codealive-mcp

🥈Silver

The most accurate and comprehensive Context Engine as a service, optimized for large codebases, powered by advanced GraphRAG and accessible via MCP. It enriches the context for AI agents like Codex, Claude Code, Cursor, etc., making them 35% more efficient and up to 84% faster.

7880Updated 1mo ago

Intermediate30min to implementautomation

Saves ~480 min per use

Quick InstallView Source

git clone https://github.com/CodeAlive-AI/codealive-mcp.git

Works with:

Claude

Overview

About This Skill

How to Use

[{"step":"Install the codealive-mcp Context Engine in your development environment. For VS Code with Cursor, run: `pip install codealive-mcp` and add the MCP server configuration to your `settings.json`.","tip":"Ensure your codebase is indexed by the Context Engine before analysis. For large repositories (>50K files), pre-indexing may take 10-30 minutes."},{"step":"Specify the project and task using the prompt template. Replace [PROJECT_NAME] with your repository name (e.g., 'my-ai-startup') and [SPECIFIC_TASK] with your goal (e.g., 'reduce technical debt in the API layer').","tip":"For best results, include the Context Engine ID (e.g., 'eco-2024-q2') if your organization has a custom-tuned model."},{"step":"Run the analysis and review the output. Focus on the 'prioritized list' section first, as it highlights the highest-impact opportunities.","tip":"Use the provided code snippets to implement fixes directly. The Context Engine can also generate pull request descriptions for approved changes."},{"step":"Iterate by refining your prompt. For example, ask for a deeper dive into a specific module or request alternative refactoring approaches.","tip":"Combine the Context Engine with your AI coding assistant (e.g., Cursor or Claude Code) to auto-implement the suggested changes."},{"step":"Monitor the efficiency gains. The Context Engine tracks improvements in codebase metrics like build time, test coverage, and bug resolution rate.","tip":"Share the generated insights with your team via Slack or email by exporting the output as a markdown report."}]

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/CodeAlive-AI/codealive-mcp

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use the codealive-mcp Context Engine to analyze the [PROJECT_NAME] codebase for [SPECIFIC_TASK]. Focus on [KEY_AREAS] such as [AREA_1], [AREA_2], and [AREA_3]. Provide a prioritized list of insights, refactoring opportunities, and potential bugs. Include code snippets where relevant. Context Engine ID: [ENGINE_ID].

Example Output

For the open-source project 'EcoSim' (a climate modeling toolkit), the codealive-mcp Context Engine identified the following high-impact opportunities:

1. **Performance Bottleneck in `climate_model.py`**: The `simulate_weather` function uses a nested loop over grid cells (10,000+ iterations) that could be optimized using NumPy vectorization. Current runtime: 42 seconds per simulation. Proposed fix reduces this to 3.2 seconds (92% improvement).

2. **Memory Leak in `data_loader.py`**: The `load_climate_data` function caches large datasets in memory without releasing them after processing. This caused the application to crash after 50 simulations on a 16GB RAM machine. Suggested solution: Implement chunked loading with `dask.dataframe` or use `weakref` for cache management.

3. **API Inconsistency in `api/endpoints.py`**: The `/predict` endpoint returns raw JSON while `/visualize` returns a base64-encoded image. Standardizing to return JSON with embedded image URLs would simplify frontend integration. Example refactor:
   ```python
   # Before
   return {"image": "data:image/png;base64,..."}
   # After
   return {"image_url": "/static/predictions/20240515.png"}
   ```

4. **Missing Test Coverage**: Only 42% of the `utils/math_utils.py` module is covered by tests. Critical functions like `calculate_co2_equivalent` lack edge-case testing. Recommend adding pytest cases for negative emissions scenarios.

5. **Dependency Conflicts**: The project uses `pandas==1.3.5` and `geopandas==0.10.2`, which are incompatible with newer versions of `shapely`. Resolved by pinning to `shapely==1.8.5` in `requirements.txt`.

The Context Engine also flagged 12 potential race conditions in the `parallel_processing.py` module, all related to shared state in multi-threaded simulations. These should be addressed before merging the upcoming v2.1 release.

Total estimated time savings: 15 developer-hours per sprint. Bug fixes would prevent ~8 hours of debugging time monthly.