google-research-mcp
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Here is a step-by-step guide with screenshots.
Google Research MCP Server v2.0.0 - Multi-Agent Architecture
An MCP server that implements Anthropic's Multi-Agent Research Architecture with true subagent spawning, adaptive stopping, and citation processing.
Architecture Overview
This implementation is fully compliant with Anthropic's multi-agent research system:
┌─────────────────────────────────────────────────────────────────┐
│ Multi-Agent Research System │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ LEAD RESEARCHER (Orchestrator) │ │
│ │ │ │
│ │ • think(plan approach) - Decompose into aspects │ │
│ │ • create subagents - Spawn parallel workers │ │
│ │ • think(synthesize) - Combine findings │ │
│ │ • evaluate coverage - "More research needed?" │ │
│ │ • complete_task - Return final report │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌───────────────┼───────────────┐ │
│ ▼ ▼ ▼ │
│ ┌────────────────┐ ┌────────────────┐ ┌────────────────┐ │
│ │ SUBAGENT 1 │ │ SUBAGENT 2 │ │ SUBAGENT N │ │
│ │ (Aspect A) │ │ (Aspect B) │ │ (Aspect N) │ │
│ │ │ │ │ │ │ │
│ │ • web_search │ │ • web_search │ │ • web_search │ │
│ │ • think(eval) │ │ • think(eval) │ │ • think(eval) │ │
│ │ • complete │ │ • complete │ │ • complete │ │
│ └────────────────┘ └────────────────┘ └────────────────┘ │
│ │ │ │ │
│ └───────────────┼───────────────┘ │
│ ▼ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ CITATION AGENT │ │
│ │ • Process documents │ │
│ │ • Identify citation locations │ │
│ │ • Insert inline citations [1], [2], etc. │ │
│ │ • Generate references section │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ MEMORY MODULE │ │
│ │ • save plan │ │
│ │ • retrieve context │ │
│ │ • persist findings │ │
│ │ • track gaps │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘Related MCP server: MCP Hub
Process Flow
Based on Anthropic's sequence diagram:
User LeadResearcher Subagent1 Subagent2 Memory CitationAgent
│ │ │ │ │ │
│──send user query────▶│ │ │ │ │
│ │ │ │ │ │
│ │◀─────────────────────────────────────────────────────│ │
│ │ think(plan approach) │ │
│ │ │ │ │ │
│ │──save plan────────────────────────────────────────▶│ │
│ │ │ │ │ │
│ │──retrieve context──────────────────────────────────▶│ │
│ │ │ │ │ │
│ │ │ │ │ │
│ │══════════════════════════════════════════════════════│ │
│ │ ITERATIVE RESEARCH LOOP │ │
│ │══════════════════════════════════════════════════════│ │
│ │ │ │ │ │
│ │──create subagent──▶│ │ │ │
│ │──create subagent────────────────────▶│ │ │
│ │ │ │ │ │
│ │ │──web_search────▶│ │ │
│ │ │◀───results──────│ │ │
│ │ │ │ │ │
│ │ │ think(evaluate)│ │ │
│ │ │ │ │ │
│ │◀──complete_task────│ │ │ │
│ │ │ │ │ │
│ │ │ │──web_search───▶│ │
│ │ │ │◀──results──────│ │
│ │ │ │ │ │
│ │ │ │ think(evaluate)│ │
│ │ │ │ │ │
│ │◀─────────────────────complete_task───│ │ │
│ │ │ │ │ │
│ │ think(synthesize results) │ │ │
│ │ │ │ │ │
│ │ ┌─────────────────────┐ │ │ │
│ │ │ More research needed?│ │ │ │
│ │ └─────────────────────┘ │ │ │
│ │ │ │ │ │ │
│ │ [Continue] [Exit Loop] │ │ │
│ │ │ │ │ │ │
│ │══════════════════════════════════════════════════════│ │
│ │ │ │ │ │
│ │──complete_task (research result)────────────────────▶│ │
│ │ │ │ │ │
│ │ │ │ │──────────────────▶│
│ │ │ │ │ Process docs + │
│ │ │ │ │ insert citations │
│ │◀───────────────────────────────────────────────────────────────────────│
│ │ │ │ │ Report with │
│ │ │ │ │ citations │
│ │──persist results──────────────────────────────────▶│ │
│ │ │ │ │ │
│◀──return research─────│ │ │ │ │
│ results with │ │ │ │ │
│ citations │ │ │ │ │Key Features
1. True Subagent Spawning
Each aspect gets its own subagent that runs independently:
Generates aspect-specific queries
Executes web searches
Fetches full page content
Evaluates findings
Reports back to Lead Researcher
2. Think/Evaluate Phases
Explicit reasoning phases between iterations:
