Context Portal

What is Context Portal MCP server (ConPort)?

Context Portal (ConPort) is your project's memory bank. It's a tool that helps AI assistants understand your specific software project better by storing important information like decisions, tasks, and architectural patterns in a structured way. Think of it as building a project-specific knowledge base that the AI can easily access and use to give you more accurate and helpful responses.

What it does:

• Keeps track of project decisions, progress, and system designs.
• Stores custom project data (like glossaries or specs).
• Helps AI find relevant project information quickly (like a smart search).
• Enables AI to use project context for better responses (RAG).
• More efficient for managing, searching, and updating context compared to simple text file-based memory banks.

ConPort provides a robust and structured way for AI assistants to store, retrieve, and manage various types of project context. It effectively builds a project-specific knowledge graph, capturing entities like decisions, progress, and architecture, along with their relationships. This structured knowledge base, enhanced by vector embeddings for semantic search, then serves as a powerful backend for Retrieval Augmented Generation (RAG), enabling AI assistants to access precise, up-to-date information for more context-aware and accurate responses.

It replaces older file-based context management systems by offering a more reliable and queryable database backend (SQLite per workspace). ConPort is designed to be a generic context backend, compatible with various IDEs and client interfaces that support MCP.

Key features include:

• Structured context storage using SQLite (one DB per workspace, automatically created).
• MCP server (context_portal_mcp) built with Python/FastAPI.
• A comprehensive suite of defined MCP tools for interaction (see "Available ConPort Tools" below).
• Multi-workspace support via workspace_id.
• Primary deployment mode: STDIO for tight IDE integration.
• Enables building a dynamic project knowledge graph with explicit relationships between context items.
• Includes vector data storage and semantic search capabilities to power advanced RAG.
• Serves as an ideal backend for Retrieval Augmented Generation (RAG), providing AI with precise, queryable project memory.
• Provides structured context that AI assistants can leverage for prompt caching with compatible LLM providers.
• Manages database schema evolution using Alembic migrations, ensuring seamless updates and data integrity.

Prerequisites

Before you begin, ensure you have the following installed:

• Python: Version 3.8 or higher is recommended.
  • Download Python
  • Ensure Python is added to your system's PATH during installation (especially on Windows).
• uv: (Highly Recommended) A fast Python environment and package manager. Using uv significantly simplifies virtual environment creation and dependency installation.
  • Install uv

Installation and Configuration (Recommended)

The recommended way to install and run ConPort is by using uvx to execute the package directly from PyPI. This method avoids the need to manually create and manage virtual environments.

uvx Configuration (Recommended for most IDEs)

In your MCP client settings (e.g., mcp_settings.json), use the following configuration:

{
  "mcpServers": {
    "conport": {
      "command": "uvx",
      "args": [
        "--from",
        "context-portal-mcp",
        "conport-mcp",
        "--mode",
        "stdio",
        "--workspace_id",
        "${workspaceFolder}",
        "--log-file",
        "./logs/conport.log",
        "--log-level",
        "INFO"
      ]
    }
  }
}

• command: uvx handles the environment for you.
• args: Contains the arguments to run the ConPort server.
• ${workspaceFolder}: This IDE variable is used to automatically provide the absolute path of the current project workspace.
• --log-file: Optional: Path to a file where server logs will be written. If not provided, logs are directed to stderr (console). Useful for persistent logging and debugging server behavior.
• --log-level: Optional: Sets the minimum logging level for the server. Valid choices are DEBUG, INFO, WARNING, ERROR, CRITICAL. Defaults to INFO. Set to DEBUG for verbose output during development or troubleshooting.

Important: Many IDEs do not expand ${workspaceFolder} when launching MCP servers. Use one of these safe options:

1. Provide an absolute path for --workspace_id.
2. Omit --workspace_id at launch and rely on per-call workspace_id (recommended if your client provides it on every call).

Alternative configuration (no --workspace_id at launch):

{
  "mcpServers": {
    "conport": {
      "command": "uvx",
      "args": [
        "--from",
        "context-portal-mcp",
        "conport-mcp",
        "--mode",
        "stdio",
        "--log-file",
        "./logs/conport.log",
        "--log-level",
        "INFO"
      ]
    }
  }
}

If you omit --workspace_id, the server will skip pre-initialization and initialize the database on the first tool call using the workspace_id provided in that call.

Installation for Developers (from Git Repository)

The most appropriate way to develop and test ConPort is to run it in your IDE as an MCP server using the configuration above. This exercises STDIO mode and real client behavior.

If you need to run against a local checkout and virtualenv, you can configure your MCP client to launch the dev server via uv run and your .venv/bin/python:

{
  "mcpServers": {
    "conport": {
      "command": "uv",
      "args": [
        "run",
        "--python",
        ".venv/bin/python",
        "--directory",
        "<path to context-portal repo> ",
        "conport-mcp",
        "--mode",
        "stdio",
        "--log-file",
        "./logs/conport-dev.log",
        "--log-level",
        "DEBUG"
      ],
      "disabled": false
    }
  }
}

Notes:

• Set --directory to your repo path; this uses your local checkout and venv interpreter.
• Logs go to ./logs/conport-dev.log with DEBUG verbosity.

Local environment setup

Set up for development or contribution via the Git repo.

1. Clone the repository

   git clone https://github.com/GreatScottyMac/context-portal.git
   cd context-portal
   

2. Create a virtual environment

   uv venv
   

   Activate it using your shell’s standard activation (e.g., source .venv/bin/activate on macOS/Linux).

3. Install dependencies

   uv pip install -r requirements.txt
   

4. Run in your IDE (recommended) Configure your IDE’s MCP settings using the "uvx Configuration" or the dev uv run configuration shown above. This is the most representative test of ConPort in STDIO mode.

5. Optional: CLI help

   uv run python src/context_portal_mcp/main.py --help
   

Notes:

• For --workspace_id behavior and IDE path handling, see the guidance under the "uvx Configuration" section above. Many IDEs do not expand ${workspaceFolder}.

For pre-upgrade cleanup, including clearing Python bytecode cache, please refer to the v0.2.4_UPDATE_GUIDE.md.

Usage with LLM Agents (Custom Instructions)

ConPort's effectiveness with LLM agents is significantly enhanced by providing specific custom instructions or system prompts to the LLM. This repository includes tailored strategy files for different environments:

• For Roo Code:

  • roo_code_conport_strategy: Contains detailed instructions for LLMs operating within the Roo Code VS Code extension, guiding them on how to use ConPort tools for context management.
  
  

• For CLine:

  • cline_conport_strategy: Contains detailed instructions for LLMs operating within the Cline VS Code extension, guiding them on how to use ConPort tools for context management.
  
  

• For Windsurf Cascade:

  • cascade_conport_strategy: Specific guidance for LLMs integrated with the Windsurf Cascade environment. Important: When initiating a session in Cascade, it is necessary to explicity tell the LLM:

  Initialize according to custom instructions
  

• For General/Platform-Agnostic Use:

  • generic_conport_strategy: Provides a platform-agnostic set of instructions for any MCP-capable LLM. It emphasizes using ConPort's get_conport_schema operation to dynamically discover the exact ConPort tool names and their parameters, guiding the LLM on when and why to perform conceptual interactions (like logging a decision or updating product context) rather than hardco

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README truncated. View full README on GitHub.