mcp-server

# @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](https://img.shields.io/npm/v/@agentled/mcp-server.svg)](https://www.npmjs.com/package/@agentled/mcp-server)
[![license](https://img.shields.io/npm/l/@agentled/mcp-server.svg)](https://github.com/Agentled/mcp-server/blob/main/LICENSE)

[![Agentled Server MCP server](https://glama.ai/mcp/servers/Agentled/mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/Agentled/mcp-server)

## What is Agentled?

[Agentled](https://www.agentled.app) 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

```bash
claude mcp add --transport stdio --scope user agentled \
  -e AGENTLED_API_KEY=wsk_... \
  -- npx -y @agentled/mcp-server
```

`--scope user` registers the server in your user MCP config so it loads in **every** project (not only the repo where you ran the command). Use a distinct server name (e.g. `agentled_my_workspace`) if you add multiple workspaces. For team-shared config in git, use `--scope project` and `.mcp.json` instead ([Claude Code MCP scopes](https://code.claude.com/docs/en/mcp)).

### Claude Code plugin (one-step install)

Prefer the plugin if you want the MCP server **and** the Agentled skill installed together. In Claude Code:

```
/plugin marketplace add Agentled/mcp-server
/plugin install agentled@agentled
```

Then set your API key in the shell Claude Code runs from:

```bash
export AGENTLED_API_KEY=wsk_...
```

### Codex plugin (one-step install)

The same repository is a Codex marketplace. Install the Agentled plugin with:

```bash
codex plugin marketplace add Agentled/mcp-server
codex plugin add agentled@agentled
```

Then set `AGENTLED_API_KEY=wsk_...` in the shell that launches Codex and start a
new task. The plugin bundles the Agentled skills, starts the MCP server through
`npx -y @agentled/mcp-server`, and loads its lifecycle hooks from the standard
`hooks/hooks.json` path.

The plugin bundles the `agentled` skill (workflow-authoring guidance, namespaced `agentled:agentled`) and auto-starts the MCP server via `npx -y @agentled/mcp-server`. The same plugin directory carries both Claude Code and Codex manifests — one bundle, both hosts.

For Codex, the hook pack acts as in-session guidance around the CLI/MCP loop:
session start explains the Agentled/Codex business-loop split, prompt/tool hooks
add turn-level guidance when client needs, priorities, failures, or product gaps
appear, and stop hooks nudge implementation handoffs to include readiness,
validation, side effects, and next decision. Hooks do not store feedback, call
Agentled APIs, run automations, spend credits, or perform customer/workspace
writes. In Codex, run `/hooks` after installing or changing the plugin so the
local hook definitions are reviewed and trusted before they run.

Use Codex automations for outside-workspace FDE cadence such as Outlook/client
email follow-up, vendor replies, repo/build checks, and weekly operator reviews.
Use Agentled routines for Agentled workspace/runtime checks such as workflow
health, routine health, execution review, workspace summaries, and managed-agent
operations. Use `submit_feedback_to_agentled` or `agentled feedback submit` when
the user explicitly wants product feedback captured.

> **Pick one install path, not both.** If you previously ran `claude mcp add agentled ...` or `--setup-skills`, remove those before (or instead of) installing the plugin — otherwise you get two identical MCP server processes and the skill registered twice. Cleanup: `claude mcp remove agentled` and delete `.claude/skills/agentled/` (or `~/.claude/skills/agentled/`). `--setup-skills` now detects an installed plugin and refuses to double-register unless you pass `--force`.

To develop the plugin locally:

```bash
claude --plugin-dir ./plugins/agentled     # load from source
claude plugin validate ./plugins/agentled  # check manifest + structure
```

> `plugins/agentled/skills/` is a generated mirror of `skills/` (synced by `publish.sh`) — edit `skills/agentled/SKILL.md`, never the mirror.

### 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.

```bash
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](https://www.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:

```json
{
  "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 con