claude-collaborator

Multi-AI MCP server for C# codebases. Claude + GLM working together.

Philosophy

Claude is the architect. GLM is the creative sidekick.

• Claude (the Boss): Makes decisions, directs work, synthesizes information
• GLM (the Sidekick): Explores alternatives, challenges assumptions, offers fresh perspectives

GLM is configured for creativity and deep thinking — it considers multiple angles and unconventional ideas. Claude evaluates these insights and makes the final call.

"The enemy of art is the absence of limitations." — GLM explores the space; Claude finds the best path.

Features

• Auto-Learning: Proactively captures knowledge during work — patterns, workarounds, preferences, architecture insights
• Two-AI Collaboration: GLM brainstorms creative approaches; Claude evaluates and decides
• Persistent Memory: Semantic vector memory that persists across sessions
• GLM Auto-Enrich: GLM automatically provides deeper insights on learnings and architecture analysis in the background
• Context Management: Smart context tracking with automatic compaction
• Pattern Discovery: Find similar code by concept, lookup codebase conventions

What This Server Does (and Doesn't Do)

This server focuses on memory, learning, and two-AI collaboration. It does NOT provide semantic code navigation — use a Roslyn-based MCP server for find-references, go-to-definition, rename, etc.

This server	Roslyn-based MCP server
Learn & remember across sessions	Find references
Semantic memory search	Go to definition
GLM brainstorm / risk check / alternatives	Find implementations
Find similar code by concept	Rename symbol
Lookup codebase conventions	Extract method
Session & task tracking	Diagnostics & code fixes

Installation

pip install claude-collaborator

Or install from source with all extras:

git clone https://github.com/coreeye/claude-collaborator-mcp.git
cd claude-collaborator-mcp
pip install -e ".[all]"

Quick Start

Claude Code (Recommended)

Register globally:

claude mcp add --scope user claude-collaborator -- python -u -m claude_collaborator.server

Or project-only:

claude mcp add --scope project claude-collaborator -- python -u -m claude_collaborator.server

Windows note: Always invoke python (or the absolute path to python.exe) directly. Do not use the py launcher — it forwards stdio through a parent process and adds a buffer layer that can hang tool-call responses indefinitely. The -u flag, plus PYTHONUNBUFFERED=1 in the env block, ensures the server's stdout is never buffered. See docs/configuration.md for the full env recommendation and troubleshooting.

Configure GLM API Key

# Windows
setx GLM_API_KEY "your_api_key_here"

# Linux/macOS
echo 'export GLM_API_KEY=your_api_key_here' >> ~/.bashrc

Or use a .env file in the project root:

GLM_API_KEY=your_api_key_here
GLM_MODEL=glm-5.1

Available Tools

Codebase Management

• switch_codebase - Switch to a different codebase
• list_codebases - Discover codebases (.sln/.git) in a directory
• get_config - View current configuration

Auto-Learning

• learn - Record observations during work (auto-categorized, deduplicated, GLM-enriched)
• session_learn - Capture session learnings in batch (GLM-enriched)

Memory

• memory_save - Save findings for future sessions
• memory_search - Search by keywords
• memory_semantic_search - Search by meaning (semantic similarity)
• memory_get - Retrieve a specific topic
• memory_status / memory_vector_stats - View statistics

Context Management

• context_retrieve - Retrieve relevant context for a query
• context_offload - Manually trigger context offload to memory
• context_stats - View context tracking statistics

Session & Task Tracking

• session_status - View current session state
• task_start / task_update / task_status - Track long-running tasks

Pattern Discovery & Analysis

• find_similar_code - Find code patterns by concept description
• lookup_convention - Learn codebase conventions from examples
• get_file_summary - Quick file overview with complexity hints

GLM Collaboration (requires API key)

• brainstorm - GLM thinks divergently — unconventional approaches, hidden trade-offs
• get_alternative - Get alternative approaches for comparison
• risk_check - Identify potential risks before changes
• summarize_large_file - GLM summarizes large files to save context

GLM Auto-Enrich

GLM automatically enriches certain tool results in the background:

Tool	What GLM adds
learn	Deeper pattern extraction from observations
session_learn	Recurring themes and knowledge gaps
find_similar_code	Pattern comparison and best approach analysis
lookup_convention	Whether conventions should evolve

Enriched insights are stored in vector memory for future semantic search.

Configuration

See docs/configuration.md for full details.

Key Settings

Option	Default	Description
codebase_path	auto-detected	Path to C# solution
glm_api_key	(none)	GLM API key
glm_model	glm-5.1	GLM model to use (glm-5.2 is newer but needs API entitlement)
embedding_model	all-MiniLM-L6-v2	Embedding model for semantic search
auto_glm_enrich	true	Enable background GLM enrichment

CLAUDE.md Setup (Optional)

For richer proactive behavior, add guidance to your CLAUDE.md:

# Global (all projects)
cp docs/CLAUDE.md.example ~/.claude/CLAUDE.md

See docs/CLAUDE.md.example for the template.

Development

pip install -e ".[all]"
python -m pytest tests/ -v -s

License

MIT License - see LICENSE for details.