cannot carry.

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 short paragraphs: what the run does, how its lifecycle behaves and how to poll it, and what blocks it plus quota cost. Front-loaded with the action and no filler sentences; each line carries distinct operational information.

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?

An output schema exists, so return shape need not be restated, and the description still supplies the status semantics an agent needs to interpret it. Combined with the blocking conditions and quota note, nothing material for correct invocation is missing.

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?

There is a single parameter with 100% schema description coverage that already explains projectId and why it is required on every call. The description adds nothing parameter-specific, so the baseline 3 for fully-documented schemas is appropriate.

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 ('Starts a brand analysis run') and immediately explains the substantive operation: analysing recent prompt results, identifying competitor topic gaps, and scoring gap size. This distinguishes it from siblings get_brand_analysis_run (read status) and get_brand_analysis_availability (precheck).

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?

Explicitly directs the agent to call get_brand_analysis_availability first and states this call is refused for exactly the same reasons, plus names get_brand_analysis_run as the polling mechanism. Also discloses when it must not be used: an in-progress run blocks it, and an unchanged tracking result prevents a repeat.

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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