mcp-conductor

name

mcp-conductor

description

Orchestrate multiple MCP servers together for complex multi-step tasks. Teaches agents to chain Exa search → Bright Data scraping → GitHub API → file operations in intelligent workflows. Use when a task requires data from multiple sources, when combining MCP tools for research, when building multi-step automations across services, or when the user says orchestrate, combine MCPs, multi-source, research pipeline, or chain tools together.

MCP Conductor — Multi-Server Orchestration

Overview

Teach your AI agent to orchestrate multiple MCP servers as a unified intelligence pipeline. Single MCP calls give shallow results. Chaining them gives 10x depth.

When to Use

• Task requires data from 2+ sources (search + scrape + analyze)
• User asks to "research" something (implies multi-source)
• Need to validate information across sources
• Building a comparison or competitive analysis
• Monitoring for changes over time
• Any workflow where one tool's output feeds another tool's input

Orchestration Patterns

Pattern 1: Research Pipeline

TASK: "Research how company X handles authentication"

Step 1: DISCOVER (Exa MCP)
  → web_search_exa: "company X authentication architecture blog"
  → Returns: 5-10 relevant URLs

Step 2: EXTRACT (Bright Data MCP)  
  → scrape_as_markdown: each URL from Step 1
  → Returns: full page content as markdown

Step 3: ANALYZE (Agent reasoning)
  → Read all scraped content
  → Extract: patterns, technologies, trade-offs
  → Cross-reference findings

Step 4: ENRICH (GitHub MCP / gh CLI)
  → Search for open-source implementations mentioned
  → Read relevant source code for concrete examples

Step 5: SYNTHESIZE (Agent output)
  → Combine all sources into structured analysis
  → Cite sources, compare approaches, recommend

Pattern 2: Competitive Intelligence

TASK: "Compare all tools that solve X"

Step 1: SEARCH (Exa MCP — semantic search)
  → "tools libraries frameworks for X comparison 2026"
  
Step 2: VALIDATE (GitHub — verify repos exist and are active)
  → gh repo view each candidate — check stars, last push, activity
  → Filter: only repos pushed in last 90 days with 100+ stars

Step 3: DEEP-READ (Bright Data — get full documentation)
  → Scrape README, docs pages, getting-started guides
  
Step 4: ANALYZE (Agent — structured comparison)
  → Feature matrix, trade-offs, community health, performance claims
  → Output: ranked recommendation with evidence

Pattern 3: Content Aggregation & Synthesis

TASK: "Create a comprehensive guide on topic Y"

Step 1: MULTI-SOURCE SEARCH
  → Exa: semantic search for authoritative sources
  → Bright Data search_engine: Google for official docs
  → GitHub: find repos, examples, real implementations

Step 2: PARALLEL EXTRACTION
  → Bright Data scrape_as_markdown: top 10 URLs (parallel batch)
  → gh API: read READMEs of top repos

Step 3: KNOWLEDGE GRAPH
  → Identify: key concepts, relationships, prerequisites
  → Map: which source covers which subtopic best
  → Find: gaps no single source covers

Step 4: GENERATION
  → Synthesize: original content citing all sources
  → Validate: cross-check claims against multiple sources
  → Format: structured guide with clear sections

Pattern 4: Monitor & React

TASK: "Watch for changes in domain Z and alert me"

Step 1: BASELINE (one-time)
  → Exa + Bright Data: current state of domain Z
  → Store: key facts, current leaders, latest news

Step 2: PERIODIC CHECK (via loop skill)
  → Exa: search for NEW content since last check
  → Compare: against stored baseline
  → Detect: changes, new entries, removals

Step 3: REACT (when change detected)
  → Bright Data: scrape the changed resource for details
  → Analyze: significance of change
  → Notify: user with summary + links

MCP Server Capabilities Reference

Server	Best For	Key Tools
Exa	Semantic/neural web search	web_search_exa (conceptual matching, not just keywords)
Bright Data	Any URL scraping, SERP, structured data	scrape_as_markdown, search_engine, web_data_*
GitHub (gh CLI)	Repos, code, issues, PRs	gh search repos, gh api, gh repo view
Slack	Team communication	slack_list_channels, slack_post_message
Linear	Project management	Issues, projects, cycles
Stripe	Payments	Customers, invoices, subscriptions

Orchestration Rules

1. Start broad, narrow deep. First search finds candidates. Then scrape only the best.
2. Validate before extracting. Don't scrape 50 URLs — verify relevance first (check title, snippet).
3. Parallelize when possible. Multiple scrapes of independent URLs can happen simultaneously.
4. Cache aggressively. If you scraped a URL this session, don't scrape it again.
5. Cite everything. Every claim must trace back to a source URL.
6. Fail gracefully. If one MCP fails, continue with others. Don't abort the pipeline.
7. Respect rate limits. Space Bright Data calls by 1-2 seconds. Batch Exa calls.

Anti-Patterns

• Using ONLY one MCP when a chain would give 10x better results
• Scraping everything without filtering first (wastes time and tokens)
• Not cross-referencing (single source = unverified)
• Sequential when parallel is possible
• Ignoring MCP errors instead of retrying/falling back

Quick Reference: Common Chains

Goal	Chain
Research a topic	Exa search → Bright Data scrape top 5 → synthesize
Find best tool for X	Exa search → GitHub verify → Bright Data docs → compare
Monitor a space	Exa baseline → loop + Exa delta → Bright Data details
Competitive analysis	Exa search → GitHub stats → Bright Data pricing pages → matrix
Build knowledge base	Exa broad → Bright Data batch scrape → extract + organize