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PopNx MCPMCP servers for people who use AI · explained in plain words

Plumb

by plumbkitversion 0.26.2i★ –i–
Registry nameio.github.plumbkit/plumb

Install

Overview

Description from the maker (original English)

LSP-backed IDE intelligence plus concurrency-safe, transactional edits for AI coding agents.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: –
  • Transport:
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

The maker has not listed install details in the registry. See the maker’s README below.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account plumbkit, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 32509 servers in this category
  • Published to the registry more recently than 64% of the other 32509 servers in this category
  • Basic checks equal to or better than 100% of the other 32509 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Plumb safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.26.2) reached the registry 21 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Fomu MCP

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago
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Remote Desktop Commander

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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday
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Fomu MCP

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Kenya government form guides: checklists, draft letters, eCitizen, Huduma, timelines. Sample data.

Runs on your computerUpdated today
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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

IDE intelligence for agents — guardrails for unattended work, coordination for fleets.

Plumb is an MCP server that gives a coding agent the intelligence layer of an IDE — LSP-backed semantics, a tree-sitter code index, and project memory — inside guardrails: atomic, lock-serialised writes with transactional rollback, scoped filesystem and git access, and a daemon that survives its own crashes. And because every agent you run shares that one daemon, plumb is also the coordination layer between them: peers see the writes others made, message each other, and hand off work instead of duplicating it. A single binary; nothing else to install.


Why Plumb

LLM agents usually work by reading whole files into the context window — token-heavy, lossy at scale, blind to symbol semantics, and unsafe to let loose on a real repo. Plumb is built on four pillars, in priority order.

1. Reliability & write-safety

Leaving an agent to edit a codebase for an hour is only viable if writes can't corrupt files and a crash can't wedge your session.

  • Atomic I/O — every write is staged in a temp file and renamed into place. No partial writes, ever. Symlink-aware, CRLF-tolerant.
  • Per-path locking — the daemon serialises concurrent writes to the same file across every session and chat window. No races.
  • Multi-file transactions — apply edits across dozens of files with guaranteed atomic rollback if any step fails.
  • Crash-resilient daemon — plumb serve is a reconnecting proxy. If the daemon crashes or hangs, it respawns one and replays the handshake; the agent never notices. In-flight writes are never silently re-run.
  • Optimistic concurrency — mtime/sha guards catch stale edits before they clobber newer changes.

See it run: docs/demos/ — two-agents-one-file.sh (a stale write is refused, nothing is lost) and daemon-respawn.sh (below — the daemon is killed mid-session; the agent's next edit still succeeds):

2. Multi-agent coordination

One daemon serves every agent you run — which makes it the natural place for agents to *see and talk to* each other, not merely avoid each other's writes. Locks stop two agents corrupting a file; coordination stops them duplicating a task, rebasing onto a function signature a peer is mid-rewrite of, or shipping a change a peer's in-flight work is about to invalidate.

  • Peer awareness (on by default) — workspace_sessions names every active session and the writes it made, as the daemon recorded them. Recorded activity, not another agent's say-so: an agent about to start a task can see that a peer is already in those files. (Read-only operations never appear, and a write that failed or was refused is kept but marked [failed — no change applied] — so "a peer is working here" and "this landed" are distinguishable at a glance.)
  • An agent-to-agent mailbox (on by default, same workspace) — leave_note / check_messages give sessions a threaded channel: hand a change to the peer already rewriting those files, or ask a peer to *measure* a behaviour instead of assuming it. Messages ride on ordinary tool results, so a working agent receives them without polling.
  • Advisory intents (opt-in: [collab] intents) — share_intent declares what an agent is working on; a peer whose write touches a claimed path gets a hint at the moment of the would-be collision. Intents are deliberately labelled as unverified claims, kept distinct from the daemon-recorded activity feed, and never block anything.
  • Durable findings (opt-in: [collab] knowledge_handoff) — share_findings turns what an agent just learned into a searchable, secret-scrubbed project memory immediately, so the knowledge outlives the session that produced it.

Coordination is advisory by design — the write-safety above never depends on agents cooperating. Reference: Cross-agent sharing in the tool docs and the [[collab] config section](docs/configuration.md#collab--cross-agent-sharing).

3. Semantic intelligence

The same primitives your editor has, exposed as structured tools:

  • LSP-backed refactors — rename_symbol, replace_symbol_body, safe_delete_symbol understand scope, types, and references.
  • Real diagnostics inline — actual gopls/pyright output is appended to every write, so the agent learns it broke the build immediately.
  • Symbol search — scoped to your code, no stdlib or dependency noise.
4. Context efficiency & safety controls
  • Read only what you need — symbols or line ranges, not 2,000-line files.
  • Scoped access you control — a per-connection path allowlist (read-only vs read-write roots) plus tiered git gating (destructive and network operations are off by default and need explicit confirmation). See SECURITY.md.
  • One-round-trip bootstrap — session_start returns workspace, branch, recent commits, diagnostics, and project memory.

