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MCP Integration Workflows and Orchestration Guide
title: MCP Integration Workflows and Orchestration Guide
ai mcp claude workflow automation
View sourcetitle: MCP Integration Workflows and Orchestration Guide description: Comprehensive guide for Model Context Protocol (MCP) server integration, orchestration patterns, and advanced workflows status: active created: 2025-09-10 updated: 2025-09-10 tags: [mcp, model-context-protocol, integration, orchestration, workflows, automation]
MCP Integration Workflows and Orchestration Guide
Overview
This comprehensive guide provides enterprise-grade integration patterns, orchestration strategies, and advanced workflows for Model Context Protocol (MCP) servers. Based on proven implementations including arxiv-mcp, deep-research, deep-code-research, and multi-server orchestration, this guide addresses complex integration challenges from simple server connections to sophisticated workflow automation.
Table of Contents
- MCP Integration Workflows and Orchestration Guide
MCP Architecture and Principles
Core MCP Concepts
graph TB
A[Client Application<br/>VS Code, Claude] --> B[MCP Client Layer]
B --> C[Protocol Handler<br/>JSON-RPC over stdio/HTTP]
C --> D[MCP Server Registry]
D --> E[arxiv-mcp<br/>Academic Research]
D --> F[deep-research<br/>Web Research]
D --> G[deep-code-research<br/>Code Analysis]
D --> H[github-mcp<br/>Repository Management]
D --> I[memory-mcp<br/>Knowledge Graphs]
E --> J[ArXiv API]
F --> K[Search Engines]
G --> L[Code Repositories]
H --> M[GitHub API]
I --> N[Vector Databases]
MCP Protocol Standards
1. Server Registration and Discovery
{
"mcpServers": {
"arxiv-mcp": {
"command": "uvicorn",
"args": ["arxiv_mcp.server:app", "--port", "3001"],
"env": {
"ARXIV_API_KEY": "${ARXIV_API_KEY}",
"CACHE_DIR": "${WORKSPACE_ROOT}/cache/arxiv"
}
},
"deep-research": {
"command": "node",
"args": ["dist/index.js"],
"env": {
"RESEARCH_API_KEY": "${RESEARCH_API_KEY}",
"MAX_DEPTH": "5",
"CACHE_ENABLED": "true"
}
},
"github-integration": {
"command": "python",
"args": ["-m", "github_mcp"],
"env": {
"GITHUB_TOKEN": "${GITHUB_TOKEN}",
"REPO_CACHE_SIZE": "1000"
}
}
}
}
2. Protocol Communication Patterns
interface MCPMessage {
jsonrpc: "2.0";
id?: string | number;
method?: string;
params?: any;
result?: any;
error?: {
code: number;
message: string;
data?: any;
};
}
// Tool invocation pattern
interface ToolInvocation {
name: string;
arguments: Record<string, any>;
timeout?: number;
retries?: number;
}
// Resource access pattern
interface ResourceAccess {
uri: string;
method: "GET" | "POST" | "PUT" | "DELETE";
headers?: Record<string, string>;
body?: any;
}
Server Capability Matrix
| Server | Research | Code Analysis | Repository Mgmt | Knowledge Graphs | Documentation |
|---|---|---|---|---|---|
| arxiv-mcp | ✅ Academic | ❌ | ❌ | ❌ | ✅ Paper Content |
| deep-research | ✅ Web | ❌ | ❌ | ❌ | ✅ Research Reports |
| deep-code-research | ❌ | ✅ Full Stack | ✅ Analysis | ❌ | ✅ Code Docs |
| github-mcp | ❌ | ✅ Repository | ✅ Full GitHub | ❌ | ✅ README/Wiki |
| memory-mcp | ❌ | ❌ | ❌ | ✅ Full Graph | ✅ Knowledge Base |
| pylance-mcp | ❌ | ✅ Python | ❌ | ❌ | ✅ API Docs |
Single Server Integration Patterns
ArXiv-MCP Integration
Basic Academic Research Workflow
# Academic research pattern with arxiv-mcp
class AcademicResearchWorkflow:
"""Structured workflow for academic research using arxiv-mcp."""
def __init__(self, mcp_client):
self.arxiv = mcp_client.get_server("arxiv-mcp")
self.research_context = {}
async def search_papers(self, topic: str, max_results: int = 10):
"""Search for papers on a specific topic."""
search_results = await self.arxiv.invoke_tool(
"search_arxiv",
{
"all_fields": topic,
"start": 0,
"max_results": max_results
}
)
self.research_context["search_results"] = search_results
return search_results
async def analyze_paper(self, paper_title: str):
"""Load and analyze a specific paper."""
