Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
MCP State Sidecar: durable state persistence for multi-agent AI workflows
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
Which app do you use?
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": {
"state-sidecar": {
"command": "uvx",
"args": [
"mcp-state-sidecar"
]
}
}
}Run this in your terminal
claude mcp add state-sidecar -- uvx mcp-state-sidecarPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"state-sidecar": {
"command": "uvx",
"args": [
"mcp-state-sidecar"
]
}
}
}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.
Paste into .vscode/mcp.json in your project
{
"servers": {
"state-sidecar": {
"type": "stdio",
"command": "uvx",
"args": [
"mcp-state-sidecar"
]
}
}
}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.
Runs on your computer; the tool list appears in your AI app after you install it.
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 7, 2026
The registry name was verified with the GitHub account askadvaith, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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.
The registry does not list a required key, though some servers ask you to sign in on first use.
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).
The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 5 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, Aiterm MCP
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
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Persistent terminals and one launcher for Claude, Codex, Grok, Cursor, and Antigravity harnesses.
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.
An MCP-native state sidecar that externalises workflow state for distributed agent deployments.
Quite a simple idea really; instead of storing state inside agents (which breaks when processes crash, scale horizontally, or span multiple frameworks), agents write to and read from this sidecar over the Model Context Protocol (MCP). The sidecar is itself an MCP server; agents call its tools exactly the same way they call any other tool!
The server itself is built with distributed environments in mind, and natively handles concurrency, crash resilience and atomic claims in addition to being a common interface for state management between agents.
Install the package via pip or your favorite Python package manager:
pip install mcp-state-sidecar
If you want to use the Redis backend:
pip install mcp-state-sidecar[redis]
To build and install the package from source:
git clone https://github.com/askadvaith/MCP-State-Sidecar.git
cd MCP-State-Sidecar
2. Install build dependencies:
pip install --upgrade build
python -m build
pip install dist/mcp_state_sidecar-*.whl
Or install the package in editable mode for active development:
pip install -e .
In a multi-agent distributed environment, you would typically run the state sidecar as an HTTP SSE service so multiple remote agents and clients can connect to it concurrently.
Start the SSE server to listen on a network port:
mcp-state-sidecar-http
By default, the server binds to 0.0.0.0 and listens on port 8000. The MCP endpoint is available at http://localhost:8000/mcp.
Launch the server via standard input/output:
mcp-state-sidecar
The server is configured entirely using environment variables:
| Environment Variable | Default | Description |
|---|---|---|
STATE_BACKEND | sqlite | Storage backend: sqlite or redis |
DB_PATH | state_sidecar.db | Path to the SQLite database file |
REDIS_URL | redis://localhost:6379 | Redis connection URL |
SIDECAR_HOST | 0.0.0.0 | IP host to bind the HTTP SSE server |
SIDECAR_PORT | 8000 | Port for the HTTP SSE server |
state_set(key, value, ttl_seconds?, agent_id?): Upsert a JSON-serialisable value with optional TTL.state_get(key): Retrieve a value (returns found=False if missing or expired).state_delete(key): Delete a key.state_list(prefix?): List all live keys, optionally filtered by prefix.workflow_create(name, tags?): Register a workflow; returns a unique run_id.workflow_discover(tags?, status?): Find workflows filtered by tags or status.workflow_claim(run_id, agent_id): Atomically claim a created workflow.workflow_checkpoint(run_id, step, output): Persist step output and advance the step counter.workflow_resume(run_id): Get full resume context including last step and all step outputs.workflow_status(run_id): Get lightweight status (status, last step, and timestamps).workflow_list(): List all registered workflows.lease_acquire(resource_id, holder_id, ttl_seconds): Attempt to acquire an exclusive lock.lease_release(resource_id, holder_id): Voluntarily release a held lease.lease_renew(resource_id, holder_id, ttl_seconds): Extend lease duration without releasing.session_save(session_id, context): Save a snapshot of workflow context.session_restore(session_id): Retrieve saved context after crash or handoff.history_log(key?, n?): Retrieve the last N state-transition records with timestamps and writer IDs.sidecar_health(): Liveness, backend type, uptime, and database metrics.sidecar_reset(): Irreversibly wipe all data.This project is licensed under the MIT License. See LICENSE for details.