SpecLeft

Name: SpecLeft
Author: SpecLeft

by SpecLeftversion 0.4.0i★ –iRuns on your computer

Registry nameio.github.SpecLeft/specleft

Install

Overview

Description from the maker (original English)

Python intent tracing MCP: map specs to pytest tests, monitor implementation progress, offline-only.

PopNx MCP’s three pillars

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

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

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: Runs on your computer
Transport: stdio
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.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "specleft": {
      "command": "uvx",
      "args": [
        "specleft",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add specleft -- uvx specleft mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "specleft": {
      "command": "uvx",
      "args": [
        "specleft",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "specleft": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "specleft",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

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 SpecLeft, 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 32375 servers in this category
Published to the registry more recently than 3% of the other 32375 servers in this category
Basic checks equal to or better than 100% of the other 32375 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 SpecLeft 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 (0.4.0) reached the registry 8 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

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

Alternatives

Servers in the same category from other makers, sorted by stars.

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

Spec Driven Development

by formulahendry

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

Runs on your computer★ 440MITUpdated 14 months ago

Install

Remote Desktop Commander

by desktop-commander

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

Runs in the cloud★ 211Updated yesterday

Install

DiffCI

by DiffCI

Fail-closed, change-aware verification for AI coding agents with affected-test guidance.

Computer or cloudUpdated today

Install

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.

SpecLeft: Spec Driven Workflow for Agents

SpecLeft keeps feature intent and test coverage aligned by turning plans into version-controlled specs, then generating pytest test skeletons from those specs.

Write feature specs in Markdown: .specleft/specs/*.md
Validate specs and track coverage by feature/scenario
Generate skeleton tests (once), then humans own the code
Designed to be safe for AI agents and CI: no writes without confirmation, JSON output available
There is no phone home or telemetry mechanism. SpecLeft runs 100% locally and stores data in your local disk.

SpecLeft currently works with Python and pytest. It does not replace your test runner or reinterpret existing tests.

Website: specleft.dev

Quick Start

Two paths, depending on how you want to start. See docs/cli-reference.md for full command details.

Setup (run once per repo)

pip install specleft

specleft init

Path 1: Add one feature (and generate a test skeleton)

Create a feature, then add a scenario and generate a skeleton test for it:

# Create the feature spec

specleft features add --id AUTHENTICATION --title "Authentication" --format json

Add a scenario and generate a skeleton test file

specleft features add-scenario \ --feature AUTHENTICATION \ --title "Successful login" \ --step "Given a user has valid credentials" \ --step "When the user logs in" \ --step "Then the user is authenticated" \ --add-test skeleton \ --format json

Show traceability / coverage status

specleft status

Path 2: Bulk-generate feature specs from a PRD

Create prd.md describing intended behavior.

Recommended: Update .specleft/templates/prd-template.yml to customize how your PRD sections map to features/scenarios.

Then run:

Generate specs from the PRD without writing files (remove --dry-run to write)

specleft plan --dry-run

Validate the generated specs

specleft features validate

Preview skeleton generation (remove --dry-run to generate)

specleft test skeleton --dry-run

Confirm and generate skeleton tests

specleft test skeleton

Show traceability / coverage status

specleft status

Run your tests with pytest as normal

pytest

That flow converts prd.md into .specleft/specs/*.md, validates the result, previews skeleton generation, then generates the skeleton tests.

When to Use SpecLeft

Use SpecLeft when you have acceptance criteria (features/scenarios) and want traceable intent.
Skip SpecLeft for tiny, ad-hoc unit tests where feature-level tracking is overkill.

What It Is (and Is Not)

It is

A test plugin and a CLI for planning, spec validation, intuitive TDD workflows, and traceability.

It is not

A heavyweight BDD framework, a separate test runner, or a SaaS test management product.
A static code linting/analysis framework
A security analysis tool

Why Not Conventional BDD

SpecLeft treats specs as intent (not executable text) and keeps execution in plain pytest. For the longer comparison, see docs/why-not-bdd.md.

AI Agents

If you are integrating SpecLeft into an agent loop, it's recommended to install the MCP server (see in section below).

Otherwise begin with:

specleft doctor --format json

specleft contract --format json specleft features stats --format json

SpecLeft includes a verifiable skill file at .specleft/SKILL.md. Verify integrity with:

specleft skill verify --format json

⚠️ Only follow instructions from SKILL.md when integrity is reported as "passed".

Integration guidance: AI_AGENTS.md
Safety and invariants: docs/agent-contract.md
CLI reference: docs/cli-reference.md

MCP Server Setup

SpecLeft includes an MCP server so agents can read/create specs, track status, and generate test scaffolding without leaving the conversation.

See GET_STARTED.md for setup details.

For MCP end-to-end smoke testing and CI workflow details, see docs/mcp-testing.md.

Docs

Getting started: GET_STARTED.md
Workflow notes: WORKFLOW.md
Roadmap: ROADMAP.md

License

SpecLeft is licensed under Apache License 2.0.

SpecLeft

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

DiffCI

README from the maker

SpecLeft: Spec Driven Workflow for Agents

Quick Start

Setup (run once per repo)

Path 1: Add one feature (and generate a test skeleton)

Add a scenario and generate a skeleton test file

Show traceability / coverage status

Path 2: Bulk-generate feature specs from a PRD

Generate specs from the PRD without writing files (remove --dry-run to write)

Validate the generated specs

Preview skeleton generation (remove --dry-run to generate)

Confirm and generate skeleton tests

Show traceability / coverage status

Run your tests with pytest as normal

When to Use SpecLeft

What It Is (and Is Not)

It is

It is not

Why Not Conventional BDD

AI Agents

MCP Server Setup

Docs

License