# Get paper details
paper_details = await self.arxiv.invoke_tool(
"get_details",
{"title": paper_title}
)
# Load full paper content
paper_content = await self.arxiv.invoke_tool(
"load_article_to_context",
{"title": paper_title}
)
# Store in research context
self.research_context["current_paper"] = {
"details": paper_details,
"content": paper_content
}
return paper_details, paper_content
async def download_paper(self, paper_title: str, download_dir: str):
"""Download paper for offline analysis."""
download_result = await self.arxiv.invoke_tool(
"download_article",
{"title": paper_title}
)
return download_result
# Usage example
async def research_machine_learning():
"""Example: Research machine learning transformer architectures."""
workflow = AcademicResearchWorkflow(mcp_client)
# Phase 1: Search for relevant papers
papers = await workflow.search_papers(
"transformer architecture attention mechanism",
max_results=20
)
# Phase 2: Analyze key papers
key_papers = [
"Attention Is All You Need",
"BERT: Pre-training of Deep Bidirectional Transformers",
"GPT-3: Language Models are Few-Shot Learners"
]
for paper_title in key_papers:
details, content = await workflow.analyze_paper(paper_title)
print(f"Analyzed: {paper_title}")
print(f"Authors: {details.get('authors', 'N/A')}")
print(f"Published: {details.get('published', 'N/A')}")
print("---")
Deep-Research Integration
Comprehensive Web Research Pattern
class DeepResearchWorkflow:
"""Advanced web research workflow with deep-research MCP."""
def __init__(self, mcp_client):
self.deep_research = mcp_client.get_server("deep-research")
self.research_cache = {}
async def conduct_research(
self,
query: str,
depth: int = 4,
breadth: int = 4,
token_budget: int = 50000,
source_preferences: str = None
):
"""Conduct comprehensive research on a topic."""
research_params = {
"query": query,
"depth": depth,
"breadth": breadth,
"tokenBudget": token_budget
}
if source_preferences:
research_params["sourcePreferences"] = source_preferences
research_result = await self.deep_research.invoke_tool(
"deep-research",
research_params
)
# Cache results for later use
self.research_cache[query] = research_result
return research_result
async def layered_research_strategy(self, primary_topic: str):
"""Multi-layered research strategy for complex topics."""
# Layer 1: Broad overview
overview = await self.conduct_research(
f"{primary_topic} overview fundamentals",
depth=2,
breadth=5,
token_budget=20000
)
# Layer 2: Technical deep-dive
technical = await self.conduct_research(
f"{primary_topic} technical implementation best practices",
depth=4,
breadth=3,
token_budget=30000,
source_preferences="avoid marketing content, prioritize technical documentation"
)
# Layer 3: Current trends and innovations
trends = await self.conduct_research(
f"{primary_topic} 2024 innovations trends future",
depth=3,
breadth=4,
token_budget=25000,
source_preferences="recent publications, avoid outdated sources"
)
return {
"overview": overview,
"technical": technical,
"trends": trends
}
# Example: Research modern documentation practices
async def research_documentation_practices():
"""Research modern documentation strategies and tools."""
workflow = DeepResearchWorkflow(mcp_client)
research_results = await workflow.layered_research_strategy(
"software documentation best practices"
)
# Extract key insights
for layer, result in research_results.items():
print(f"\n=== {layer.upper()} RESEARCH ===")
print(f"Query: {result.get('query', 'N/A')}")
print(f"Sources: {len(result.get('sources', []))}")
print(f"Key findings: {result.get('summary', 'N/A')[:200]}...")
