Percival Deep Research
Integrates DuckDuckGo as the default web search engine for research operations, providing fast raw snippet searches and multi-source web research capabilities without requiring an API key.
Utilizes OpenAI SDK transport architecture to connect with various LLM providers (Venice AI, MiniMax, OpenRouter) for research orchestration, requiring the 'openai:' prefix for model configuration regardless of the actual provider.
Click on "Install 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., "@Percival Deep Researchdeep research on quantum computing advancements in 2024"
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.
🤖 Percival Deep Research - percival.OS MCP
Version 0.0.2
📋 Description
Percival Deep Research is a highly capable MCP server designed to equip the Nanobot agent with autonomous, deep-dive web research capabilities. It explores and validates numerous sources, focusing only on relevant, trusted, and up-to-date information.
This server is part of the percival.OS ecosystem, a Personal Agentic Operating System designed for autonomy, security, and absolute privacy.
🛡️ percival.OS Principles
Like all components of percival.OS, this MCP server strictly follows our core principles:
Privacy & Governance: The entire research and synthesis process is governed by your API keys and local configurations.
Data Sovereignty: Knowledge extracted from the web is processed locally and integrated into your agent's context without external harvesting.
Hardened Security: We implement Defense-in-depth with strict input sanitization against prompt injection and isolation of untrusted web content.
Transparency: Based on the
GPT Researcherproject, but extensively refactored and hardened for the Percival ecosystem.
🚀 Features & Tools
Research Tools
research_deep(query): Start multi-source deep web research (30–120s). Returns aresearch_id.research_quick_search(query): Fast raw snippet search via DuckDuckGo (3–10s).research_write_report(research_id, custom_prompt?): Generates a structured Markdown report from an existing session.research_get_sources(research_id): Returns title, URL, and content size for all sources consulted.research_get_context(research_id): Returns the raw synthesized context text without generating a report.
Resources
research://{topic}: Access cached or live web research context for a topic directly as an MCP resource.
⚙️ Configuration in percival.OS (Nanobot)
This server is tuned to run via stdio. Add the following to your ~/.nanobot/config.json:
{
"mcpServers": {
"percival_deep_research": {
"command": "uv",
"args": [
"run",
"--no-sync",
"percival-deep-research"
],
"env": {
"OPENAI_API_KEY": "YOUR_KEY",
"OPENAI_BASE_URL": "https://api.venice.ai/api/v1",
"FAST_LLM": "venice:llama-3.3-70b",
"RETRIEVER": "duckduckgo"
},
"tool_timeout": 300
}
}
}🛠️ Development & Testing
This project uses the uv for dependency management within the unified percival.OS_Dev environment.
cd percival.OS_Dev
uv sync
uv run percival-deep-research📚 About the Project
This server is an integral module of the percival.OS project. It enables Nanobot to perform complex research tasks that require multiple steps of validation and synthesis.
Main Repository: https://github.com/bill-kopp-ai-dev/percival.OS
License: MIT
Developed with ❤️ by the percival.OS Team
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