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Context Portal MCP (ConPort)

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A server for managing structured project context using SQLite, with support for vector embeddings for semantic search and Retrieval Augmented Generation (RAG).

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Context Portal MCP (ConPort)

(It's a memory bank!)

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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.

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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.

Automatic Workspace Detection

ConPort can automatically determine the correct workspace_id so you do not need to hardcode an absolute path in your MCP client configuration. This is especially useful for IDEs that fail to expand ${workspaceFolder} when launching MCP servers.

Detection is enabled by default and can be controlled via CLI flags:

Flags:

  • --auto-detect-workspace (default: enabled) Turns on automatic detection.
  • --no-auto-detect Disables detection (explicit --workspace_id or per-tool workspace_id must then be provided).
  • --workspace-search-start <path> Optional starting directory for upward search (defaults to current working directory).

How it works (multi‑strategy):

  1. Strong Indicators (fast path): Looks for high-confidence project roots containing any of: package.json, .git, pyproject.toml, Cargo.toml, go.mod, pom.xml.
  2. Multiple General Indicators: If ≥2 general indicators (README, license, build files, etc.) exist in a directory, it is treated as a root.
  3. Existing ConPort Workspace: Presence of a context_portal/ directory indicates a valid workspace.
  4. MCP Environment Context: Honors environment variables like VSCODE_WORKSPACE_FOLDER or CONPORT_WORKSPACE when set and valid.
  5. Fallback: If no indicators are found, uses the starting directory verbosely (with a warning).

Tooling:

  • get_workspace_detection_info (MCP tool) exposes a diagnostic dictionary showing:
    • start_path
    • detected_workspace
    • detection_method (strong_indicators | multiple_indicators | existing_context_portal | fallback)
    • indicators_found
    • relevant environment variables

Best Practices:

  • Keep detection enabled unless you operate multi-root scenarios where explicit isolation per call is required.
  • If an IDE passes the literal string ${workspaceFolder}, ConPort will ignore it and auto-detect safely (logged at WARNING).
  • For debugging ambiguous roots (e.g., nested repos), run the detection info tool to confirm which directory was selected.

Example MCP launch (relying fully on auto-detect):

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

To disable detection explicitly (forcing provided IDs only):

{
  "mcpServers": {
    "conport": {
      "command": "uvx",
      "args": [
        "--from", "context-portal-mcp",
        "conport-mcp",
        "--mode", "stdio",
        "--no-auto-detect",
        "--workspace_id", "/absolute/path/to/project"
      ]
    }
  }
}

If you have a launcher that starts inside a deep subdirectory, provide a higher start path:

conport-mcp --mode stdio --workspace-search-start ../../

See UNIVERSAL_WORKSPACE_DETECTION.md for full rationale, edge cases, and troubleshooting.

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.

Note: For convenience, all integer-like parameters accept either numbers or digit-only strings (e.g., "10", " 3"). The server trims whitespace and coerces these to integers while preserving validation bounds (e.g., ge=1). Credit: @cipradu.

  • 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:

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.

Acknowledgments

  • Special thanks to @cipradu for the valuable suggestion to implement integer-string coercion for numeric arguments, which improves the user experience when interacting with the MCP server from various clients.

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.