tokenchit
Related Servers
Alternatives to tokenchit
No user-submitted related servers found.
Related Servers
- AlicenseAqualityBmaintenanceAnalyzes local Claude Code logs to measure subscription value, waste, and rate-limit usage, including chat share and current limits, without uploading data.4MIT
- AlicenseNot gradedqualityCmaintenanceAnalyzes Claude Code session token usage and cost locally — where spend actually lands across cache-read, cache-write and output, and what is consuming the context window. Read-only and offline: it parses your own session files and exposes analyze_claude_cost, get_cost_benchmark and tokenscope_share_summary.39 npm4MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents such as Claude Code, Cursor, Codex, and OpenCode to query local token usage, costs, quotas, projects, and models through six strictly read-only tools. All data stays on the local machine, drawn from passively parsed session logs rather than uploaded telemetry.MIT
- AlicenseNot gradedqualityAmaintenanceToken usage and estimated cost for Claude Code, Codex, Cursor and 13 more AI coding agents, read locally from their session files. No API key.1AGPL 3.0
- AlicenseBqualityBmaintenanceLets a coding agent inspect its own run efficiency mid-session from local Claude Code transcripts, exposing the share of paid tokens that became model output versus context re-reading. Ships two stdio tools — one returning the shares as structured data and one returning the full cost-weighted report — with no network calls.238 PyPIMIT
- AlicenseNot gradedqualityBmaintenanceEnables offline, read-only forensic analysis of Claude Code session transcripts, providing exact token usage, cost estimates, context growth, and compaction suggestions.MIT
This server cannot be deployed
TDQS
Scored across 4 tools
get_usage (windowed totals), get_daily_usage (per-day series), and get_recap (year-in-review aggregates) have partially overlapping subject matter, but each description clearly states its scope and output shape. detect_agents is cleanly distinct. Minor risk that an agent asks for a time range and picks get_usage over get_daily_usage.
Three tools follow a clean get_<noun> snake_case pattern (get_usage, get_daily_usage, get_recap). detect_agents breaks the prefix convention slightly but is still snake_case and readable, so the set is nearly uniform.
Four focused tools cover the analytics surface without redundancy. It is on the lean side but each tool earns its place; nothing feels padded or missing at the count level.
Covers lifetime/window totals, daily series, yearly recap, and agent discovery/diagnostics, which addresses the core questions of a usage-tracking server. Possible gaps like per-project rollups or cross-agent comparison views are not indicated as supported, but no obvious dead end for the stated purpose.