context-portal

What is Context Portal MCP server (ConPort)?

Context Portal (ConPort) is a memory bank database system that effectively builds a project-specific knowledge graph, capturing entities like decisions, progress, and architecture, along with their relationships. This serves as a powerful backend for Retrieval Augmented Generation (RAG), enabling AI assistants to access precise, up-to-date project information. It aims to replace older file-based context management systems by offering a more reliable and queryable database backend (SQLite per workspace).

How to use Context Portal (ConPort)?

ConPort can be installed and run using uvx directly from PyPI, which is the recommended method for most users. This involves configuring your MCP client (e.g., in mcp_settings.json) to use uvx to execute the context-portal-mcp package. For developers, installation from the Git repository is also possible, involving cloning the repository, setting up a virtual environment with uv, and installing dependencies via uv pip install -r requirements.txt.

To initialize ConPort in a new workspace, you can create a projectBrief.md file in your project's root directory with a high-level overview. LLM agents configured with ConPort custom instructions will then prompt you to import this content into the Product Context, providing an immediate baseline for the project's understanding.

Key Features of Context Portal (ConPort)

• Structured Context Storage: Uses SQLite for persistent storage, with one database per workspace, automatically created.
• MCP Server: Built with Python/FastAPI, providing a robust backend for AI assistants and developer tools.
• Comprehensive MCP Tools: A suite of defined tools for interaction, including product context management, active context management, decision logging, progress tracking, system pattern management, custom data management, context linking, history, and import/export functionalities.
• Multi-workspace Support: Manages context for multiple projects via workspace_id.
• Dynamic Project Knowledge Graph: Enables building explicit relationships between context items.
• Vector Data Storage & Semantic Search: Enhances RAG capabilities by powering advanced semantic search.
• Retrieval Augmented Generation (RAG) Backend: Provides precise, queryable project memory for AI.
• Prompt Caching: Supports prompt caching with compatible LLM providers.
• Database Schema Evolution: Manages schema changes using Alembic migrations for seamless updates.

Use Cases of Context Portal (ConPort)

• AI Assistant Enhancement: Provides AI assistants with structured, up-to-date project information for more accurate and context-aware responses.
• Developer Tool Integration: Integrates with IDEs and other interfaces to offer context-aware development.
• Project Knowledge Management: Acts as a central memory bank for project decisions, progress, architectural patterns, and custom data.
• Improved Context Management: Offers a more reliable and queryable alternative to file-based context management systems.
• Facilitating Prompt Engineering: Helps LLM agents understand project context for better prompt generation and response quality.

FAQ about Context Portal (ConPort)

Q: What is the purpose of the --workspace_id argument?

A: The --workspace_id argument provides the server process with the absolute path to the project workspace it should initially be associated with. It also acts as a critical safety check to prevent the server from mistakenly creating its database files inside its own installation directory. It's the standard way for an MCP client (like an IDE extension) to signal to the server which project it is launching for.

Q: Does the --workspace_id provided at server startup automatically apply to all subsequent MCP tool calls?

A: No, the --workspace_id provided at server startup is not automatically used as the workspace_id parameter for every subsequent MCP tool call. ConPort tools are designed to require the workspace_id parameter explicitly in each call (e.g., get_product_context({"workspace_id": "..."})). This design supports the possibility of a single server instance managing multiple workspaces and ensures clarity for each operation. Your client IDE/MCP client is responsible for providing the correct workspace_id with each tool call.

Q: How can I provide initial context to ConPort in a new workspace?

A: The recommended way is to create a projectBrief.md file in the root directory of your project workspace. Populate it with a high-level overview of your project. When an LLM agent using ConPort custom instructions initializes, it will check for this file and ask if you'd like to import its content into the ConPort Product Context. Alternatively, if projectBrief.md is not found or you choose not to import it, the LLM agent may offer to help you define the Product Context manually.

Context Portal MCP (ConPort)

(It's a memory bank!)

           

A database-backed Model Context Protocol (MCP) server for managing structured project context, designed to be used by AI assistants and developer tools within IDEs and other interfaces.

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

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.

Installation for Developers (from Git Repository)

These instructions guide you through setting up ConPort for development or contribution by cloning its Git repository and installing dependencies.

1. Clone the Repository: Open your terminal or command prompt and run:

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

2. Create and Activate a Virtual Environment: In the context-portal directory:

   uv venv
   

   Activate the environment:

   • Linux/macOS (bash/zsh):

     source .venv/bin/activate
     

   • Windows (Command Prompt):

     .venv\Scripts\activate.bat
     

   • Windows (PowerShell):

     .venv\Scripts\Activate.ps1
     

3. Install Dependencies: With your virtual environment activated:

   uv pip install -r requirements.txt
   

4. Verify Installation (Optional): Ensure your virtual environment is activated.

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

   This should output the command-line help for the ConPort server.

