N
nguyenvanduocit/research-kit
Toolkit to help you get started with Spec-Driven Research
View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site
checked 2026-11-14 — this package is not on npm/PyPI (E404); the command below fails as written.
Install & config
- Copy the command above
- Paste it as one entry under
mcpServersinclaude_desktop_config.json— local servers take command + args (example: clients) - Restart your client
Client config (built from the install command this repo gives)
{
"mcpServers": {
"nguyenvanduocit-research-kit": {
"command": "uv",
"args": ["tool", "install", "research-cli"]
}
}
}Parsed from the README we fetched on 2026-11-07; we have not executed it.
command/args come from the install command the repository states. When it is ambiguous, the project README wins.
VS Code (.vscode/mcp.json - the key is servers)
{
"servers": {
"nguyenvanduocit-research-kit": {
"command": "uv",
"args": ["tool", "install", "research-cli"]
}
}
}Same server, but VS Code keeps it under <code>servers</code>.
Claude Code (command line)
claude mcp add --transport stdio nguyenvanduocit-research-kit -- uv tool install research-cli
Claude Code does not read a config file; add the server with this command.
Key names differ by client: Clients & CLI
- Source
- collected from the abordage/awesome-mcp list · type: MCP server
- Harvest history
- first seen 2026-11-05 · last seen 2026-11-14 · seen 10× · listed by 10 sources
- Score
- score 1,003 · 10 sources · mcp
- Install
uv tool install research-cli
FAQ (answers come from our records)
How do I install it?
The repository provides this install command:
uv tool install research-cli. It comes from the repository itself; we cannot guarantee it runs as-is in your client.Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
first catalogued 2026-11-05；last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.
Research Kit
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
Deep Research Toolkit for AI Agents
An open source toolkit for conducting structured, rigorous research with AI agents. Move beyond ad-hoc queries to systematic investigation with quality standards and reproducible methodologies.
## Origin & Motivation
Research Kit is a fork of Spec Kit by GitHub, originally built as a personal project to adapt the Specification-Driven Development (SDD) workflow for systematic research.
The deeper motivation behind this project is to demonstrate a simple but powerful idea: AI agents perform significantly better when guided by multiple structured phases and template constraints. Instead of asking an AI to "do research" in a single open-ended prompt, Research Kit breaks the process into 10 distinct phases, each with its own template, quality standards, and output expectations. This structured approach:
Reduces hallucination by constraining AI output to specific, well-defined templates at each phase
Improves consistency by enforcing the same methodology across different research topics
Enables quality control by making each phase reviewable before proceeding to the next
Produces better results by giving the AI clear context boundaries instead of unbounded instructions
If you've ever noticed that AI gives better answers when you break a complex question into smaller parts, Research Kit applies that principle systematically to the entire research workflow.
For details on what changed from the upstream Spec Kit, see The Fork.
## Table of Contents
Origin & Motivation
What is Research Kit?
The Problem
Who It's For
Key Features
Quick Start
Research Workflow
Research Types
Supported AI Agents
Installation
Research CLI Reference
Example Output
Documentation
Prerequisites
Troubleshooting
Contributing
License
## What is Research Kit?
Research Kit is a structured research framework designed for AI agents. It transforms ad-hoc prompting into systematic investigation by providing:
Multi-phase research workflow - From defining research questions to executing comprehensive studies
Quality standards enforcement - Built-in principles for source evaluation, methodology rigor, and bias mitigation
Research type specialization - Tailored approaches for academic, market, technical, and general research
Reproducible methodologies - Documented approaches that can be validated and replicated
Citation management - Integrated BibTeX support for proper source attribution
AI agent integration - Works with Claude Code and Codex CLI
## The Problem
Traditional AI interactions treat research as a single-shot query: you ask a question, get an answer, and hope it's accurate. This approach struggles with:
Lack of source transparency - Where did this information come from?
No methodology documentation - How was this answer derived?
Inconsistent quality - Results vary wildly between queries
No bias mitigation - Unexamined assumptions baked into responses
Poor reproducibility - Can't validate or build on previous research
Research Kit solves this by making research a structured, auditable process rather than a black-box interaction.
## Who It's For
Research Kit is designed for anyone who needs rigorous, documented research:
An open source toolkit for conducting structured, rigorous research with AI agents. Move beyond ad-hoc queries to systematic investigation with quality standards and reproducible methodologies.
## Origin & Motivation
Research Kit is a fork of Spec Kit by GitHub, originally built as a personal project to adapt the Specification-Driven Development (SDD) workflow for systematic research.
The deeper motivation behind this project is to demonstrate a simple but powerful idea: AI agents perform significantly better when guided by multiple structured phases and template constraints. Instead of asking an AI to "do research" in a single open-ended prompt, Research Kit breaks the process into 10 distinct phases, each with its own template, quality standards, and output expectations. This structured approach:
Reduces hallucination by constraining AI output to specific, well-defined templates at each phase
Improves consistency by enforcing the same methodology across different research topics
Enables quality control by making each phase reviewable before proceeding to the next
Produces better results by giving the AI clear context boundaries instead of unbounded instructions
If you've ever noticed that AI gives better answers when you break a complex question into smaller parts, Research Kit applies that principle systematically to the entire research workflow.
For details on what changed from the upstream Spec Kit, see The Fork.
## Table of Contents
Origin & Motivation
What is Research Kit?
The Problem
Who It's For
Key Features
Quick Start
Research Workflow
Research Types
Supported AI Agents
Installation
Research CLI Reference
Example Output
Documentation
Prerequisites
Troubleshooting
Contributing
License
## What is Research Kit?
Research Kit is a structured research framework designed for AI agents. It transforms ad-hoc prompting into systematic investigation by providing:
Multi-phase research workflow - From defining research questions to executing comprehensive studies
Quality standards enforcement - Built-in principles for source evaluation, methodology rigor, and bias mitigation
Research type specialization - Tailored approaches for academic, market, technical, and general research
Reproducible methodologies - Documented approaches that can be validated and replicated
Citation management - Integrated BibTeX support for proper source attribution
AI agent integration - Works with Claude Code and Codex CLI
## The Problem
Traditional AI interactions treat research as a single-shot query: you ask a question, get an answer, and hope it's accurate. This approach struggles with:
Lack of source transparency - Where did this information come from?
No methodology documentation - How was this answer derived?
Inconsistent quality - Results vary wildly between queries
No bias mitigation - Unexamined assumptions baked into responses
Poor reproducibility - Can't validate or build on previous research
Research Kit solves this by making research a structured, auditable process rather than a black-box interaction.
## Who It's For
Research Kit is designed for anyone who needs rigorous, documented research:
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