Skip to main content
Glama
arsentev-ai

contextburn

by arsentev-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to contextburn

No user-submitted related servers found.

    Related Servers

    • tokenscope

      wartzar-bee
      A
      license
      Not graded
      quality
      B
      maintenance
      Analyzes 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.
      113 npm
      4
      MIT

    • tokenchit

      iyashjayesh
      A
      license
      Not graded
      quality
      A
      maintenance
      Reads the Claude Code, Codex and OpenCode usage logs already on your machine and reports token totals, per-day and per-hour activity, a per-agent and per-model breakdown, and a year-in-review recap. It makes no network request at all — a test fails if any source file in the package can open a socket.
      4
      15
      MIT

    • Token Analyzer MCP

      cordlesssteve
      F
      license
      Not graded
      quality
      Not graded
      maintenance
      Provides intelligent analysis of token usage patterns and optimization recommendations to improve efficiency and reduce costs in Claude Code sessions. Offers real-time analysis, cost metrics, and actionable insights for better context window and tool usage optimization.
      3 npm
      -

    • token-meter

      whdrnr2583-cmd
      A
      license
      A
      quality
      B
      maintenance
      Local-first dashboard + MCP server that parses Claude Code and Codex JSONL files into a SQLite cost / token tracker. Per-MCP and per-tool breakdown, session drill-down, dedup by request_id; never talks to vendor APIs
      5
      117 npm
      1
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.2/5.0

    Scored across 2 tools

    Disambiguation3/5

    Both tools report on token spend and efficiency over the last N hours, and spend_breakdown explicitly includes run efficiency, so their boundaries overlap. An agent could reasonably confuse which tool to call for a pure efficiency metric versus a full breakdown.

    Naming Consistency4/5

    Both tools use snake_case with descriptive two-word noun phrases, which is a predictable and consistent convention. However, run_efficiency is slightly less standard as a noun than spend_breakdown, and neither follows a verb_noun action pattern.

    Tool Count3/5

    Two tools is borderline thin for a server analyzing context burn. While each tool has a distinct output, the surface feels minimal and may lack supporting operations like listing sessions or filtering by project.

    Completeness3/5

    The tools cover efficiency metrics and a spend breakdown including sessions, but there is no tool to list sessions, drill into a specific session, or filter by project/model. Core reporting is present but not exhaustive, which could cause dead ends for deeper analysis.

    Maintenance

    ActivityNo data
    ResponsivenessUnresponsive

    Resources