@agentled/mcp-server

The automation engine built for AI agents. Intelligent AI workflow orchestration with long-term memory, 100+ integrations, and unified credits.

👁 npm version
👁 license

👁 Agentled Server MCP server

What is Agentled?

Agentled is the automation engine built for AI agents. It gives Claude, Codex, Cursor, Windsurf, and any MCP-compatible client direct access to intelligent workflow orchestration, long-term memory, and 100+ integrations.

Three things make it different:

🧠 Long-Term Memory — A built-in Knowledge Graph stores insights across workflow executions. Your agents get smarter over time — they remember past research, lead scores, content performance, and business context.

⚡ Unified Credits — One API key, one credit system, 100+ services. No need to sign up for LinkedIn, email, scraping, AI models, or video generation separately. Connect once, use everything.

🎯 Intelligent Orchestration — AI reasons at every step. Workflows aren't just "if this then that" — they understand context, make decisions, and adapt to results.

Related MCP server: MCP- N8N

See it in action

$ agentled create "Outbound to fintech CTOs in Europe"

Loading workspace context from Knowledge Graph...
✦ ICP loaded ✦ 3 prior campaigns ✦ 847 contacts in KG

Creating campaign with 3 workflows...

━━ Workflow 1: Prospect Research linkedin · hunter · clearbit
 ✓ LinkedIn: CTO + fintech + EU → 189 profiles
 ✓ Enriched via Hunter + Clearbit → 156 matched
 ✓ ICP scoring → 43 high-intent leads

━━ Workflow 2: Signal Detection web-scraper · crunchbase
 ✓ Job postings → 12 hiring devops
 ✓ Crunchbase → 8 recently funded
 ✓ Cross-match: hiring + funded → 5 hot leads

━━ Workflow 3: Outreach email · linkedin · kg
 ✓ Personalized emails from context
 ✓ LinkedIn requests with custom notes
 ✓ 43 leads saved to Knowledge Graph

Campaign saved. Scheduled: every 48h
Credits used: 720
→ https://www.agentled.app/your-team/fintech-cto-outbound

One prompt. Three workflows. LinkedIn enrichment, email finding, AI scoring, multi-channel outreach — all orchestrated, all stored in the Knowledge Graph for the next run.

Quick Start

claude mcp add agentled \
 -e AGENTLED_API_KEY=wsk_... \
 -- npx -y @agentled/mcp-server

Local development

Use the local built entrypoint when you want to test unpublished changes against a local app. npx -y @agentled/mcp-server always uses the latest published npm package.

cd agentled-mcp-server
npm run build

claude mcp add --transport stdio agentled_local \
 --env AGENTLED_API_KEY=wsk_... \
 --env AGENTLED_URL=http://localhost:8080 \
 -- node /absolute/path/to/agentsled-front/agentled-mcp-server/dist/index.js

Getting your API key

1. Sign up at agentled.app

2. Open Workspace Settings > Developer

3. Generate a new API key (starts with wsk_)

Why Agentled MCP?

One API Key. One Credit System. 100+ Services.

No need to sign up for LinkedIn APIs, email services, web scrapers, video generators, or AI models separately. Agentled handles all integrations through a single credit system.

Workflows That Learn

Other automation tools start from zero every run. Agentled's Knowledge Graph remembers across executions — what worked, what didn't, what humans corrected. Scoring workflows can use compact row-level scoring_profile summaries and bounded scoring-memory retrieval so every run compounds on the last without dumping raw history into prompts.

Run 1: Investor scoring → 62% accuracy (cold start)
Run 5: → 78% (learning from IC feedback)
Run 12: → 89% (compound learning from outcomes, zero manual tuning)

Intelligent Orchestration

Unlike trigger-action tools, Agentled workflows have AI reasoning at every step. Multi-model support (Claude, GPT-4, Gemini, Mistral, DeepSeek, Moonshot), adaptive execution, and human-in-the-loop approval gates when needed.

Agent Teams

Agent Teams let you run multiple AI specialists in a single workflow step. Pick a preset and describe what you need — the team handles coordination, delegation, and synthesis.

"Add an Agent Team step that researches the company and produces an investment memo"

Six built-in presets cover the most common patterns:

When creating Agent Team steps via MCP, include preset metadata so the step opens correctly in the builder:

{
 "id": "analyze",
 "type": "agentOrchestrator",
 "name": "Agent Team",
 "orchestratorConfig": {
 "pattern": "supervisor",
 "workers": [
 { "id": "researcher", "name": "Researcher", "systemPrompt": "Research {{input.company_url}} — team, funding, market position" },
 { "id": "analyst", "name": "Analyst", "systemPrompt": "Analyse the research. Identify risks and growth signals." }
 ]
 },
 "metadata": {
 "agentTeamPreset": "research-and-summarize",
 "agentTeamMode": "simple",
 "agentTeamUxVersion": 1
 },
 "next": { "stepId": "milestone" }
}

Existing steps created with raw orchestratorConfig and no metadata continue to work — they open in advanced mode in the builder without errors.

What Can You Build?

Lead Enrichment & Sales Automation

"Find fintech CTOs in Europe, enrich via LinkedIn + Hunter, score by ICP fit,
draft personalized outreach, save everything to the Knowledge Graph"

Content & Media Production

"Scrape trending topics in our niche, generate 5 LinkedIn posts with AI,
create thumbnail images, schedule publishing for the week"

Company Research & Intelligence

"Research this company from its URL — team, funding, market position, competitors.
Generate an investment memo. Store in KG for future reference."

