Crafting Researcher.yml: Web Research & Automation Guide

by Alex Johnson 57 views

Let's dive into creating a researcher.yml file, similar to the claude.yml or engineer.yml configurations, tailored for in-depth web research and automation. This configuration will enable the system to search the web, conduct thorough research, interact with Playwright MCP servers, and utilize Hyperbrowser MCP servers. Ultimately, it will commit its findings to a dedicated research_findings folder as markdown files. This process avoids the need for pull requests, allowing direct commits to the main branch upon task completion.

Understanding the Core Components

Before we begin, it's essential to understand the key components involved in this researcher.yml setup. The main objective is to automate the process of information gathering, analysis, and documentation. Therefore, the configuration should seamlessly integrate web searching capabilities, interaction with MCP servers for dynamic content handling, and a structured output mechanism.

  • Web Searching: This involves leveraging search engines to gather relevant information based on specified queries. The configuration needs to define how the system formulates search queries, navigates search results, and extracts pertinent data from web pages.
  • Playwright MCP Server Interaction: Playwright is a powerful automation library that enables interaction with web pages as a real user would. The MCP (Managed Control Plane) server enhances Playwright's capabilities by providing a centralized control point. The researcher.yml file must specify how to connect to and interact with the Playwright MCP server, allowing for dynamic content rendering and data extraction.
  • Hyperbrowser MCP Server Utilization: Similar to Playwright, Hyperbrowser provides web automation capabilities. The researcher.yml file needs to detail how to utilize the Hyperbrowser MCP server to perform tasks such as navigating complex websites, filling out forms, and extracting data from dynamic elements.
  • Research Findings Output: All gathered information, analyses, and insights should be systematically documented in markdown files within the research_findings folder. The configuration needs to define the structure and format of these files, ensuring clarity and organization.

Setting Up the researcher.yml Configuration

Creating a robust researcher.yml configuration requires careful consideration of each component's settings and their interactions. Here’s a step-by-step guide to setting up the file:

1. Defining the Basic Structure

Start by defining the basic structure of the researcher.yml file. This includes specifying the name of the researcher, its description, and the entry point for the research process.

name: WebResearcher
description: A researcher that searches the web, interacts with MCP servers, and documents findings.
entry_point: research_task

2. Configuring Web Searching

Next, configure the web searching capabilities. This involves specifying the search engine to use, the API keys (if required), and the parameters for search queries. For example, you might use Google Search with a custom search engine ID and API key.

web_search:
  engine: google
  api_key: YOUR_GOOGLE_API_KEY
  cse_id: YOUR_CUSTOM_SEARCH_ENGINE_ID
  max_results: 5
  • Search Engine Selection: Choose the appropriate search engine based on your requirements. Google, Bing, and DuckDuckGo are popular options.
  • API Keys: Obtain the necessary API keys from the search engine provider. These keys are required to access the search engine's API.
  • Custom Search Engine ID: If using Google, create a custom search engine to narrow down the search scope.
  • Maximum Results: Specify the maximum number of search results to retrieve for each query.

3. Integrating Playwright MCP Server

To interact with dynamic web content, integrate the Playwright MCP server. This involves specifying the server address, authentication credentials, and any custom configurations.

playwright_mcp:
  server_address: http://playwright-mcp.example.com
  api_key: YOUR_PLAYWRIGHT_MCP_API_KEY
  timeout: 60  # seconds
  • Server Address: Provide the address of the Playwright MCP server.
  • API Key: Include the API key for authenticating with the server.
  • Timeout: Set a timeout value for Playwright operations to prevent indefinite waiting.

4. Utilizing Hyperbrowser MCP Server

Similarly, configure the Hyperbrowser MCP server for web automation tasks. Specify the server address, authentication credentials, and any specific settings.

hyperbrowser_mcp:
  server_address: http://hyperbrowser-mcp.example.com
  api_key: YOUR_HYPERBROWSER_MCP_API_KEY
  timeout: 60  # seconds
  • Server Address: Enter the address of the Hyperbrowser MCP server.
  • API Key: Include the API key for authenticating with the server.
  • Timeout: Set a timeout value for Hyperbrowser operations.

5. Defining Research Tasks

Define the specific research tasks that the researcher should perform. These tasks can include searching for information, extracting data from web pages, and analyzing the gathered data.

research_task:
  - action: search_web
    query: "Configuration for Playwright MCP server"
  - action: extract_data
    from: playwright_mcp
    selector: "#content"
  - action: search_web
    query: "Configuration for Hyperbrowser MCP server"
  - action: extract_data
    from: hyperbrowser_mcp
    selector: "#content"
  - action: analyze_data
    input: "extracted_data"
  - action: document_findings
    output_path: "research_findings/findings.md"
  • Search Web: Specifies a web search action with a given query.
  • Extract Data: Extracts data from a specified source (e.g., Playwright MCP server) using a CSS selector.
  • Analyze Data: Performs data analysis on the extracted information.
  • Document Findings: Documents the findings in a markdown file.