Multi-Server Orchestration
Orchestration Framework
class MCPOrchestrator:
"""Advanced orchestration framework for multiple MCP servers."""
def __init__(self):
self.servers = {}
self.workflow_state = {}
self.execution_graph = {}
def register_server(self, name: str, server_client):
"""Register an MCP server for orchestration."""
self.servers[name] = server_client
async def execute_workflow(self, workflow_definition: dict):
"""Execute a complex workflow across multiple servers."""
workflow_id = workflow_definition.get("id")
self.workflow_state[workflow_id] = {"status": "running", "steps": []}
try:
for step in workflow_definition.get("steps", []):
step_result = await self._execute_step(step)
self.workflow_state[workflow_id]["steps"].append({
"step": step,
"result": step_result,
"timestamp": datetime.now().isoformat()
})
self.workflow_state[workflow_id]["status"] = "completed"
return self.workflow_state[workflow_id]
except Exception as e:
self.workflow_state[workflow_id]["status"] = "failed"
self.workflow_state[workflow_id]["error"] = str(e)
raise
async def _execute_step(self, step: dict):
"""Execute a single workflow step."""
server_name = step.get("server")
tool_name = step.get("tool")
parameters = step.get("parameters", {})
# Parameter substitution from previous steps
parameters = self._substitute_parameters(parameters)
server = self.servers.get(server_name)
if not server:
raise ValueError(f"Server {server_name} not registered")
return await server.invoke_tool(tool_name, parameters)
def _substitute_parameters(self, parameters: dict):
"""Substitute parameters from previous workflow steps."""
# Implementation for parameter substitution logic
return parameters
# Orchestration workflow definitions
RESEARCH_TO_DOCUMENTATION_WORKFLOW = {
"id": "research-to-docs",
"description": "Complete research to documentation pipeline",
"steps": [
{
"name": "academic_search",
"server": "arxiv-mcp",
"tool": "search_arxiv",
"parameters": {
"all_fields": "{{topic}}",
"start": 0
}
},
{
"name": "web_research",
"server": "deep-research",
"tool": "deep-research",
"parameters": {
"query": "{{topic}} implementation best practices",
"depth": 4,
"breadth": 4
}
},
{
"name": "code_analysis",
"server": "github-mcp",
"tool": "search_repositories",
"parameters": {
"query": "{{topic}} language:python stars:>100"
}
},
{
"name": "store_knowledge",
"server": "memory-mcp",
"tool": "create_entities",
"parameters": {
"entities": [
{
"name": "{{topic}}_research",
"entityType": "research_topic",
"observations": ["{{academic_search.result}}", "{{web_research.result}}"]
}
]
}
}
]
}
Advanced Integration Patterns
1. Research Aggregation Pattern
class ResearchAggregationOrchestrator(MCPOrchestrator):
"""Specialized orchestrator for aggregating research from multiple sources."""
async def comprehensive_research(
self,
topic: str,
include_academic: bool = True,
include_web: bool = True,
include_code: bool = True
):
"""Aggregate research from all available sources."""
research_tasks = []
if include_academic and "arxiv-mcp" in self.servers:
research_tasks.append(
self._academic_research(topic)
)
if include_web and "deep-research" in self.servers:
research_tasks.append(
self._web_research(topic)
)
if include_code and "github-mcp" in self.servers:
research_tasks.append(
self._code_research(topic)
)
# Execute all research tasks concurrently
research_results = await asyncio.gather(*research_tasks, return_exceptions=True)
# Aggregate and synthesize results
aggregated_research = await self._synthesize_research(topic, research_results)
# Store in knowledge graph
if "memory-mcp" in self.servers:
await self._store_research_knowledge(topic, aggregated_research)
return aggregated_research
async def _academic_research(self, topic: str):
"""Conduct academic research via arxiv-mcp."""
arxiv = self.servers["arxiv-mcp"]
# Search for papers
search_results = await arxiv.invoke_tool(
"search_arxiv",
{"all_fields": topic, "start": 0}
)
# Get details for top papers
papers = []
for paper_title in list(search_results.keys())[:5]:
details = await arxiv.invoke_tool(
"get_details",
{"title": paper_title}
)
papers.append(details)
return {
"source": "academic",
"search_results": search_results,
"detailed_papers": papers
}
async def _web_research(self, topic: str):
"""Conduct web research via deep-research."""
