A database-backed Model Context Protocol (MCP) server for managing structured project context, designed to be used by AI assistants and developer
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. That is what the context portal mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
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.
Configuration on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
The server publishes 14 tools. What each one is for:
get_product_context — Retrieves the overall project goals, features, and architectureupdate_product_context — Updates the product context. Accepts full content (object) or patch_content (object) for partial updates (use DELETE as a value in patch toget_active_context — Retrieves the current working focus, recent changes, and open issuesupdate_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 removelog_decision — Logs an architectural or implementation decisionArgs — summary (str, req), rationale (str, opt), implementation_details (str, opt), tags (list[str], opt)get_decisions — Retrieves logged decisionssearch_decisions_fts — Full-text search across decision fields (summary, rationale, details, tags)delete_decision_by_id — Deletes a decision by its IDlog_progress — Logs a progress entry or task statusget_progress — Retrieves progress entriesupdate_progress — Updates an existing progress entryBefore 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
This sits in the monitoring and observability group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Context Portal's toolset — get_product_context, update_product_context, get_active_context and 11 more — is a fair guide to whether it matches your workflow. It is maintained by GreatScottyMac; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| 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). |
| 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). |
| 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. |
| search_decisions_fts | Full-text search across decision fields (summary, rationale, details, tags). |
| delete_decision_by_id | Deletes a decision by its ID. |
| log_progress | Logs a progress entry or task status. |
| get_progress | Retrieves progress entries. |
| update_progress | Updates an existing progress entry. |
| delete_progress_by_id | Deletes a progress entry by its ID. |
| log_system_pattern | Logs or updates a system/coding pattern. |
{
"mcpServers": {
"conport": {
"command": "uvx",
"args": [
"--from",
"context-portal-mcp",
"conport-mcp",
"--mode",
"stdio",
"--workspace_id",
"${workspaceFolder}",
"--log-file",
"./logs/conport.log",
"--log-level",
"INFO"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
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
Read-only SQL access to Postgres — let your assistant inspect schemas and answer questions from real data.
Manage your whole Supabase project in conversation — database, auth, storage, Edge Functions and branches.
Query, modify and analyse local SQLite databases in conversation — the fastest way to chat with a data file.
Metabase ships its own MCP endpoint — search your BI content, build and run queries, and save questions and dashboards without leaving the chat.
Official MongoDB server covering data, schemas and Atlas management — from find queries to spinning up clusters.
Serverless Postgres with database branching — point your assistant at Neon and let it work on disposable copies.