contextburn
PYPI · CONTEXTBURN · SCANNED SEP 20
Run efficiency for coding agents: share of paid tokens that became output, not context re-reading.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs setuptools.build_meta at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- No production dependencies, so there is no dependency health to assess. View diagnostics → Pass
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 8 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability71
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 97 tokens (~48/item across 2 items; 2 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management30
- Stability observed for 9 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage83
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 50% of tool parameters carry a description.Partial
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 2 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 2 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the contextburn MCP server?
contextburn runs locally as a PyPI package, launched with uvx contextburn. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · contextburn
claude mcp add ai-arsentev-contextburn -- uvx contextburn
{
"mcpServers": {
"ai-arsentev-contextburn": {
"command": "uvx",
"args": [
"contextburn"
]
}
}
} {
"servers": {
"ai-arsentev-contextburn": {
"command": "uvx",
"args": [
"contextburn"
]
}
}
} codex mcp add ai-arsentev-contextburn -- uvx contextburn
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"ai-arsentev-contextburn": {
"type": "local",
"command": [
"uvx",
"contextburn"
],
"enabled": true
}
}
} openclaw mcp add ai-arsentev-contextburn --command uvx --arg contextburn
mcp_servers:
ai-arsentev-contextburn:
command: "uvx"
args: ["contextburn"] {
"McpServers": {
"ai-arsentev-contextburn": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"contextburn"
]
}
}
} assistant mcp add ai-arsentev-contextburn -t stdio -c uvx -a contextburn
{
"mcpServers": {
"ai-arsentev-contextburn": {
"command": "uvx",
"args": [
"contextburn"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 20 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.
- 19 Sept 26 +4
- Stability: unverified → 0.27 ▲ functional
- 14 Sept 26 0
- Security disclosure: unverified → fail ▼ functional
- 13 Sept 26 0
- Security disclosure: fail → unverified ▼ functional
- 11 Sept 26 68
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 20 Sept 2026 · Analysed pypi/contextburn@0.2.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | setuptools.build_meta |
Background: Why install scripts are a supply-chain risk →
Dependencies 0 packages
| Packages resolved | 0 |
|---|---|
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
run_efficiency Run efficiency ~57
Share of paid tokens that became model output versus re-reading of context already sent, over the last N hours of local Claude Code sessions. Reported by tokens and cost-weighted.
| Name | Type | Req | Description |
|---|---|---|---|
| hours | number | – | Look-back window in hours. |
No output schema declared.
No examples provided.
spend_breakdown Spend breakdown ~40
Human-readable breakdown of token spend over the last N hours: run efficiency, sessions, and what specifically inflated the context.
| Name | Type | Req | Description |
|---|---|---|---|
| hours | number | – | – |
No output schema declared.
No examples provided.
What is the contextburn MCP server?
contextburn is an MCP server listed in the public MCP registry as ai.arsentev/contextburn. Run efficiency for coding agents: share of paid tokens that became output, not context re-reading. This page covers its PyPI package (contextburn).
Is the contextburn MCP server safe to use?
contextburn scores 73 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 20 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the contextburn MCP server expose?
contextburn exposes 2 tools: run_efficiency, spend_breakdown. Their descriptions and schemas cost roughly 97 tokens of context every time the server is loaded.
Is the contextburn MCP server still maintained?
contextburn is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the contextburn MCP server under?
contextburn declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.