F
licenseC
qualityD
maintenanceA multi-agent book-writing MCP server that manages a guarded chapter pipeline (writer → editor → third-pass agents → approve) with project planning, continuity tracking, and export/diff helpers.
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brettapps MCP Agent Workforce
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@brettapps MCP Agent Workforcewrite a chapter for my sci-fi novel"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
brettapps MCP Agent Workforce
BrettApps — MCP Server Agents, one per agent, integrated with local Ollama models.
Architecture
brettapps/
├── agents/ # One folder per agent
│ ├── manuscript_author/ # Manuscript author agent
│ │ ├── agent.py # Agent logic (BaseAgent subclass)
│ │ └── server.py # FastAPI MCP server exposing tools
│ ├── ebook_production/ # Ebook production agent
│ ├── market_research/ # Market research agent
│ ├── cover_creative_director/
│ ├── editorial/
│ ├── fact_check_compliance/
│ ├── cloud_archivist/
│ ├── analytics_optimisation/
│ ├── positioning_offer/
│ ├── sales_copy/
│ ├── funnel_fulfillment/
│ ├── launch_strategist/
│ ├── product_director/
│ ├── outline_architect/
│ ├── credentials_manager/
│ ├── obsidian_knowledge_manager/
│ └── google_drive_agent/
├── models/ # Shared Ollama client
│ └── ollama_client.py
├── shared/ # Shared utilities
│ ├── base.py # BaseAgent class
│ └── config.py # Config loader
├── pyproject.toml
├── README.md
├── Makefile
└── AGENTS.mdRelated MCP server: mcp-canon
Running
# Start a single agent MCP server
uvicorn agents.manuscript_author.server:app --host 127.0.0.1 --port 8001
# Start all agents (one port each, 8001+)
make run-allOllama Models
brettanthonysjoberg179/brettapps:latest(Llama 3.2 3.2B Q4_K_M) — default agent modelgemma3:4b— fallback small modelgemma4:31b-cloud— large model for complex tasks
Agent Conventions
Each agent has
execute()returningdictwithstatus,message, and agent-specific dataMCP tools map 1:1 to agent capabilities
All agents use the shared Ollama client for LLM calls
This server cannot be deployed
Maintenance
ActivityMaintained
ResponsivenessNo issues
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