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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 │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘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 |
|---|---|
google_research | Full multi-agent research with all components |
deep_search | Search + fetch full content (single iteration) |
deep_search_news | News-specific deep search |
fetch_page | Fetch single page content |
google_search | Simple search (snippets only) |
web_search | Search with quality scoring |
research_session | Manual session management |
run_subagent | Manually spawn a subagent |
evaluate_coverage | Check coverage and gaps |
add_source | Add source to session |
get_citations | 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)
- Go to Programmable Search Engine
- 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_subagent` tool - Manual subagent control
- NEW: `evaluate_coverage` tool - Check coverage and gaps
- NEW: `deep_search_news` tool - News-specific deep search
- Improved 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