codex-usage-tracker MCP Server
douglasmonsky/codex-usage-tracker
[](https://agentrank-ai.com/tool/douglasmonsky--codex-usage-tracker/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) Local-first MCP tools and dashboard for investigating Codex token usage, credits, costs, caching, and thread patterns.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
douglasmonsky/codex-usage-tracker is a Python tool licensed under MIT. Local-first MCP tools and dashboard for investigating Codex token usage, credits, costs, caching, and thread patterns. Topics: codex, openai, python, token-usage, codex-plugin, codex-skill, dashboard, cli, codex-cli, cost-analysis, developer-tools, llm-observability, local-first, privacy-first, prompt-caching, usage-analytics, mcp, model-context-protocol, pypi, sqlite.
Ranked #7373 out of 24846 indexed tools.
Ecosystem
Score Breakdown
195 stars → growing community
Last commit 2mo ago → moderate activity
40/53 issues closed → decent issue management
6 contributors → small team
No dependents → no downstream usage
5 weekly installs → early adoption
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
Matched Queries
From the README
# Codex Usage Tracker Codex Usage Tracker is a local usage-data product for answering questions about how Codex work consumes tokens, time, tools, allowance, credits, and cost. It turns local session metadata into exact, bounded facts and stable evidence that an agent can explore and explain. ## Current release and next direction Version 0.28.0 is the current public release. Its six-tool MCP surface, Evidence Console, and SQLite kernel remain supported while the replacement is qualified. The next product is a clean, agent-first kernel rather than a larger dashboard: - fast recent-history setup with explicit 24-hour through all-time coverage; - incremental reuse on every later invocation; - exact session, turn, call, tool, resource, allowance, token, credit, and cost facts; - compact named queries and stable evidence selectors; - agent-owned interpretation instead of server-authored narrative findings; - installed Codex qualification measured for latency, MCP calls, response byRead full README on GitHub →
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