web-automation-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| web_automation_queryA | Process natural language queries to interact with ChatGPT, Gemini, or Cloud Desktop. Examples: "Ask ChatGPT about quantum computing", "Send to Gemini: explain machine learning", "Clear ChatGPT conversation" |
| send_to_serviceA | Send input to Cloud Desktop, ChatGPT, or Gemini and get response |
| get_latest_responseC | Get the latest response from a service |
| clear_conversationB | Clear the conversation history in a service |
| initialize_browserA | Initialize the browser (use headless: false to see the browser) |
| cleanup_browserA | Close all browser windows and clean up resources |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
web_automation_query overlaps heavily with send_to_service and also covers clearing conversations, making it unclear when to use which tool. Other tools are distinct, but the primary query tool's broad scope creates ambiguity.
Most tools follow a consistent verb_noun pattern (send_to_service, get_latest_response, clear_conversation, initialize_browser, cleanup_browser), but web_automation_query breaks the pattern as a noun_phrase, causing a minor inconsistency.
With 6 tools, the set is well-scoped for browser automation covering initialization, querying, response retrieval, conversation clearing, and cleanup. Each tool has a clear place in the workflow, and the count is appropriate.
The tool set covers the core lifecycle: initialize, send query, get response, clear conversation, and cleanup. Minor gaps exist (e.g., listing available services or fetching full conversation history), but they are not critical for the primary purpose.