# Agentled Mcp Server

> The automation engine built for AI agents. Workflows, memory, and 100+ integrations — one API key.

- **Type:** MCP server
- **Install:** `agentstack add mcp-agentled-mcp-server`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Agentled](https://agentstack.voostack.com/s/agentled)
- **Installs:** 0
- **Category:** [Integrations](https://agentstack.voostack.com/c/integrations)
- **Latest version:** 0.15.13
- **License:** MIT
- **Upstream author:** [Agentled](https://github.com/Agentled)
- **Source:** https://github.com/Agentled/mcp-server

## Install

```sh
agentstack add mcp-agentled-mcp-server
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# @agentled/mcp-server

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

[](https://www.npmjs.com/package/@agentled/mcp-server)
[](https://github.com/Agentled/mcp-server/blob/main/LICENSE)

[](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/`) and Codex lifecycle hooks — 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 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, 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 Skills And Integrations

**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

### Use-case operating guides

When work starts from an existing workflow ID, call `get_workflow` first and
inspect `useCaseContext`. Then call `get_use_case` with the returned key or
workflow graph ID, and read any `operatingGuides` before answering
workflow-specific questions, running backfills, or editing live config. Missing
guide warnings mean the operating README is not attached yet and the context is
incomplete.

When work starts from a user's business goal rather than an existing workflow,
use [How to Build Use Cases From User Goals](../docs/HOW_TO_BUILD_USE_CASES_FROM_USER_GOALS.md).
It covers the external-agent lifecycle from bounded chat result to KG text/list
state, `WorkspaceUseCase`, workflows, approval queues, and home summary updates.

### Workflows

| Tool | Description |
|------|-------------|
| `list_workflows` | List all workflows in the workspace |
| `get_workflow` | Get full workflow definition by ID, including `useCaseContext` when linked |
| `get_workflow_credits` | Get ledger-derived, period-labelled workflow credit usage; opt in to cost drivers with `includeCostDrivers` |
| `create_workflow` | Create a new workflow from pipeline JSON |
| `update_workflow` | Update an existing workflow (top-level scalars; for context/metadata prefer `update_workflow_context`) |
| `update_workflow_context` | Workflow-level analog of `update_step` — three explicit verbs (`updates` / `replace` / `unset`) on `context.*` and `metadata.*` paths, returns `diff` + `warnings` |
| `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 |

### Public Form Links

Public form links are the external intake surface for workflows with
`context.executionInputConfig` fields. Use them when people outside the
workspace need to submit a workflow form without signing in: inbound lead
forms, pitch deck submissions, referral forms, support intake, assessment
questionnaires, or any workflow whose first step is a manual/input trigger.

Do **not** use a public form link for internal child workflows. Child workflows
should use `context.executionInputConfig.internal: true` and be called from
another workflow with `agentled.call-workflow`.

| Tool | Description |
|------|-------------|
| `list_public_form_links` | List existing public form links for a workflow |
| `create_public_form_link` | Create and enable a public form link |
| `update_public_form_link` | Enable/disable a link or update limits, expiry, auto-share, and thank-you copy. To revoke external access, set `enabled: false`. |

> **Deletion is intentionally not exposed via the external API or MCP.** To
> revoke a public form link, call `update_public_form_link` with
> `enabled: false`. Permanent deletion requires an authenticated workspace
> member acting through the UI — destructive ops on the form-link surface are
> not granted to the public API key.

Typical agent flow:

```text
1. get_workflow({ workflowId })
2. Confirm context.executionInputConfig exists and is not internal.
3. list_public_form_links({ workflowId })
4. If none exists, create_public_form_link({ workflowId, enabled: true })
5. Return the publicUrl to the user.
```

The public URL is `/en/forms/{formLinkId}`. On submit, Agentled validates the
form link, starts the workflow with the submitted `input`, records a
`PublicFormSubmission`, and increments `submissionCount`. Optional settings:

- `enabled`: disable without deleting the link.
- `expiresAt`: ISO datetime expiry.
- `submissionLimit`: maximum accepted submissions.
- `autoShare`: when true, the public form status page can show generated
  results after completion. Use this only when the workflow output is safe for
  the submitter to see.
- `shareExpiresInDays`: expiry for auto-shared result links.
- `successMessage`: custom thank-you message after submission.

### Internal-only Workflows

Mark a workflow as a child / sub-workflow that is only run via `agentled.call-workflow` from an orchestrator by setting `context.executionInput

…

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Agentled](https://github.com/Agentled)
- **Source:** [Agentled/mcp-server](https://github.com/Agentled/mcp-server)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.15.13 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.15.13** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-agentled-mcp-server
- Seller: https://agentstack.voostack.com/s/agentled
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.