deep_research = self.servers["deep-research"]
result = await deep_research.invoke_tool(
"deep-research",
{
"query": f"{topic} comprehensive guide best practices",
"depth": 4,
"breadth": 4,
"tokenBudget": 40000
}
)
return {
"source": "web",
"research_result": result
}
async def _code_research(self, topic: str):
"""Research code implementations via github-mcp."""
github = self.servers["github-mcp"]
# Search repositories
repos = await github.invoke_tool(
"search_repositories",
{"query": f"{topic} language:python stars:>50"}
)
# Search code examples
code_examples = await github.invoke_tool(
"search_code",
{"query": f"{topic} path:*.py"}
)
return {
"source": "code",
"repositories": repos,
"code_examples": code_examples
}
async def _synthesize_research(self, topic: str, research_results: list):
"""Synthesize research from multiple sources."""
synthesis = {
"topic": topic,
"sources": [],
"key_findings": [],
"implementation_examples": [],
"academic_foundation": None,
"practical_guidance": None
}
for result in research_results:
if isinstance(result, Exception):
continue
source_type = result.get("source")
synthesis["sources"].append(source_type)
if source_type == "academic":
synthesis["academic_foundation"] = result
elif source_type == "web":
synthesis["practical_guidance"] = result
elif source_type == "code":
synthesis["implementation_examples"] = result
return synthesis
2. Workflow Pipeline Pattern
class WorkflowPipeline:
"""Pipeline pattern for chaining MCP operations."""
def __init__(self, orchestrator: MCPOrchestrator):
self.orchestrator = orchestrator
self.pipeline_steps = []
def add_step(self, server: str, tool: str, parameters: dict = None, condition: callable = None):
"""Add a step to the pipeline."""
self.pipeline_steps.append({
"server": server,
"tool": tool,
"parameters": parameters or {},
"condition": condition
})
return self
def add_parallel_steps(self, steps: list):
"""Add multiple steps to be executed in parallel."""
self.pipeline_steps.append({
"type": "parallel",
"steps": steps
})
return self
async def execute(self, initial_context: dict = None):
"""Execute the pipeline."""
context = initial_context or {}
results = []
for step in self.pipeline_steps:
if step.get("type") == "parallel":
# Execute parallel steps
parallel_tasks = []
for parallel_step in step["steps"]:
if self._should_execute_step(parallel_step, context):
task = self._execute_single_step(parallel_step, context)
parallel_tasks.append(task)
parallel_results = await asyncio.gather(*parallel_tasks, return_exceptions=True)
results.extend(parallel_results)
# Update context with parallel results
for i, result in enumerate(parallel_results):
if not isinstance(result, Exception):
step_name = step["steps"][i].get("name", f"parallel_step_{i}")
context[step_name] = result
else:
# Execute single step
if self._should_execute_step(step, context):
result = await self._execute_single_step(step, context)
results.append(result)
# Update context
step_name = step.get("name", f"step_{len(results)}")
context[step_name] = result
return {
"results": results,
"final_context": context
}
def _should_execute_step(self, step: dict, context: dict) -> bool:
"""Check if a step should be executed based on conditions."""
condition = step.get("condition")
if condition:
return condition(context)
return True
async def _execute_single_step(self, step: dict, context: dict):
"""Execute a single pipeline step."""
server_name = step["server"]
tool_name = step["tool"]
parameters = step["parameters"].copy()
# Substitute context variables in parameters
parameters = self._substitute_context_variables(parameters, context)
server = self.orchestrator.servers[server_name]
return await server.invoke_tool(tool_name, parameters)
def _substitute_context_variables(self, parameters: dict, context: dict) -> dict:
"""Substitute context variables in parameters."""
def substitute_value(value):
if isinstance(value, str) and value.startswith("{{") and value.endswith("}}"):
var_name = value[2:-2].strip()
return context.get(var_name, value)
elif isinstance(value, dict):
return {k: substitute_value(v) for k, v in value.items()}
elif isinstance(value, list):
return [substitute_value(item) for item in value]
return value
return {k: substitute_value(v) for k, v in parameters.items()}
# Example: Documentation generation pipeline
async def create_documentation_pipeline():
"""Create a pipeline for generating comprehensive documentation."""
