prompteye-mcp

Official

by PROMPTEYE-SP-Z-O-O

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TypeScript

Remote

Read the account behind the key

get_account

Read-only

Retrieve the owner, plan, prompt usage, tracked assistants, and next scan start for the configured PromptEye API key to diagnose access, verify feature coverage, or confirm when fresh figures begin.

Instructions

Who the configured PromptEye API key belongs to, which plan the workspace is on, how many prompts it tracks against its limit, which assistants those prompts are asked on, and when the next run starts. Call this to diagnose a key, to check whether a plan covers a feature before promising it, or to answer when fresh figures will arrive.

nextScanAt is when the run begins, not when it is done: the prompts are put to every assistant and the answers are read back over the tens of minutes that follow, so the figures arrive gradually after that time rather than all at once on it. Say the run has started rather than that the numbers are ready.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description
`id`	Yes
`plan`	Yes	null = no plan assigned.
`email`	Yes
`addons`	Yes
`models`	Yes
`scopes`	Yes
`nextScanAt`	Yes	When the next run starts, not when it finishes, ISO 8601 in UTC; answers land over the hours after it, so figures keep moving.
`promptCount`	Yes	Prompts tracked across the workspace, active or pending; paused prompts are not counted.
`promptLimit`	Yes	Prompts the plan allows in total; the room left is promptLimit minus promptCount.
`scanFrequency`	Yes	How often every active prompt is asked, e.g. daily.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.0.22Oct 2, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": false,
+  "properties": {
+    "addons": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "email": {
+      "type": "string"
+    },
+    "id": {
+      "type": "string"
+    },
+    "models": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "nextScanAt": {
+      "description": "When the next run starts, not when it finishes, ISO 8601 in UTC; answers land over the hours after it, so figures keep moving.",
+      "type": "string"
+    },
+    "plan": {
+      "anyOf": [
+        {
+          "additionalProperties": false,
+          "properties": {
+            "key": {
+              "type": "string"
+            },
+            "name": {
+              "type": "string"
+            }
+          },
+          "required": [
+            "key",
+            "name"
+          ],
+          "type": "object"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "description": "null = no plan assigned."
+    },
+    "promptCount": {
+      "description": "Prompts tracked across the workspace, active or pending; paused prompts are not counted.",
+      "type": "number"
+    },
+    "promptLimit": {
+      "description": "Prompts the plan allows in total; the room left is promptLimit minus promptCount.",
+      "type": "number"
+    },
+    "scanFrequency": {
+      "description": "How often every active prompt is asked, e.g. daily.",
+      "type": "string"
+    },
+    "scopes": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "id",
+    "email",
+    "plan",
+    "addons",
+    "scopes",
+    "promptCount",
+    "promptLimit",
+    "models",
+    "scanFrequency",
+    "nextScanAt"
+  ],
+  "type": "object"
+}

First observedv1.0.5Sep 16, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations only declare readOnlyHint and openWorldHint, so the description carries real extra weight: it explains that nextScanAt is when the run begins, not finishes, and that figures arrive gradually over tens of minutes, with explicit guidance to say the run started rather than results are ready. This is exactly the kind of semantic trait annotations cannot express.

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 opening sentence front-loads the returned fields, and the second paragraph earns its place by clarifying the non-obvious nextScanAt timing. It is slightly dense and list-heavy, but no sentence is wasteful.

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-value documentation is not required, yet the description still adds semantic meaning to the key fields (prompt counts vs limit, next-run timing). For a zero-parameter read tool, nothing an agent needs to call it correctly is missing.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; per the rubric this establishes a baseline of 4. No parameter-level detail is needed or missing.

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 resource (the account behind the configured API key) and enumerates the concrete data returned: key owner, plan, prompt count against limit, assistants, and next run start. This is unmistakably distinct from siblings like get_active_project or list_workspaces, which deal with projects, not the key's account/plan context.

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?

Gives three explicit call scenarios: diagnosing a key, checking plan feature coverage before promising it, and answering when fresh figures arrive. That is strong when-to-use guidance, but it names no alternative tool or exclusion condition (there is no close sibling, so the omission is minor).

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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