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.

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[![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_...
```

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 also carries the Codex manifest (`.codex-plugin/`) — one bundle, both hosts.

> **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 continue to work — they open in advanced mode in the builder without errors.


## Analytics vs ROI semantics

When describing workflow outcomes, keep these terms separate:

- `pipeline.analyticsConfig` = **business metrics** (execution outcome stats shown in Business Metrics cards/charts).
- `pipeline.metadata.roi` = **ROI assumptions/rollups** (time saved and cost-value estimates).

If you update one without the other, name exactly what changed (e.g. "business metrics configured" vs "ROI assumptions configured").

## CLI parity guard

The repository includes an automated parity guard so MCP tool additions do not silently drift from the CLI surface.

- Test: `__tests__/cli/cli-mcp-parity.test.ts`
- Docs: `docs/CLI_MCP_PARITY.md`

Run it with:

```bash
yarn test:node -- cli-mcp-parity.test.ts
```

## 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, che