@agentled/mcp-server

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

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.

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Use the local built entrypoint when you want to test unpublished changes against a local app.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.

Capability	Credits	Without Agentled
LinkedIn company enrichment	50	LinkedIn API ($99/mo+)
Email finding & verification	5	Hunter.io ($49/mo)
AI analysis (Claude/GPT/Gemini)	10-30	Multiple API keys + billing
Web scraping	3-10	Apify account ($49/mo+)
Image generation	30	DALL-E/Midjourney subscription
Video generation (8s scene)	300	RunwayML ($15/mo+)
Text-to-speech	60	ElevenLabs ($22/mo+)
Knowledge Graph storage	1-2	Custom infrastructure
CRM sync (Affinity, HubSpot)	5-10	CRM API + middleware

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:

Preset	What it does
research-and-summarize	Specialists gather information, one synthesizes a summary
analyze-and-recommend	Multiple analysts evaluate options, produce a ranked recommendation
generate-then-review	A generator drafts content, reviewers critique and refine
compare-options	Specialists argue for competing options, coordinator arbitrates
investigate-in-parallel	Independent specialists explore different angles simultaneously
review-and-improve	Reviewers find issues, an editor applies improvements

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

Tool	Description
list_workflows	List all workflows in the workspace
get_workflow	Get full workflow definition by ID
create_workflow	Create a new workflow from pipeline JSON
update_workflow	Update an existing workflow
add_step	Add a step with automatic positioning and next-pointer rewiring
update_step	Deep-merge updates into a single step by ID
remove_step	Remove a step with automatic next-pointer rewiring
delete_workflow	Permanently delete a workflow
validate_workflow	Validate pipeline structure, returns errors per step
publish_workflow	Change workflow status (draft, live, paused, archived)
export_workflow	Export a workflow as portable JSON
import_workflow	Import a workflow from exported JSON

Drafts & Snapshots

Tool	Description
get_draft	Get the current draft version of a workflow
promote_draft	Promote a draft to the live version
discard_draft	Discard the current draft
create_snapshot	Create a manual config snapshot
delete_snapshot	Delete a specific config snapshot
list_snapshots	List version snapshots for a workflow
restore_snapshot	Restore a workflow to a previous snapshot

Executions

Tool	Description
start_workflow	Start a workflow execution with input. Pass useMocks: false to force a real (credit-consuming) run that ignores per-step mock data; defaults to honoring the workflow's configured mocks.
list_executions	List executions for a workflow (paginated via nextToken)
get_execution	Get execution details with step results
list_timelines	List step execution records (timelines) for an execution (paginated via nextToken)
get_timeline	Get a single timeline by ID with full step output
stop_execution	Stop a running execution
retry_execution	Retry a failed step — auto-detects the most recent failure if no timeline ID provided

Apps & Testing

Tool	Description
list_apps	List available apps and integrations
get_app_actions	Get action schemas for an app
test_app_action	Test an app action without creating a workflow
test_ai_action	Test an AI prompt without creating a workflow
test_code_action	Test JavaScript code in the same sandboxed VM as production
get_step_schema	Get allowed PipelineStep fields grouped by category

Knowledge & Data

Tool	Description
get_workspace	Get workspace info and settings
get_workspace_company_profile	Get the editable workspace company profile and offerings
update_workspace_company_profile	Update top-level company profile fields like name, URLs, logo, industry, size, and additional information
upsert_workspace_company_offerings	Create new offerings or update existing offerings in the workspace company profile
list_knowledge_lists	List knowledge lists in the workspace
get_knowledge_rows	Get rows from a knowledge list (paginated, max 50)
get_knowledge_rows_by_ids	Fetch specific rows by ID (max 200) — use after query_kg_edges
get_knowledge_text	Get text content from a knowledge entry
create_knowledge_list	Create a new knowledge list with a typed schema (idempotent on key collision)
update_knowledge_list_schema	Add or remove fields on an existing list schema
delete_knowledge_list	Permanently delete a list and all its rows
upsert_knowledge_rows	Insert or update rows in a list (max 500/call, per-row error reporting)
delete_knowledge_rows	Delete rows by ID
upsert_knowledge_text	Create or update a text knowledge entry
delete_knowledge_text	Delete a text knowledge entry by key
query_kg_edges	Query knowledge graph edges
get_scoring_history	Get scoring history for an entity

Branding (Whitelabel)

Tool	Description
get_branding	Get the workspace's whitelabel branding config (displayName, logo, colors, favicon, badge)
update_branding	Update branding — set displayName, logoUrl, tagline, primaryColor, primaryColorDark, faviconUrl, hideBadge

Conversational Agent

Tool	Description
chat	Send a message to the AgentLed AI agent. Build workflows through natural language — no JSON required. Supports multi-turn conversations via session_id.

Intent Router

Tool	Description
do	Natural language intent router — describe what you want and it auto-selects and executes the right tool

Coming from n8n?

Import existing n8n workflows and make them AI-native:

Tool	Description
preview_n8n_import	Preview an n8n workflow import (dry run)
import_n8n_workflow	Import an n8n workflow into Agentled

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