6. Configuring Output and Documentation

Configure the output and documentation process to ensure that research findings are properly organized and documented. This includes specifying the output directory, file format, and documentation templates.

output:
  directory: research_findings
  format: markdown
  template: |  # Markdown template
    # Research Findings

    ## Summary
    {summary}

    ## Details
    {details}
  • Directory: Specifies the output directory for research findings.
  • Format: Sets the file format to markdown.
  • Template: Defines a template for structuring the markdown files.

Detailed Configuration and Usage

To effectively use the researcher.yml configuration, you need to understand how each component interacts and contributes to the overall research process. Let’s delve deeper into each section.

Web Searching Configuration Details

When configuring web searching, it’s important to refine the search queries and parameters to obtain the most relevant results. Here are some best practices:

  • Query Formulation: Craft precise and specific search queries. Use keywords, phrases, and Boolean operators to narrow down the search scope. For example, instead of searching for “MCP server,” use “Configuration settings for Playwright MCP server.”
  • Search Parameters: Utilize search engine-specific parameters to filter results. For instance, use the site: operator to limit search results to a specific domain or the filetype: operator to search for specific file types.
  • Error Handling: Implement error handling to manage cases where the search engine API returns errors or when no results are found. This ensures that the research process continues smoothly even when encountering issues.

Playwright and Hyperbrowser MCP Server Interaction

Interacting with Playwright and Hyperbrowser MCP servers involves programmatically controlling web browsers to perform tasks such as navigating web pages, filling out forms, and extracting data. Here are some considerations:

  • Authentication: Ensure that the researcher has the necessary credentials to access the MCP servers. Store the API keys securely and use environment variables to avoid hardcoding them in the configuration file.
  • Session Management: Implement session management to maintain state across multiple interactions with the MCP servers. This is particularly important for tasks that involve multiple steps or require authentication.
  • Dynamic Content Handling: Use Playwright and Hyperbrowser’s capabilities to handle dynamic content. This includes waiting for elements to load, interacting with JavaScript-driven components, and extracting data from dynamically generated tables and forms.

Data Analysis and Documentation

After gathering information from web searches and MCP servers, the next step is to analyze the data and document the findings. Here are some tips for effective data analysis and documentation:

  • Data Cleaning: Clean and preprocess the extracted data to remove noise and inconsistencies. This includes removing duplicate entries, correcting errors, and standardizing formats.
  • Analysis Techniques: Apply appropriate analysis techniques to extract insights from the data. This can include statistical analysis, sentiment analysis, and topic modeling.
  • Documentation Structure: Organize the research findings in a clear and structured manner. Use headings, subheadings, bullet points, and tables to present the information effectively. Include a summary of the key findings, detailed explanations, and supporting evidence.

Committing Findings Directly

To streamline the workflow, the researcher.yml configuration is designed to commit findings directly to the main branch upon task completion, bypassing the need for pull requests. This requires careful consideration of version control practices and security measures.

  • Version Control: Ensure that the researcher has the necessary permissions to commit directly to the main branch. Use Git hooks to enforce code quality and prevent accidental commits of incomplete or erroneous findings.
  • Security Measures: Implement security measures to prevent unauthorized access to the repository. Use SSH keys for authentication and restrict access to sensitive files and directories.
  • Testing and Validation: Before committing the findings, perform thorough testing and validation to ensure accuracy and completeness. Use automated tests to verify the correctness of the analysis and documentation.

Example Scenario: Researching Cloud Computing Trends

To illustrate the use of the researcher.yml configuration, consider a scenario where the researcher needs to gather information about the latest trends in cloud computing. The researcher would use the following steps:

  1. Formulate Search Queries: The researcher would formulate search queries such as “latest cloud computing trends,” “cloud computing market analysis,” and “emerging cloud technologies.”
  2. Search the Web: The researcher would use the web searching capabilities to retrieve relevant articles, reports, and blog posts from various sources.
  3. Extract Data from MCP Servers: The researcher would use the Playwright and Hyperbrowser MCP servers to extract data from dynamic web pages, such as market research reports and industry surveys.
  4. Analyze the Data: The researcher would analyze the gathered data to identify the key trends in cloud computing, such as the adoption of multi-cloud environments, the rise of serverless computing, and the increasing use of AI and machine learning in the cloud.
  5. Document the Findings: The researcher would document the findings in a markdown file, including a summary of the key trends, detailed explanations, and supporting evidence from the gathered data.
  6. Commit the Findings: The researcher would commit the markdown file directly to the research_findings folder in the main branch of the repository.

Conclusion

By creating a well-defined researcher.yml configuration, you can automate the process of web research, data analysis, and documentation. This not only saves time and effort but also ensures that research findings are accurate, consistent, and readily available. The direct commit feature further streamlines the workflow, allowing for rapid dissemination of information.

By following these guidelines and customizing the configuration to your specific needs, you can empower your researchers to efficiently gather insights and contribute to your organization's knowledge base. Remember to prioritize security, accuracy, and clarity in your configuration and workflows to maximize the benefits of automation.

For more information on web automation and related topics, visit the Selenium Official Website. This external resource provides valuable insights and tools for enhancing your web research and automation capabilities.