开源MCP工具

🚀 Quick Start

1. Clone and install:

git clone https://github.com/yogsoth-ai/de-anthropocentric-research-engine.git
cd de-anthropocentric-research-engine
npm install

1. Copy mcp.example.json to .mcp.json and fill in your API keys:

cp mcp.example.json .mcp.json

1. Configure your Claude Code project to use the skills:

{
  "permissions": {
    "allow": ["skill:*"]
  },
  "skills": {
    "path": "path/to/de-anthropocentric-research-engine/skills"
  }
}

1. Invoke the entry point:

/de-anthropocentric-research-engine

The orchestrator will guide you through North Star crystallization, then generate an executable Research Spec. To execute the spec later, invoke /executing-specs.

⚙️ Configuration

MCP Server Environment Variables

semantic-scholar (@yogsoth-ai/semantic-scholar-mcp)

wiki-vault (@yogsoth-ai/wiki-vault)

brave-search (@brave/brave-search-mcp-server)

apify (@apify/actors-mcp-server)

alphaxiv (HTTP — no local install)

No configuration needed. Connects directly to https://api.alphaxiv.org/mcp/v1.

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⚔️ Arsenal, Not Pipeline

Every existing autonomous research system — AI Scientist v2 (Sakana), AI-Researcher (HKUDS), Agent Laboratory, Dolphin, ARIS — implements a fixed pipeline: stages execute in a predetermined order, and the agent's autonomy is confined to local decisions within a single stage. Backtracking, when it exists at all, means retrying the current step — not returning from experiment design to literature review because the knowledge base turned out to be insufficient.

DARE is not a pipeline. It is an arsenal — a strategy book that the AI reads, then decides how to act.

What this means concretely:

In a pipeline system, the workflow is hardcoded: literature → gap → hypothesis → experiment. The agent has no say in the order, cannot skip stages, and cannot go back. If the experiment phase reveals that the literature review missed a critical subfield, the system has no mechanism to return and fix it.

In DARE, the Research Spec defines backtrack conditions for every stage — explicit rules like "if stress-test invalidates >50% of hypotheses, return to hypothesis-formation." The executing agent has full cross-stage routing authority: it reads the spec, assesses the current research state, and decides which campaign to invoke next, which strategies within that campaign to combine, and when the current path has failed hard enough to warrant retreat.

Within each campaign, the agent faces not one method but many. A gap-analysis campaign offers 15+ detection methods (coverage mapping, white-space identification, boundary unfolding, niche analysis...). A creative-ideation campaign offers 31+ generation techniques (SCAMPER, TRIZ, biomimicry, morphological analysis, concept blending...). The agent selects and combines methods based on the research context — not because "more is better," but because different research problems demand different tools, and a system locked to one approach per phase cannot adapt.

The human's role: approve the spec (including its backtrack conditions and recommended campaign combinations) before execution begins. After that, the agent navigates the research space autonomously within the ±10% deviation bounds defined in the spec. If it needs to deviate further — backtrack to an earlier stage, skip a stage entirely, or add one — it asks.

This is the fundamental architectural difference. Pipelines assume the research process is predictable. Arsenals assume it is not.