Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Packet ordering simulation and inter-arrival jitter telemetry.
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.
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Download the maker’s .mcpb file
The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.
Run this in your terminal
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.
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 Alpha-Park, 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 (1.0.1) reached the registry 40 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need MCPB on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, Claude Checkpoint 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.
MCP server that checkpoints Claude Code session context to survive crashes and token limits
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.
Packet ordering simulation and inter-arrival jitter telemetry.
This is a packet metadata simulation, not a WebRTC implementation or audio codec. Timestamps must already be converted to milliseconds, and sequence numbers must be monotonically extended (no RTP wraparound). Missing packets produce markers, not synthesized audio. The caller controls playout timing; target_delay_ms is telemetry, not an enforced timer.
Python 3.9 or newer. The library and stdio MCP server have no runtime dependencies.
python -m pip install https://github.com/Alpha-Park/genpark-audio-packet-jitter-resilience-buffer-skill/releases/download/v1.0.1/genpark_jitter_buffer-1.0.1-py3-none-any.whl
PyPI publication is pending account setup. The intended PyPI project is genpark-jitter-buffer; do not assume pip install genpark-jitter-buffer is available until the project is published.
from genpark_jitter_buffer import AudioPacketJitterResilienceBuffer
client = AudioPacketJitterResilienceBuffer() print(client.run_benchmark_jitter_simulation())
After installing the wheel, configure your MCP client with the installed command:
{
"mcpServers": { "genpark-jitter-buffer": { "command": "genpark-jitter-buffer", "args": [] } } }
If the command is not on PATH, use its absolute path or python -m genpark_jitter_buffer with the same interpreter where you installed the wheel. The GitHub release also contains a .mcpb bundle for clients supporting desktop extensions. That bundle requires a Python 3.9+ interpreter on PATH; it bundles the server source.
Available tools: ingest_packet, extract_playout_frame, get_jitter_telemetry, run_benchmark_jitter_simulation. tools/list returns required arguments and JSON schemas. Each MCP process holds its own state. Benchmark tools use isolated instances.
python -m unittest discover -s tests
python -m pip install mcp python tests/check_mcp.py python -m pip install build twine python -m build python -m twine check dist/*
python mcp_server.py --test runs the deterministic example; it is not a protocol conformance test. The MCP client check exercises initialize, tools/list, tools/call and ping over stdio.
GitHub source and release artifacts are the primary distribution until PyPI is configured. Registry submissions are tracked separately; a manifest is not proof of registry acceptance. See PUBLISHING.md for the repeatable PyPI workflow.
MIT license. Maintained by GenPark.