orchestrator = MCPOrchestrator()
# Register servers (assumed to be done elsewhere)
pipeline = WorkflowPipeline(orchestrator)
# Step 1: Research the topic
pipeline.add_parallel_steps([
{
"name": "academic_research",
"server": "arxiv-mcp",
"tool": "search_arxiv",
"parameters": {"all_fields": "{{topic}}", "start": 0}
},
{
"name": "web_research",
"server": "deep-research",
"tool": "deep-research",
"parameters": {
"query": "{{topic}} documentation best practices",
"depth": 3,
"breadth": 3
}
}
])
# Step 2: Analyze code examples
pipeline.add_step(
"github-mcp",
"search_code",
{"query": "{{topic}} documentation examples"},
condition=lambda ctx: "web_research" in ctx
)
# Step 3: Store knowledge
pipeline.add_step(
"memory-mcp",
"create_entities",
{
"entities": [{
"name": "{{topic}}_documentation",
"entityType": "documentation_research",
"observations": ["Research completed for {{topic}}"]
}]
}
)
return pipeline
Workflow Automation Patterns
Event-Driven Orchestration
class EventDrivenOrchestrator(MCPOrchestrator):
"""Event-driven orchestration for reactive workflows."""
def __init__(self):
super().__init__()
self.event_handlers = {}
self.event_queue = asyncio.Queue()
self.running = False
def register_event_handler(self, event_type: str, handler: callable):
"""Register an event handler."""
if event_type not in self.event_handlers:
self.event_handlers[event_type] = []
self.event_handlers[event_type].append(handler)
async def emit_event(self, event_type: str, data: dict):
"""Emit an event to the queue."""
event = {
"type": event_type,
"data": data,
"timestamp": datetime.now().isoformat()
}
await self.event_queue.put(event)
async def start_event_loop(self):
"""Start the event processing loop."""
self.running = True
while self.running:
try:
# Wait for event with timeout
event = await asyncio.wait_for(
self.event_queue.get(),
timeout=1.0
)
await self._process_event(event)
except asyncio.TimeoutError:
continue
except Exception as e:
print(f"Error processing event: {e}")
async def _process_event(self, event: dict):
"""Process a single event."""
event_type = event["type"]
handlers = self.event_handlers.get(event_type, [])
for handler in handlers:
try:
await handler(event["data"])
except Exception as e:
print(f"Error in event handler: {e}")
# Event-driven workflow example
async def setup_research_automation():
"""Set up automated research workflows triggered by events."""
orchestrator = EventDrivenOrchestrator()
# Handler for new research topics
async def handle_new_research_topic(data):
topic = data.get("topic")
if not topic:
return
# Trigger comprehensive research
research_workflow = ResearchAggregationOrchestrator()
result = await research_workflow.comprehensive_research(topic)
# Emit research completed event
await orchestrator.emit_event("research_completed", {
"topic": topic,
"result": result
})
# Handler for completed research
async def handle_research_completed(data):
topic = data.get("topic")
result = data.get("result")
# Generate documentation
# This would trigger documentation generation workflow
print(f"Research completed for {topic}, generating documentation...")
# Register event handlers
orchestrator.register_event_handler("new_research_topic", handle_new_research_topic)
orchestrator.register_event_handler("research_completed", handle_research_completed)
return orchestrator
Scheduled Workflow Management
class ScheduledWorkflowManager:
"""Manager for scheduled and recurring workflows."""
def __init__(self, orchestrator: MCPOrchestrator):
self.orchestrator = orchestrator
self.scheduled_tasks = {}
self.cron_jobs = {}
def schedule_workflow(
self,
workflow_id: str,
workflow_definition: dict,
schedule: str,
timezone: str = "UTC"
):
"""Schedule a workflow using cron-like syntax."""
from croniter import croniter
self.cron_jobs[workflow_id] = {
"workflow": workflow_definition,
"schedule": schedule,
"timezone": timezone,
"next_run": croniter(schedule, datetime.now()).get_next(datetime)
}
async def start_scheduler(self):
"""Start the workflow scheduler."""