VC Investor Matching (real case study)

"Match this startup against our 2,000+ investor database. Score by sector focus,
stage preference, check size, and portfolio synergy. Compare with last round's outcomes."

3,000+ profiles processed. IC-ready reports. Prediction vs outcome learning — accuracy went from 62% to 89% over 12 runs with zero manual tuning.

Built-in Capabilities

Media Production: Video generation, image generation, text-to-speech, auto-captions, media assembly

AI Intelligence: Multi-model AI (Claude, GPT-4, Gemini, Mistral, DeepSeek, Moonshot, xAI), Knowledge Graph, feedback loops, scoring & analytics

Data & Integration: LinkedIn (search, enrich, post), email (send, personalize), web scraping, social publishing, CRM sync, document analysis, OCR

Available Tools

Workflows

Drafts & Snapshots

Executions

Apps & Testing

Knowledge & Data

Branding (Whitelabel)

Conversational Agent

Intent Router

Coming from n8n?

Import existing n8n workflows and make them AI-native:

Looking Up Entity-Scoped Data

When you need all records related to a specific entity, use the two-tool chain instead of paginating get_knowledge_rows:

Example 1 — all deals scored by an investor:

1. query_kg_edges({ entityName: "Investor Name", relationshipType: "SCORED" })
 → returns edges with targetNodeIds

2. get_knowledge_rows_by_ids({ rowIds: <targetNodeIds from step 1> })
 → returns full row data for each matched deal

Example 2 — all leads sourced from a campaign:

1. query_kg_edges({ entityName: "Campaign Name", relationshipType: "SOURCED" })
 → returns edges with targetNodeIds

2. get_knowledge_rows_by_ids({ rowIds: <targetNodeIds from step 1> })
 → returns full contact/lead rows

Why this matters: get_knowledge_rows is limited to 50 rows per call. At 3k rows that means 60 round trips; at 10k it means 200. The KG-edge path is O(edges for that entity) — independent of total list size — so it stays fast regardless of how large the list grows.

Node ID convention: source_node_id and target_node_id values from query_kg_edges are knowledge row IDs. Rows outside the authenticated workspace are silently excluded.

For Agencies: White-Label Ready

Build workflows once, deploy to multiple clients under your own brand. Configure branding directly from the MCP server:

"Set my workspace branding: displayName 'Acme AI', primaryColor '#6366f1', tagline 'Powered by Acme'"

Use get_branding and update_branding to manage displayName, logo, colors, favicon, tagline, and badge visibility. Client portal appearance updates instantly.

Persistent Memory — Examples

Memories let workflows learn across executions. Store what worked, recall it next time.

Store a fact after enrichment

"Store a memory: key 'icp_criteria', value { industry: 'fintech', minEmployees: 50, region: 'EU' },
category 'preference', scope 'workspace'"

Recall before scoring

"Recall memory 'icp_criteria' at workspace scope — use it to score this batch of leads"

Search for past outcomes

"Search memories for 'conversion rate' in the 'outcome' category"

Track a running metric

"Store memory: key 'total_leads_processed', value 43, merge 'increment', scope 'workspace'"

Each subsequent call with merge: 'increment' adds to the existing value — no read-modify-write needed.

Proactive Agents — Examples

Proactive agents are background monitors that autonomously trigger workflows when conditions are met.

Create an agent that watches for new leads

"Create a proactive agent named 'New Lead Watcher' that checks the 'incoming-leads' knowledge list
every 5 minutes. When new rows appear, start the 'lead-enrichment' workflow with the new rows as input.
Limit to 10 actions per day."

Config structure:

{
 "monitorInterval": "5m",
 "evaluation": { "mode": "rules" },
 "monitors": [{
 "type": "kg_list",
 "listKey": "incoming-leads",
 "condition": "new_rows"
 }],
 "actions": [{
 "type": "start_workflow",
 "workflowId": "wf_abc123",
 "inputMapping": { "leads": "{{monitor.newRows}}" }
 }],
 "maxActionsPerDay": 10,
 "cooldownMs": 300000
}

Create an AI-evaluated agent

"Create a proactive agent that checks execution history every hour.
Use AI evaluation to decide if the failure rate is abnormal, then notify me via email."

{
 "monitorInterval": "1h",
 "evaluation": { "mode": "ai", "modelTier": "mini", "maxCreditsPerDay": 50 },
 "monitors": [{
 "type": "execution_history",
 "condition": "consecutive_failures",
 "threshold": 3
 }],
 "actions": [{
 "type": "notify",
 "channel": "email",
 "message": "{{monitor.summary}}"
 }],
 "maxActionsPerDay": 5
}

Pause and resume

"Pause proactive agent pa_xyz789"
"Resume proactive agent pa_xyz789"

Works With

• Claude Code (Anthropic)

• Codex (OpenAI)

• Cursor

• Windsurf

• Any MCP-compatible client

Links

• Agentled Platform

• npm Package

• GitHub

• Report Issues

Building from Source

git clone https://github.com/Agentled/mcp-server.git
cd mcp-server
npm install
npm run build

License

MIT

Maintenance

Resources

• NPM Package
• GitHub Repository
Need Help?Related Servers

Tools

• chatA
• create_snapshotA
• create_workflowC
• delete_snapshotA
• delete_workflowA
• discard_draftA
• export_workflowA
• get_app_actionsA