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agentburn

PYPI · AGENTBURN

Local profiler: which usage window took you out, and where your agent's money goes

Available components

0 this week 95 Trust /100

Last scanned (20 hours ago)

Scores higher than 98% of PyPI servers

Category Data and Analytics

Trust breakdown (7 categories)

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 build step with no custom scripting around it. View diagnostics → Pass
  • No production dependencies, so there is no dependency health to assess. View diagnostics → Pass
Provenance & Transparency97
  • Source repository is publicly reachable at the declared URL. View diagnostics → Pass
  • Cryptographically verified build provenance (signed, bound to Socialpranker/agentburn). View diagnostics → Pass
  • Clear OSI-approved license (MIT).Pass
  • Actively maintained (last published 59 days ago).Pass
  • Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability77
  • AI-judged instruction clarity (excellent).Pass
  • Context-footprint check failed: tool/resource definitions use about 710 tokens (~118/item across 6 items; 6 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
  • No destabilizing schema changes in the last 30 days.Pass
Tool Coverage100
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 100% of tool parameters carry a description.Pass
Tool Safety100
  • No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
  • We read all 6 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
  • An AI judge read all 6 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
Changelog

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.

  • 27 Oct 26 0
    • Stability: 0.97 → pass security
  • 26 Oct 26 0
    • Stability: pass → 0.97 functional
  • 25 Oct 26 0
    • Package version: 0.12.3 → 0.14.0 functional
  • 20 Oct 26 0
    • Stability: 0.97 → pass security
  • 19 Oct 26 0
    • Stability: pass → 0.97 functional
    • Package version: 0.12.3 → 0.14.0 functional
  • 17 Oct 26 +2
    • Stability: 0.87 → pass security
  • 15 Oct 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 80 to 83. That category is still filling its 30-day observation window: 24 days of observed history at the previous scan, 25 at this one. The score rises as the window fills, whether or not the server changes.

  • 14 Oct 26 −3
    • Stability: pass → 0.80 functional
Diagnostics

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 4 Nov 2026 · Analysed pypi/agentburn@0.14.0

Provenance Verified

A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.

Result Verified
Ecosystem pypi
Reason Verified
Discovered via Registry attestation endpoint
Source repo Socialpranker/agentburn
Certificate issuer https://token.actions.githubusercontent.com
Certificate SAN https://github.com/Socialpranker/agentburn/.github/workflows/publish.yml@refs/tags/v0.14.0
Rekor log index 2735365699
Predicate type PyPI publish attestation https://docs.pypi.org/attestations/publish/v1
Subject digest sha256:f660b6c8c6d3839a0fe29136b29f123cd577dbdd792a2db9115f17ecd3e5156a

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 →

MCP tools · 6 exposed · ~710 tokens

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 →

Tool Tokens
burn_card ~88

Anonymized shareable burn summary (plain text, safe to post).

NameTypeReqDescription
agentstring–hermes | openclaw | claude-code (default: first detected)
daysinteger–window in days (default 30, 0 = all time)
sourcestring–drill into one source, e.g. telegram / cron / heartbeat / subagent

No output schema declared.

No examples provided.

burn_commits ~109

What each git commit cost, in weighted tokens: sessions joined to the repositories they ran in (read-only git log). Costliest commits, median per repository. Returns JSON.

NameTypeReqDescription
agentstring–hermes | openclaw | claude-code (default: first detected)
daysinteger–window in days (default 30, 0 = all time)
sourcestring–drill into one source, e.g. telegram / cron / heartbeat / subagent

No output schema declared.

No examples provided.

burn_context ~140

The price of long contexts on this machine: share of the usage window spent at each context size, what a /clear at 100k/150k/200k/300k would have saved, the longest sessions, usage by effort level, and what each skill costs per load (measured from context growth). Returns JSON.

NameTypeReqDescription
agentstring–hermes | openclaw | claude-code (default: first detected)
daysinteger–window in days (default 30, 0 = all time)
sourcestring–drill into one source, e.g. telegram / cron / heartbeat / subagent

No output schema declared.

No examples provided.

burn_limits ~139

For subscription plans, where the invoice is fixed and what runs out is the usage window: how full the peak rolling 5-hour window got, how it compares with a typical one, what filled it (model, source, cache reads vs output). Weighted by published price ratios; no provider limit formula is assumed. Returns JSON.

NameTypeReqDescription
agentstring–hermes | openclaw | claude-code (default: first detected)
daysinteger–window in days (default 30, 0 = all time)
sourcestring–drill into one source, e.g. telegram / cron / heartbeat / subagent

No output schema declared.

No examples provided.

burn_report ~117

Where this machine's AI agent burns money: totals, monthly pace, breakdown by source (cron/heartbeat/gateways/subagents/cli), models, overnight window, fixed overhead per call, recommendations. Returns JSON.

NameTypeReqDescription
agentstring–hermes | openclaw | claude-code (default: first detected)
daysinteger–window in days (default 30, 0 = all time)
sourcestring–drill into one source, e.g. telegram / cron / heartbeat / subagent

No output schema declared.

No examples provided.

burn_why ~117

Behavioral forensics from the agent's own records: functions called (with error counts), re-read loops, retry storms, idle heartbeats, money burned in failed runs, plus what-to-change observations. Returns JSON.

NameTypeReqDescription
agentstring–hermes | openclaw | claude-code (default: first detected)
daysinteger–window in days (default 30, 0 = all time)
sourcestring–drill into one source, e.g. telegram / cron / heartbeat / subagent

No output schema declared.

No examples provided.

Install

How do I install the agentburn MCP server?

agentburn runs locally as a PyPI package, launched with uvx agentburn. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

pypi · agentburn

# add to Claude Code
claude mcp add socialpranker-agentburn -- uvx agentburn
Common questions

What is the agentburn MCP server?

agentburn is an MCP server listed in the public MCP registry as io.github.Socialpranker/agentburn. Local profiler: which usage window took you out, and where your agent's money goes. This page covers its PyPI package (agentburn).

Is the agentburn MCP server safe to use?

agentburn scores 95 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 4 November 2026. Its build provenance is signed and verified. 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 agentburn MCP server expose?

agentburn exposes 6 tools: burn_report, burn_why, burn_limits, burn_context, burn_commits, burn_card. Their descriptions and schemas cost roughly 710 tokens of context every time the server is loaded.

Is the agentburn MCP server still maintained?

agentburn is still listed as active in the MCP registry. We last reached this channel on 4 November 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 agentburn MCP server under?

agentburn declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.