while True:
current_time = datetime.now()
# Check for due workflows
for workflow_id, job in self.cron_jobs.items():
if current_time >= job["next_run"]:
# Execute workflow
try:
await self.orchestrator.execute_workflow(job["workflow"])
print(f"Executed scheduled workflow: {workflow_id}")
except Exception as e:
print(f"Error executing workflow {workflow_id}: {e}")
# Update next run time
from croniter import croniter
job["next_run"] = croniter(job["schedule"], current_time).get_next(datetime)
# Sleep for a minute before checking again
await asyncio.sleep(60)
# Example: Scheduled research updates
DAILY_RESEARCH_UPDATE = {
"id": "daily-research-update",
"description": "Daily update of research topics",
"steps": [
{
"name": "check_trending_topics",
"server": "deep-research",
"tool": "deep-research",
"parameters": {
"query": "trending technology 2024",
"depth": 2,
"breadth": 5
}
},
{
"name": "update_knowledge_base",
"server": "memory-mcp",
"tool": "add_observations",
"parameters": {
"observations": [{
"entityName": "daily_research",
"contents": ["{{check_trending_topics.result}}"]
}]
}
}
]
}
Research and Documentation Workflows
Comprehensive Research Documentation Pipeline
class ResearchDocumentationPipeline:
"""Complete pipeline from research to publication-ready documentation."""
def __init__(self, orchestrator: MCPOrchestrator):
self.orchestrator = orchestrator
self.research_cache = {}
self.document_templates = {}
async def generate_comprehensive_documentation(
self,
topic: str,
documentation_type: str = "guide",
output_format: str = "markdown"
):
"""Generate comprehensive documentation from research."""
# Phase 1: Multi-source research
research_data = await self._conduct_comprehensive_research(topic)
# Phase 2: Content synthesis and structuring
structured_content = await self._synthesize_content(topic, research_data)
# Phase 3: Documentation generation
documentation = await self._generate_documentation(
topic,
structured_content,
documentation_type,
output_format
)
# Phase 4: Quality assurance
validated_documentation = await self._validate_documentation(documentation)
# Phase 5: Knowledge graph integration
await self._store_documentation_knowledge(topic, validated_documentation)
return validated_documentation
async def _conduct_comprehensive_research(self, topic: str):
"""Conduct research across all available MCP servers."""
research_tasks = {}
# Academic research
if "arxiv-mcp" in self.orchestrator.servers:
research_tasks["academic"] = self._academic_research_task(topic)
# Web research
if "deep-research" in self.orchestrator.servers:
research_tasks["web"] = self._web_research_task(topic)
# Code research
if "github-mcp" in self.orchestrator.servers:
research_tasks["code"] = self._code_research_task(topic)
# Documentation research
if "deepwiki" in self.orchestrator.servers:
research_tasks["documentation"] = self._documentation_research_task(topic)
# Execute all research tasks
research_results = {}
for research_type, task in research_tasks.items():
try:
research_results[research_type] = await task
except Exception as e:
print(f"Research task {research_type} failed: {e}")
research_results[research_type] = None
return research_results
async def _academic_research_task(self, topic: str):
"""Conduct academic research via arxiv-mcp."""
arxiv = self.orchestrator.servers["arxiv-mcp"]
# Search for papers
search_result = await arxiv.invoke_tool(
"search_arxiv",
{
"all_fields": topic,
"start": 0
}
)
# Get detailed information for top papers
papers_details = []
for paper_title in list(search_result.keys())[:10]:
try:
details = await arxiv.invoke_tool(
"get_details",
{"title": paper_title}
)
papers_details.append(details)
except Exception as e:
print(f"Failed to get details for {paper_title}: {e}")
return {
"search_results": search_result,
"paper_details": papers_details
}
async def _web_research_task(self, topic: str):
"""Conduct web research via deep-research."""
deep_research = self.orchestrator.servers["deep-research"]
# Comprehensive research with high token budget
result = await deep_research.invoke_tool(
"deep-research",
{
"query": f"{topic} comprehensive guide implementation best practices",
"depth": 5,
"breadth": 4,
"tokenBudget": 60000,
"sourcePreferences": "prioritize technical documentation, avoid marketing content"
}
)
return result
async def _code_research_task(self, topic: str):
"""Research code implementations and examples."""