think(plan approach)- Decompose topic into aspectsthink(evaluate)- Each subagent evaluates its findingsthink(synthesize)- Lead Researcher combines all findings
3. Adaptive Stopping
Dynamic "More research needed?" decision:
Coverage score calculation (0-100%)
Configurable thresholds per depth level
Gap identification and filling
Exits early when coverage is sufficient
4. Aspect-Based Decomposition
Topics are broken into researchable aspects:
Basic: 2 aspects (overview, mechanism)
Moderate: 5 aspects (+use cases, benefits, challenges)
Comprehensive: 11 aspects (+history, comparisons, implementation, future, research, case studies)
5. Memory Module
Persistent context across iterations:
Research plan storage
Findings per aspect
Gap tracking
Iteration history
6. Citation Agent
Dedicated citation processing:
Assigns citation IDs by quality
Inserts inline citations [1], [2]
Generates references section
Groups by quality tier
Tools
Tool | Description |
| Full multi-agent research with all components |
| Search + fetch full content (single iteration) |
| News-specific deep search |
| Fetch single page content |
| Simple search (snippets only) |
| Search with quality scoring |
| Manual session management |
| Manually spawn a subagent |
| Check coverage and gaps |
| Add source to session |
| Format citations |
Installation
{
"mcpServers": {
"google-research": {
"command": "npx",
"args": ["google-research-mcp"],
"env": {
"GOOGLE_API_KEY": "your-api-key",
"GOOGLE_CX": "your-search-engine-id"
}
}
}
}Prerequisites
1. Google API Key
Go to Google Cloud Console
Enable "Custom Search API"
Create an API Key
2. Search Engine ID (CX)
Create engine with "Search the entire web"
Copy the Search Engine ID
Usage Examples
Full Multi-Agent Research
"Research quantum computing with comprehensive depth"This triggers the full architecture:
Lead Researcher plans 11 aspects
Spawns 3-4 subagents per iteration
Each subagent researches in parallel
Evaluates coverage after each iteration
Continues until 90% coverage or max iterations
Citation Agent processes final report
Manual Subagent Control
// Create session
research_session({ action: "create", topic: "AI safety", depth: "moderate" })
// Spawn specific subagents
run_subagent({ sessionId: "rs_xxx", aspect: "AI alignment techniques" })
run_subagent({ sessionId: "rs_xxx", aspect: "AI safety research organizations" })
// Check coverage
evaluate_coverage({ sessionId: "rs_xxx" })
// Generate final report
research_session({ action: "complete", sessionId: "rs_xxx" })Depth Levels
Depth | Iterations | Aspects | Coverage Threshold | Min Sources/Aspect |
basic | 2 | 2 | 60% | 2 |
moderate | 3 | 5 | 75% | 3 |
comprehensive | 4 | 11 | 90% | 5 |
Source Quality Scoring
Based on Anthropic's source quality heuristics:
Score | Tier | Examples |
10 | Primary | .gov, .edu, arxiv, nature.com, PubMed, official docs |
8-9 | Authoritative | Wikipedia, Reuters, BBC, NYT, WSJ |
7 | Quality | Stack Overflow, TechCrunch, Wired |
5-6 | General | Medium, Dev.to, Substack |
1-4 | Low | Pinterest, Facebook, Twitter (deprioritized) |
Changelog
v2.0.0 - Multi-Agent Architecture (Anthropic Compliant)
NEW: True subagent spawning - Parallel workers for different aspects
NEW: Think/Evaluate phases - Explicit reasoning between iterations
NEW: Adaptive stopping - Dynamic "More research needed?" decision
NEW: Aspect-based decomposition - Topics broken into researchable aspects
NEW: Memory module - Persistent context across iterations
NEW: Citation Agent - Dedicated citation processing with inline insertion
NEW:
run_subagenttool - Manual subagent controlNEW:
evaluate_coveragetool - Check coverage and gapsNEW:
deep_search_newstool - News-specific deep searchImproved report generation with subagent reports
Full iteration history tracking
v1.2.0 - Deep Research Edition
Full page content fetching
Readability-style extraction
Source quality scoring
v1.0.0
Initial release
License
MIT
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