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Description
Is your feature request related to a problem?
Thanks for this powerful, standalone tool for performing detailed research. However, because it's run from the command line, it's difficult to integrate as a "tool" into larger AI agent frameworks or multi-agent systems. A more sophisticated agent has no standardized way to discover what this tool does or how to call it.
Describe the solution you'd like
I propose adding a simple MCP (Model Context Protocol) server to this project.
This would expose the core research functionality as a callable tool over an API. When another agent queries the MCP endpoint, it would receive a structured response describing how to use the research function.
For example, the server could be started with npm run server
and would expose a context like this:
{
"functions": [
{
"name": "perform_deep_research",
"description": "Performs iterative, deep research on a given topic by combining search, web scraping, and LLMs to produce a detailed report.",
"parameters": {
"type": "object",
"properties": {
"topic": {
"type": "string",
"description": "The central topic or question to research."
},
"depth": {
"type": "integer",
"description": "How many recursive levels of research to perform (e.g., 2)."
},
"breadth": {
"type": "integer",
"description": "How many search results to explore at each level (e.g., 4)."
}
},
"required": ["topic"]
}
}
]
}
Why is this a good idea?
- Makes it a Composable Tool: This immediately turns
open-deep-research
from a script into a building block that can be used by other AI agents. - Standardized Integration: MCP is becoming a standard for agent-to-tool communication. Supporting it makes this project instantly compatible with a growing ecosystem.
- Increases Utility: It allows this project's powerful research capability to be called programmatically, unlocking its use in autonomous systems, chatbots, and more complex workflows.
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