Purpose of the --workspace_id Command-Line Argument:

When you launch the ConPort server, particularly in STDIO mode (--mode stdio), the --workspace_id argument serves several key purposes:

1. Initial Server Context: It provides the server process with the absolute path to the project workspace it should initially be associated with.
2. Critical Safety Check: In STDIO mode, this path is used to perform a vital check that prevents the server from mistakenly creating its database files (context.db, conport_vector_data/) inside its own installation directory. This protects against misconfigurations where the client might not correctly provide the workspace path.
3. Client Launch Signal: It's the standard way for an MCP client (like an IDE extension) to signal to the server which project it is launching for.

Important Note: The --workspace_id provided at server startup is not automatically used as the workspace_id parameter for every subsequent MCP tool call. ConPort tools are designed to require the workspace_id parameter explicitly in each call (e.g., get_product_context({"workspace_id": "..."})). This design supports the possibility of a single server instance managing multiple workspaces and ensures clarity for each operation. Your client IDE/MCP client is responsible for providing the correct workspace_id with each tool call.

Key Takeaway: ConPort critically relies on an accurate --workspace_id to identify the target project. Ensure this argument correctly resolves to the absolute path of your project workspace, either through IDE variables like ${workspaceFolder} or by providing a direct absolute path.

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 hardcoding specific tool invocation details.

How to Use These Strategy Files:

1. Identify the strategy file relevant to your LLM agent's environment.
2. Copy the entire content of that file.
3. Paste it into your LLM's custom instructions or system prompt area. The method varies by LLM platform (IDE extension settings, web UI, API configuration).

These instructions equip the LLM with the knowledge to:

• Initialize and load context from ConPort.
• Update ConPort with new information (decisions, progress, etc.).
• Manage custom data and relationships.
• Understand the importance of workspace_id. Important Tip for Starting Sessions: To ensure the LLM agent correctly initializes and loads context, especially in interfaces that might not always strictly adhere to custom instructions on the first message, it's a good practice to start your interaction with a clear directive like: Initialize according to custom instructions. This can help prompt the agent to perform its ConPort initialization sequence as defined in its strategy file.

Initial ConPort Usage in a Workspace

When you first start using ConPort in a new or existing project workspace, the ConPort database (context_portal/context.db) will be automatically created by the server if it doesn't exist. To help bootstrap the initial project context, especially the Product Context, consider the following:

Using a projectBrief.md File (Recommended)

1. Create projectBrief.md: In the root directory of your project workspace, create a file named projectBrief.md.
2. Add Content: Populate this file with a high-level overview of your project. This could include:
   • The main goal or purpose of the project.
   • Key features or components.
   • Target audience or users.
   • Overall architectural style or key technologies (if known).
   • Any other foundational information that defines the project.
3. Automatic Prompt for Import: When an LLM agent using one of the provided ConPort custom instruction sets (e.g., roo_code_conport_strategy) initializes in the workspace, it is designed to:
   • Check for the existence of projectBrief.md.
   • If found, it will read the file and ask you if you'd like to import its content into the ConPort Product Context.
   • If you agree, the content will be added to ConPort, providing an immediate baseline for the project's Product Context.

Manual Initialization

If projectBrief.md is not found, or if you choose not to import it:

• The LLM agent (guided by its custom instructions) will typically inform you that the ConPort Product Context appears uninitialized.
• It may offer to help you define the Product Context manually, potentially by listing other files in your workspace to gather relevant information.

By providing initial context, either through projectBrief.md or manual entry, you enable ConPort and the connected LLM agent to have a better foundational understanding of your project from the start.

Available ConPort Tools

The ConPort server exposes the following tools via MCP, allowing interaction with the underlying project knowledge graph. This includes tools for semantic search powered by vector data storage. These tools facilitate the Retrieval aspect crucial for Augmented Generation (RAG) by AI agents. All tools require a workspace_id argument (string, required) to specify the target project workspace.

