Kiro Research MCP Server

A Claude Research-style MCP server implementing Anthropic's multi-agent research methodology that outperforms single-agent by 90.2%.

Features

• 🔍 Iterative Search - Start broad, then narrow (Claude Research methodology)
• 📊 Source Quality Scoring - Prioritizes primary sources over SEO content farms
• 📝 Citation Tracking - Automatic numbered citations [1], [2], etc.
• 📈 Scaling Rules - Built-in guidance for simple, comparison, and complex research
• 📋 Quality-Tiered Reports - Sources grouped by authority level

Quick Start

Installation

Use directly with npx (no installation required):

npx @thejusdutt/kiro-research-mcp

Or install globally:

npm install -g @thejusdutt/kiro-research-mcp

Configuration

Kiro IDE

Add to .kiro/settings/mcp.json:

{
  "mcpServers": {
    "research": {
      "command": "npx",
      "args": ["-y", "@thejusdutt/kiro-research-mcp"],
      "env": {
        "TAVILY_API_KEY": "your-tavily-api-key"
      },
      "autoApprove": ["web_search", "add_source", "get_citations", "research_session", "generate_report"]
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "research": {
      "command": "npx",
      "args": ["-y", "@thejusdutt/kiro-research-mcp"],
      "env": {
        "TAVILY_API_KEY": "your-tavily-api-key"
      }
    }
  }
}

Get a Tavily API Key

1. Go to tavily.com
2. Sign up for free (1,000 searches/month free)
3. Copy your API key

Claude Research Methodology

This server implements the research methodology described in Anthropic's engineering blog:

Architecture

┌─────────────────────────────────────────────────────────────┐
│                    LEAD RESEARCHER (You)                     │
│  Plans strategy → Coordinates searches → Synthesizes         │
└─────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        ▼                     ▼                     ▼
┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│   SUBAGENT 1  │   │   SUBAGENT 2  │   │   SUBAGENT 3  │
│  (Aspect A)   │   │  (Aspect B)   │   │  (Aspect C)   │
└───────────────┘   └───────────────┘   └───────────────┘
        │                     │                     │
        └─────────────────────┼─────────────────────┘
                              ▼
                    ┌───────────────┐
                    │ CITATION AGENT│
                    │ Validates all │
                    │ claims/sources│
                    └───────────────┘

Scaling Rules

| Task Type | Searches | Sources | Example | |-----------|----------|---------|---------| | Simple fact | 3-10 | 3-5 | "What is WebAssembly?" | | Comparison | 10-15/aspect | 4-6/aspect | "Compare React vs Vue" | | Complex research | 25+ | 10-15 | "State of AI in healthcare 2025" |

Source Quality Scoring

Anthropic found that agents often chose SEO content farms over authoritative sources. This server implements quality heuristics:

| Score | Tier | Examples | |-------|------|----------| | 10 | Primary | Official docs, research papers, company blogs | | 9 | Authoritative | .gov, .edu, major institutions | | 7 | Quality | Quality journalism, expert analysis | | 5 | General | General web content | | 3 | Low | SEO farms, social media (deprioritized) |

Search Strategy

1. Phase 1 - Broad Exploration: Start with 2-4 word queries
2. Phase 2 - Parallel Exploration: Search different aspects simultaneously
3. Phase 3 - Gap Filling: Target identified knowledge gaps
4. Phase 4 - Verification: Cross-reference key claims

Tools

web_search

Search the web with quality scoring.

Parameters:
- query: Search query (start broad, then refine)
- maxResults: Number of results (default: 10)
- sessionId: Session ID to track progress

add_source

Track a fetched URL as a citation source.

Parameters:
- sessionId: Research session ID
- url: URL of the source
- title: Title of the article
- author: (optional) Author name
- publishedDate: (optional) Publication date
- siteName: (optional) Site name

research_session

Manage research sessions with scaling guidance.

Actions:
- create: Start new session (provides scaling targets)
- update: Add findings and gaps
- status: Get progress metrics
- complete: Mark session done

get_citations

Get formatted citations.

Parameters:
- sessionId: Research session ID
- format: markdown, inline, apa, mla, chicago

generate_report

Generate a quality-tiered research report.

Parameters:
- sessionId: Research session ID
- title: (optional) Report title
- includeSources: Include sources section (default: true)
- includeGaps: Include gaps section (default: true)

Example Workflow

User: Research the current state of WebAssembly in 2025

1. Create session:
   research_session(action: "create", query: "WebAssembly 2025 state")
   → Returns scaling guidance: 10-15 searches, 5-8 sources

2. Broad search:
   web_search(query: "WebAssembly 2025", sessionId: "...")
   → Returns quality-scored results

3. Aspect searches:
   web_search(query: "WebAssembly server-side performance")
   web_search(query: "WebAssembly browser support 2025")
   web_search(query: "companies using WebAssembly production")

4. Fetch & track sources:
   mcp_fetch_fetch(url: "https://...")
   add_source(sessionId: "...", url: "...", title: "...")

5. Record findings:
   research_session(action: "update", findings: [...], gaps: [...])

6. Generate report:
   generate_report(sessionId: "...")
   → Quality-tiered report with citations

Environment Variables

| Variable | Required | Description | |----------|----------|-------------| | TAVILY_API_KEY | Yes | Tavily API key for web search |

Development

# Clone the repo
git clone https://github.com/thejusdutt/kiro-research-mcp.git
cd kiro-research-mcp

# Install dependencies
npm install

# Build
npm run build

# Run locally
node build/index.js

References

• How we built our multi-agent research system - Anthropic Engineering
• Model Context Protocol - MCP Documentation
• Tavily API - Search API

License

MIT © Thejus Dutt