contextburn runs on your own machine — the client starts it, so there is no endpoint to ping. Last commit 13 Sep 2026.
Run efficiency for coding agents: share of paid tokens that became output, not context re-reading.
What the code does
We read the source, 17 min ago · rules 3dff92dd89df
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
let p = Process()
execFile(
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
This code can reach further than it looks
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
Connect this server
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add contextburn -- uvx contextburn{
"mcpServers": {
"contextburn": {
"args": [
"contextburn"
],
"command": "uvx"
}
}
}[mcp_servers.contextburn]
command = "uvx"
args = ["contextburn"]{
"mcpServers": {
"contextburn": {
"args": [
"contextburn"
],
"command": "uvx"
}
}
}{
"mcpServers": {
"contextburn": {
"args": [
"contextburn"
],
"command": "uvx"
}
}
}Alternatives to contextburn
same job, measured the same wayCompact, source-linked context packs that reduce token waste for coding agents.
Context GC for LLM agents: offload large tool outputs and recall them to save tokens.
Memory that reasons: continual learning for stateful agents. Better context, fewer tokens.
Efficient context management: code search, evidence packs, and memory for coding agents.
Token-bounded repository context for coding agents over MCP.
Deterministic context packing for AI coding agents; measured 83% fewer input tokens, local.
The Semantic Layer for AI Agents. Prunes noisy API data by 70%+ for token-efficient reasoning.
Token efficient search for coding agents over public and private documentation.
contextburn — questions
Answers built from our own checks of this server.