• Product Context Management:
  • get_product_context: Retrieves the overall project goals, features, and architecture.
  • update_product_context: Updates the product context. Accepts full content (object) or patch_content (object) for partial updates (use __DELETE__ as a value in patch to remove a key).
• Active Context Management:
  • get_active_context: Retrieves the current working focus, recent changes, and open issues.
  • update_active_context: Updates the active context. Accepts full content (object) or patch_content (object) for partial updates (use __DELETE__ as a value in patch to remove a key).
• Decision Logging:
  • log_decision: Logs an architectural or implementation decision.
    • Args: summary (str, req), rationale (str, opt), implementation_details (str, opt), tags (list[str], opt).
  • get_decisions: Retrieves logged decisions.
    • Args: limit (int, opt), tags_filter_include_all (list[str], opt), tags_filter_include_any (list[str], opt).
  • search_decisions_fts: Full-text search across decision fields (summary, rationale, details, tags).
    • Args: query_term (str, req), limit (int, opt).
  • delete_decision_by_id: Deletes a decision by its ID.
    • Args: decision_id (int, req).
• Progress Tracking:
  • log_progress: Logs a progress entry or task status.
    • Args: status (str, req), description (str, req), parent_id (int, opt), linked_item_type (str, opt), linked_item_id (str, opt).
  • get_progress: Retrieves progress entries.
    • Args: status_filter (str, opt), parent_id_filter (int, opt), limit (int, opt).
  • update_progress: Updates an existing progress entry.
    • Args: progress_id (int, req), status (str, opt), description (str, opt), parent_id (int, opt).
  • delete_progress_by_id: Deletes a progress entry by its ID.
    • Args: progress_id (int, req).
• System Pattern Management:
  • log_system_pattern: Logs or updates a system/coding pattern.
    • Args: name (str, req), description (str, opt), tags (list[str], opt).
  • get_system_patterns: Retrieves system patterns.
    • Args: tags_filter_include_all (list[str], opt), tags_filter_include_any (list[str], opt).
  • delete_system_pattern_by_id: Deletes a system pattern by its ID.
    • Args: pattern_id (int, req).
• Custom Data Management:
  • log_custom_data: Stores/updates a custom key-value entry under a category. Value is JSON-serializable.
    • Args: category (str, req), key (str, req), value (any, req).
  • get_custom_data: Retrieves custom data.
    • Args: category (str, opt), key (str, opt).
  • delete_custom_data: Deletes a specific custom data entry.
    • Args: category (str, req), key (str, req).
  • search_project_glossary_fts: Full-text search within the 'ProjectGlossary' custom data category.
    • Args: query_term (str, req), limit (int, opt).
  • search_custom_data_value_fts: Full-text search across all custom data values, categories, and keys.
    • Args: query_term (str, req), category_filter (str, opt), limit (int, opt).
• Context Linking:
  • link_conport_items: Creates a relationship link between two ConPort items, explicitly building out the project knowledge graph.
    • Args: source_item_type (str, req), source_item_id (str, req), target_item_type (str, req), target_item_id (str, req), relationship_type (str, req), description (str, opt).
  • get_linked_items: Retrieves items linked to a specific item.
    • Args: item_type (str, req), item_id (str, req), relationship_type_filter (str, opt), linked_item_type_filter (str, opt), limit (int, opt).
• History & Meta Tools:
  • get_item_history: Retrieves version history for Product or Active Context.
    • Args: item_type ("product_context" | "active_context", req), version (int, opt), before_timestamp (datetime, opt), after_timestamp (datetime, opt), limit (int, opt).
  • get_recent_activity_summary: Provides a summary of recent ConPort activity.
    • Args: hours_ago (int, opt), since_timestamp (datetime, opt), limit_per_type (int, opt, default: 5).
  • get_conport_schema: Retrieves the schema of available ConPort tools and their arguments.
• Import/Export:
  • export_conport_to_markdown: Exports ConPort data to markdown files.
    • Args: output_path (str, opt, default: "./conport_export/").
  • import_markdown_to_conport: Imports data from markdown files into ConPort.
    • Args: input_path (str, opt, default: "./conport_export/").
• Batch Operations:
  • batch_log_items: Logs multiple items of the same type (e.g., decisions, progress entries) in a single call.
    • Args: item_type (str, req - e.g., "decision", "progress_entry"), items (list[dict], req - list of Pydantic model dicts for the item type).

Further Reading

For a more in-depth understanding of ConPort's design, architecture, and advanced usage patterns, please refer to:

• conport_mcp_deep_dive.md

Contributing

Please see our CONTRIBUTING.md guide for details on how to contribute to the ConPort project.

License

This project is licensed under the Apache-2.0 license.

Database Migration & Update Guide

For detailed instructions on how to manage your context.db file, especially when updating ConPort across versions that include database schema changes, please refer to the dedicated v0.2.4_UPDATE_GUIDE.md. This guide provides steps for manual data migration (export/import) if needed, and troubleshooting tips.