See the measured, reproducible numbers behind this: docs/use-cases.md — reading one function is 2.9×–33.4× less context than the whole file (the ratio is how much of the file you didn't need), and find_references returns the real call sites where a text search is 60% noise. The page publishes the losses too: read_multiple_files costs 1.31× more payload than reading the files natively (down from 1.32×, but still a loss — see Scenario 10 for why it isn't smaller). Every figure is regenerated by scripts/measure-use-cases.py.


Get started

Plumb is a single binary — from zero to your first answer:

1. Install

# Homebrew (macOS + Linux) — recommended

brew install plumbkit/plumb/plumb

or with Go

go install github.com/plumbkit/plumb/cmd/plumb@latest

or grab a prebuilt binary: https://github.com/plumbkit/plumb/releases

macOS note: prebuilt binaries are not yet notarised — on first run you may

need xattr -d com.apple.quarantine ./plumb, or right-click → Open. Homebrew

installs avoid this.

2. Connect your agent

plumb setup claude-code      # also: claude-desktop, codex, gemini, cursor, …

plumb setup writes the MCP config for you — no hand-editing JSON.

3. Open your project and try it

Make sure the language server you need is on your $PATH (gopls for Go, pyright for Python, …), then point your agent at a real question. In Claude Code:

cd your/project

claude "Use plumb to orient in this repo (session_start), then show me everywhere is called and what would break if I changed its signature."

Plumb resolves the workspace and runs session_start for orientation, then answers with real LSP and topology data — actual call sites and blast radius — instead of guessing from file dumps. It's read-only; nothing is modified. (Any connected agent works — just paste the prompt.)

No go.mod/pyproject.toml and not a git repo? Run plumb init once to pin

the workspace root (it also seeds .plumb/context.md and project config).

Full walkthrough → docs/getting-started.md.


Language support (honest version)

Plumb negotiates LSP capabilities per language and also ships a built-in tree-sitter index for search and navigation with no language server. Support comes in tiers — we'd rather be precise than claim a big number.

TierLanguagesWhat you get
First-class (CI-tested, real-binary integration)Go (gopls), Python (pyright)Full LSP: definitions, references, rename, diagnostics, hierarchies + all write tools
ValidatedJava (jdtls), Rust (rust-analyzer), Swift (sourcekit-lsp), TypeScript/JS (typescript-language-server), Zig (zls), Kotlin (kotlin-lsp), HTML (vscode-html-language-server)Full LSP; just put the server on $PATH and it activates automatically (exclude any language with [lsp. ] enabled = false). HTML carries one caveat: that server has no filesystem access, so it answers only from documents already opened
Search & navigation (tree-sitter, no LSP needed)31+ incl. JS/TS/TSX, Ruby, C, C#, Elixir, Scala, PHP, JSON, CSS, SCSS, XML, Lua, C++, Objective-C, Dart, Bash, SQL, HCL, Dockerfile, TOML, YAML, MarkdownRanked symbol search, outlines, graph exploration via the Topology index

Real-binary validation has been exercised on macOS and Linux — as of 2026-08-21, all nine adapters pass their integration tests against real server binaries on both. Details, including three toolchain traps that look like adapter bugs, are in docs/adding-an-lsp.md. Windows is tracked but not yet supported — the daemon's Unix-socket architecture needs a port.


How it works

plumb serve is a thin, reconnecting stdio proxy. The real work happens in one shared background daemon, so language servers stay warm across chats.

flowchart TD

A1["Claude"] --> S1["plumb serve *"] A2["Codex"] --> S2["plumb serve *"] A3["Gemini"] --> S3["plumb serve *"] S1 --> K["plumb.sock"] S2 --> K S3 --> K K --> D["plumb daemon "] D --> SDB[("stats.db * global — all projects")] D --> G["gopls → /projects/foo"] D --> P["pyright → /projects/bar"] G --> F1[("/projects/foo/.plumb/ * topology.db · memory.db")] P --> F2[("/projects/bar/.plumb/ * topology.db · memory.db")]

* plumb serve is a reconnecting proxy — if the daemon crashes or hangs it respawns one and replays the handshake, so your session survives without the agent noticing.

** one shared process, reused across every conversation.

*** SQLite. One global stats.db (tool stats + episodic summaries); two per-project indexes under each workspace's .plumb/ — topology.db (the code graph) and memory.db (memory search). Schema details → docs/architecture.md.

Servers stay warm across chats, per-path locks are shared across every connection, and symbol indexes update live after each write. Full architecture → docs/architecture.md.


Monitoring (TUI)

Run plumb with no arguments for a live dashboard — see what your agent is doing in real time: every tool call as it happens, daemon health, per-tool stats, and streaming logs you can follow and filter. The fastest way to catch a runaway loop or confirm an edit landed.


Core capabilities

Plumb exposes 61 tools. The ones you'll use constantly:

session_start · workspace_symbols · get_definition · find_references · rename_symbol · edit_file · transaction_apply · diagnostics

The rest cover filesystem reads/writes, LSP hierarchies, tiered git, an optional local Topology index (ranked search + blast-radius/route analysis, no language server needed), durable per-project memory, and cross-agent coordination (peer sessions, an age

Only the beginning is shown; read the rest in the repo

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