expert_personality_generationLABSCO SUMMARY
Several declared viewpoints on one question, instead of one averaged answer.
The traits are stored, not retyped: an agent defined once by first principles, systems thinking and risk management answers in that character every time you call it. Every answer goes through DeepSeek, so that key is where the cost lands.
What it is
A Go server that creates agents from named personality traits and simulates their answers through the DeepSeek API. Five tools cover creation, answering and management.
What you get
- An agent created from a name and a list of core traits — expert_personality_generation
- That agent's answer to a question, with the planned round count and the current round passed on each call — agent_answer
- One agent fetched, all agents listed, one deleted — get_agent, list_agents, delete_agent
- Multi-round dialogue, where the caller signals whether more rounds are needed
- A base for multi-agent research along the lines of a collaborative research project
Requirements
Go 1.24.1 or higher to build, and a DeepSeek API key in DEEPSEEK_API_KEY. Clone the repository and run make build, then point your client at the binary. LOG_LEVEL, LOG_PATH, CONFIG_PATH, PORT and DEBUG are optional.
Setup effort
Build from source — git clone https://github.com/HundunOnline/mcp-agent-forge.git
USE CASES
Stress-testing a decision
Create two agents with deliberately different traits and put the same question to both, so the disagreement is visible rather than averaged away.
Running a debate over several rounds
Each call carries the planned round count and the current round, so a position can develop instead of being stated once.
Reusing a persona across sessions
Agents persist and are fetchable by id, so the character you tuned is available next time rather than rebuilt from the prompt.
Clearing out the ones that did not work
Delete an agent by id and keep the roster small enough that the model picks sensibly between them.
5 tools
Listed in project docs. Your client may show a different list.
agent_answerget_agentlist_agentsdelete_agentBEFORE YOU INSTALL
What to know first
Tools you get
5 tools named in the project's documentation. expert_personality_generation creates a new agent from a name and core traits; delete_agent is the other write. agent_answer leaves multi-round debate to the caller via planned_rounds, current_round and need_more_rounds.
Requires
- Go 1.24.1 or later
- DEEPSEEK_API_KEY in your environment
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
You clone and build this one yourself.
Server setup
git clone https://github.com/HundunOnline/mcp-agent-forge.git
cd mcp-agent-forge && make buildMCP Clients
Any MCP client
{
"mcpServers": {
"mcp-agent-forge": {
"command": "<YOUR_PATH_TO_MCP_AGENT_FORGE>",
"env": {
"DEEPSEEK_API_KEY": "<YOUR_DEEPSEEK_API_KEY>"
}
}
}
}