Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Autopilot Browser MCP Server search for workflows that can extract product prices from e-commerce sites"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for executing Autopilot Browser workflows via Claude Desktop and other AI agents.
Add this to your Claude Desktop configuration file:
MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"autopilot": {
"command": "npx",
"args": ["-y", "@autopilotbrowser/mcp-server"],
"env": {
"AUTOPILOT_API_KEY": "your_api_key_here"
}
}
}
}Related MCP server: Cloudflare Playwright MCP
Once installed, Claude can:
Search for available workflows by keyword
List all available workflows
Execute workflows with the appropriate inputs
"Show me all available Autopilot workflows"
"Search for web scraping workflows"
"Run the data extraction workflow with URL https://example.com"
Visit Autopilot Browser to sign up and generate your API key.
Workflow Search: Find workflows by keyword
Workflow Execution: Run automated browser workflows
Real-time Results: Get instant feedback from workflow execution
For issues or questions:
Support: nika@autopilotbrowser.com
Execute a specific Autopilot Browser workflow with the provided inputs. Use search_workflows first to find available workflows and their required inputs.
| Name | Required | Description | Default |
|---|---|---|---|
| workflowName | Yes | The name of the workflow to execute | |
| workflowInputs | Yes | Key-value pairs of inputs required by the workflow. The required inputs depend on the specific workflow being executed. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions execution and inputs, it lacks critical details such as whether this is a read-only or destructive operation, what permissions are required, how errors are handled, or what the execution entails (e.g., timeouts, side effects). For a tool that executes workflows with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with two sentences: the first states the purpose, and the second provides usage guidance. Every sentence earns its place, and it's front-loaded with the core functionality, making it efficient and easy to understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of executing workflows with inputs, no annotations, and no output schema, the description is moderately complete. It covers purpose and usage but lacks details on behavioral aspects (e.g., effects, errors) and output expectations. It's adequate as a minimum viable description but has clear gaps for a tool that performs execution.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents both parameters ('workflowName' and 'workflowInputs') with their types and descriptions. The description adds minimal value beyond the schema, mentioning 'provided inputs' but not elaborating on parameter semantics. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Execute a specific Autopilot Browser workflow with the provided inputs.' It specifies the verb ('Execute') and resource ('Autopilot Browser workflow'), though it doesn't explicitly differentiate from its sibling 'search_workflows' beyond mentioning it in usage guidance. The purpose is clear but could be more distinct from the sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines: 'Use search_workflows first to find available workflows and their required inputs.' This clearly indicates when to use this tool (after searching) and references the alternative sibling tool by name, offering a complete workflow for proper usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for available Autopilot Browser workflows by keyword. Returns a list of workflows that match the search term. If no search term is provided, returns all workflows.
| Name | Required | Description | Default |
|---|---|---|---|
| searchTerm | No | Optional keyword to search for specific workflows (e.g., 'scraping', 'automation', 'data extraction') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns a list of workflows, which is basic output information, but it doesn't disclose behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, pagination behavior, or error handling. The description is minimal and lacks rich behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with two sentences: the first states the purpose and basic behavior, and the second clarifies the optional parameter's effect. Every sentence earns its place, and there is no wasted text, making it easy to understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter, no output schema, no annotations), the description is adequate but has clear gaps. It covers the basic purpose and parameter usage, but without annotations or output schema, it lacks details on behavioral traits, return format, and error handling. The description is complete enough for a simple search tool but could be more informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'searchTerm' well-documented as an optional keyword. The description adds marginal value by reinforcing that the search term is optional and providing examples ('e.g., 'scraping', 'automation', 'data extraction''), but it doesn't add significant meaning beyond what the schema already provides. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for available Autopilot Browser workflows by keyword.' It specifies the verb ('search'), resource ('Autopilot Browser workflows'), and scope ('by keyword'). However, it doesn't explicitly differentiate from the sibling tool 'run_workflow' beyond their different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool through its purpose statement, and it provides guidance on the optional search term: 'If no search term is provided, returns all workflows.' However, it lacks explicit guidance on when to use this tool versus the sibling 'run_workflow' or any alternatives, and it doesn't mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
run_workflowsearch_workflowsThe two tools have clearly distinct purposes: search_workflows is for discovery and information retrieval, while run_workflow is for execution. There is no overlap in functionality, and the descriptions explicitly differentiate them with one being preparatory and the other operational.
Both tools follow a consistent verb_noun naming pattern (search_workflows, run_workflow) with snake_case throughout. The naming is predictable and aligns well with their functions, making them easily distinguishable and readable.
With only 2 tools, the server feels thin for a browser automation domain that typically involves more operations like navigation, clicking, or data extraction. While the tools cover basic workflow execution, the limited count suggests significant gaps in functionality for comprehensive browser control.
The tool set is severely incomplete for a browser automation server. It lacks core operations such as page navigation, element interaction, data scraping, or session management. The existing tools only handle workflow search and execution, leaving most browser automation tasks unsupported and likely causing agent failures.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
AI-powered browser automation — navigate, click, fill forms, and extract data from any website.
AI-powered web automation. Navigate websites using AI agents for one page or a thousand
AI-powered web automation. Navigate websites using AI agents for one page or a thousand
Direct access to 40+ scraping and search tools. Extract structured data from Google (Search, Maps, Trends), Amazon, Airbnb, Social Media, and any web page directly into your AI agent.
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