Prompt Lab MCP Server

pull_ui_history

Retrieve logged UI actions from a workspace to see what the user did between agent calls. Each session summary and regression run is automatically recorded.

Instructions

Fetch all history entries the UI has pushed to this workspace.

The UI auto-pushes after every session summary ("Summarize & new") and every regression run. This gives you a record of what the user did in the UI between agent calls.

ALWAYS save the response to a local file: prompt-lab/workspaces//_ui_history.json

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": {
      "workspaceId": "<workspaceId>"
    },
    "name": "pull_ui_history"
  }
}

Input Schema

Name	Required	Description	Default
`workspaceId`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 20, 2026

TDQS

A3.7/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It correctly indicates a read operation ('Fetch') and provides context about UI push events. However, it does not disclose potential behavioral traits such as response format, pagination, rate limits, or required permissions, leaving some gaps.

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 relatively short and front-loaded with the core purpose. Including an actionable instruction to save to a local file adds value but slightly deviates from describing the tool itself. Overall, it is concise with no unnecessary repetition.

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?

Given the simple schema (one parameter, no output schema), the description provides helpful context about UI push events but lacks details about the response structure or potential errors. It is marginally complete for a fetch tool but could be improved.

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?

The input schema has one required parameter (workspaceId) with 0% schema description coverage. The description does not explain the parameter beyond its appearance in the file path instruction. No details about expected format or valid values are provided, so the parameter semantics are weak.

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 clearly states 'Fetch all history entries the UI has pushed to this workspace', using a specific verb (Fetch) and resource (UI history). It distinguishes from sibling tools which include actions like add_test_cases or apply_suggestion, as it uniquely retrieves UI interaction records.

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?

The description explains when the UI pushes history (after session summaries and regression runs), giving context for when to use the tool. It implies the tool is for reviewing user actions between agent calls, but does not explicitly state when not to use it or suggest alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.