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PROMPTEYE-SP-Z-O-O

prompteye-mcp

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by PROMPTEYE-SP-Z-O-O
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TypeScript
Remote

Add prompts by hand (not the recommended way)

add_prompts

Add user-supplied prompts verbatim to a project for tracking, creating groups as needed.

Instructions

Tracks prompts written by hand in the active project, in one call.

This is not the recommended way to add prompts, and it should not be the first thing you reach for. PromptEye generates the prompts a project tracks: it works out which questions carry demand and phrases them the way people actually put questions to AI assistants, then proposes each one with the gap in the funnel it fills, the demand behind it, how close to a purchase it is asked and how well it fits the brand. list_prompt_suggestions returns those, ready to be accepted. Call list_prompt_suggestions and work from what it returns.

A prompt added here skips all of that. It is not weighed against what the project already tracks, so it can duplicate an existing prompt; it carries no demand, priority, purchase intent or fit until PromptEye computes them; and a question phrased the way a person writes rather than the way people actually ask assistants will quietly measure nothing — it will sit in the project at 0% visibility and look like a brand problem when it is a prompt problem. Every prompt also counts against the workspace plan.

Groups are handled by name: a groupName that does not exist yet is created, and one that does is reused, so there is no separate group-creation step.

Use it only when the user has prompts of their own that must be tracked verbatim — migrating from another tool, or a list a client insists on — and has said as much. If the user simply wants more prompts, or better coverage, use list_prompt_suggestions instead. When unsure, ask the user before calling this; do not decide on their behalf.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptsYesThe prompts to track, at most 200. Send them in one call rather than one call per prompt.
confirmBypassPromptIntelligenceYesMust be true, and only set it once the user has knowingly chosen hand-written prompts over the ones PromptEye would generate. It is an acknowledgement that this call skips the demand, duplicate and fit checks behind list_prompt_suggestions.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.11

TDQS

A4.8/5.0
Behavior5/5

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

The description goes well beyond the annotations. It discloses that hand-written prompts skip demand/duplicate/fit checks, can duplicate existing prompts, carry no demand/priority/purchase intent/fit until PromptEye computes them, may sit at 0% visibility, and count against the workspace plan. It also explains group-by-name behavior (created vs reused). This is rich behavioral context that annotations alone do not provide.

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 longer than average, but every section earns its place: the warning is front-loaded, the alternative is named early, the consequences are concrete, and the usage conditions are explicit. It is structured with clear paragraphs and bolded emphasis. Slightly verbose in the middle section, but not padded.

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?

For a mutation tool with no output schema, the description is complete: it explains the operation, the caveats, the group behavior, the plan impact, and the exact conditions for use. An agent has everything needed to decide whether to call it and what to expect. The absence of an output schema is compensated by the detailed behavioral disclosure.

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 100%, so the schema already documents both parameters thoroughly. The description adds value by explaining the consequences of the confirmBypassPromptIntelligence flag (acknowledgement of skipping checks) and the groupName behavior (created/reused, no separate step). It reinforces the schema's meaning without repeating it verbatim, though the schema already carries most of the semantic weight.

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 ('Tracks prompts written by hand in the active project, in one call') and immediately distinguishes it from the recommended alternative, list_prompt_suggestions. It clearly identifies what the tool does and what it is not, making sibling differentiation explicit.

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?

The description gives explicit when-to-use guidance: only when the user has prompts of their own that must be tracked verbatim (e.g., migrating from another tool or a client insists), and says to ask the user when unsure. It also names the alternative (list_prompt_suggestions) and the condition for using it instead, providing clear exclusions.

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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