github = self.orchestrator.servers["github-mcp"]
# Search for repositories
repos = await github.invoke_tool(
"search_repositories",
{
"query": f"{topic} language:python stars:>10",
"perPage": 20
}
)
# Search for code examples
code_examples = await github.invoke_tool(
"search_code",
{
"query": f"{topic} extension:py",
"perPage": 30
}
)
return {
"repositories": repos,
"code_examples": code_examples
}
async def _synthesize_content(self, topic: str, research_data: dict):
"""Synthesize research data into structured content."""
synthesis = {
"topic": topic,
"overview": None,
"academic_foundation": None,
"practical_implementation": None,
"code_examples": None,
"best_practices": None,
"tools_and_frameworks": None,
"references": []
}
# Process academic research
if research_data.get("academic"):
academic = research_data["academic"]
synthesis["academic_foundation"] = {
"key_papers": academic.get("paper_details", [])[:5],
"research_trends": self._extract_research_trends(academic)
}
# Add academic references
for paper in academic.get("paper_details", []):
if isinstance(paper, dict) and "title" in paper:
synthesis["references"].append({
"type": "academic",
"title": paper.get("title"),
"authors": paper.get("authors"),
"url": paper.get("link"),
"published": paper.get("published")
})
# Process web research
if research_data.get("web"):
web_research = research_data["web"]
synthesis["overview"] = self._extract_overview(web_research)
synthesis["best_practices"] = self._extract_best_practices(web_research)
synthesis["tools_and_frameworks"] = self._extract_tools(web_research)
# Process code research
if research_data.get("code"):
code_research = research_data["code"]
synthesis["code_examples"] = self._extract_code_examples(code_research)
synthesis["practical_implementation"] = self._extract_implementation_patterns(code_research)
return synthesis
def _extract_research_trends(self, academic_data: dict) -> list:
"""Extract research trends from academic papers."""
trends = []
papers = academic_data.get("paper_details", [])
# Analyze publication dates and topics
recent_papers = [p for p in papers if isinstance(p, dict) and
p.get("published", "").startswith(("2023", "2024"))]
if recent_papers:
trends.append(f"Recent research focus: {len(recent_papers)} papers published in 2023-2024")
return trends
def _extract_overview(self, web_research: dict) -> str:
"""Extract overview from web research."""
if isinstance(web_research, dict):
return web_research.get("summary", "")[:500] + "..."
return ""
def _extract_best_practices(self, web_research: dict) -> list:
"""Extract best practices from web research."""
# This would contain logic to parse best practices from research
return ["Practice 1", "Practice 2", "Practice 3"] # Placeholder
def _extract_tools(self, web_research: dict) -> list:
"""Extract tools and frameworks from web research."""
# This would contain logic to identify tools mentioned in research
return ["Tool 1", "Tool 2", "Tool 3"] # Placeholder
def _extract_code_examples(self, code_research: dict) -> list:
"""Extract and categorize code examples."""
examples = []
if "code_examples" in code_research:
for example in code_research["code_examples"][:10]:
if isinstance(example, dict):
examples.append({
"repository": example.get("repository", {}).get("full_name", ""),
"file_path": example.get("path", ""),
"url": example.get("html_url", ""),
"preview": example.get("text_matches", [{}])[0].get("fragment", "")[:200]
})
return examples
def _extract_implementation_patterns(self, code_research: dict) -> dict:
"""Extract implementation patterns from code research."""
patterns = {
"common_patterns": [],
"architecture_examples": [],
"popular_repositories": []
}
if "repositories" in code_research:
for repo in code_research["repositories"][:5]:
if isinstance(repo, dict):
patterns["popular_repositories"].append({
"name": repo.get("full_name", ""),
"description": repo.get("description", ""),
"stars": repo.get("stargazers_count", 0),
"url": repo.get("html_url", "")
})
return patterns
This comprehensive MCP integration guide provides enterprise-grade patterns for orchestrating multiple MCP servers, implementing complex workflows, and creating sophisticated automation systems. The examples demonstrate real-world applications from academic research to code analysis and documentation generation.
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