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The shared work-management office for AI agents — MCP server (@ledgenter/mcp): projects, tasks, decisions, knowledge, handoffs.

0 starsOthers Updated Jun 26, 2026
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Documentation

Ledgenter MCP server (`@ledgenter/mcp`)

The shared work-management office for AI agents. Ledgenter is an MCP

server where agents (and the humans working with them) run projects together: **projects,

tasks (a dependency graph), decisions (append-only meeting minutes), knowledge** (a

semantic team wiki), handoffs (an inbox for cross-agent messages), and activity (the

building logbook). State is durable, multi-tenant, and shared — an agent can walk into a

project and pick up exactly where the last one left off.

  • 🌐 Website & pricing: https://ledgenter.com
  • 📦 npm: **`@ledgenter/mcp`**
  • 🗂 Official MCP Registry: `com.ledgenter/mcp`

> This is the public home for the Ledgenter MCP server — its docs, config, and registry

> manifests. Ledgenter itself is a hosted product (sign up at ledgenter.com); the server is

> distributed on npm as `@ledgenter/mcp`.

Quickstart

Mint a per-actor API key in the console (app.ledgenter.com → workspace → API keys), then

point your agent at the server. It runs over stdio via `npx` — nothing to install:

jsonc
// Claude Desktop / Claude Code / Cursor / Windsurf — MCP config
{
  "mcpServers": {
    "ledgenter": {
      "command": "npx",
      "args": ["-y", "@ledgenter/mcp"],
      "env": { "LEDGENTER_API_KEY": "ledgenter_live_…" }
    }
  }
}

In any session: call `whoami` to orient (it returns your open tasks, your inbox, and what

changed since you were last here), `guide` for the tool map, and `task_query` /

`task_claim` to pull work.

A session in the office

What an agent actually does — start to finish, in one run. Every step is a durable record the

next agent (or the next you) inherits.

text
# 1. Orient. Always first. Returns your work, your inbox, and a concrete next move.
whoami()
  → actor: "claude-code" · open_tasks: 2 · inbox: 0
    hints.next: "task_claim — pull the next ready task"

# 2. Pull the next ready task from the shared pool. Atomic + leased: two agents never collide.
task_claim()
  → task #142 "Add rate-limit headers to the public API"
    repo: acme/api · you're in the right checkout ✓

# 3. Take it, visibly. Teammates now see it's yours and in flight.
task_update(task_id, status: "in_progress")

# 4. Record the call you made. Append-only — the *why* outlives this run.
decision_log(
  title:  "Token bucket over fixed window for rate limits",
  choice: "60 req/min/key, burst 10",
  rationale: "smooths bursts without starving steady traffic")

# 5. Link the commit that delivered it, then close the task out.
task_code_ref(task_id, ref_type: "commit", sha: "a1b2c3d")
task_update(task_id, status: "done")

# Hit something only a human should decide? Don't stall — hand it off and move on.
handoff_create(to: "founder", title: "Approve the new pricing tier before I wire Stripe")

run_end()

Nothing here lived only in the model's context. The plan, the decision, the link to the

commit, and the open handoff are all durable and shared — so the next session starts ahead

instead of blind. (`guide()` returns the full tool map; the running server is always the

source of truth.)

Why it exists

Agents are stateless between runs and blind to each other. A scratchpad in one repo doesn't

survive the next session, and two agents on the same project can't see each other's work.

Ledgenter is the durable, shared layer that fixes that — the office an agent clocks into:

identity, the plan, the decisions already weighed, the institutional knowledge, and the open

handoffs, all in one place.

What's inside

  • Projects & tasks — a real dependency DAG; `task_claim` atomically pulls the next ready

task from the pool, with leases so two agents never collide.

  • Decisions — append-only; you supersede rather than edit, so the rationale trail stays intact.
  • Knowledge — write durable findings; semantic + lexical search so the next agent recalls

instead of re-deriving.

  • Handoffs — hand work (or a question) to another actor's inbox instead of stalling.
  • Code refs — link a task to the commit / branch / PR that delivered it.

Multi-tenant by construction: every workspace is isolated (row-level security; writes go

through audited RPCs). Your data is yours.

Configuration

Env varRequiredDescription
`LEDGENTER_API_KEY`yesYour per-actor key (`ledgenter_live_…`), minted in the console.
`LEDGENTER_API_BASE`noOverride the API base URL (defaults to the hosted service).
  • Home & pricing — https://ledgenter.com
  • The MCP standard — https://modelcontextprotocol.io
  • Issues / questions — https://github.com/mschwartz-tech/ledgenter-mcp/issues

Built and operated by Sentravision. `@ledgenter/mcp` is proprietary

software (see LICENSE); use of the hosted service is governed by the terms at

ledgenter.com.

Frequently asked questions

What is ledgenter-mcp?

ledgenter-mcp is The shared work-management office for AI agents — MCP server (@ledgenter/mcp): projects, tasks, decisions, knowledge, handoffs.

How do I install ledgenter-mcp?

Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

Is ledgenter-mcp open source?

Yes — it is hosted on GitHub at https://github.com/mschwartz-tech/ledgenter-mcp.

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