opencode-glm-quota
MCP ServerFreeOpenCode plugin to query Z.ai GLM Coding Plan usage statistics including quota limits, model usage, and MCP tool usage
Capabilities5 decomposed
z.ai glm coding plan quota usage retrieval
Medium confidenceFetches real-time quota consumption metrics from Z.ai's GLM Coding Plan API, parsing structured usage data including total quota limits, consumed tokens, remaining capacity, and plan tier information. Implements MCP server protocol to expose quota endpoints as standardized tools callable from OpenCode IDE, abstracting authentication and API versioning details behind a unified interface.
Exposes Z.ai GLM quota as native MCP tools within OpenCode IDE rather than requiring separate dashboard access, enabling quota checks as part of the development workflow without context switching. Implements Z.ai-specific quota schema parsing rather than generic usage APIs.
Tighter IDE integration than checking Z.ai web dashboard manually, and more specific to GLM Coding Plans than generic cloud cost monitoring tools like CloudZero or Kubecost
model-specific usage breakdown retrieval
Medium confidenceDisaggregates quota consumption by individual GLM model variants (e.g., GLM-4, GLM-3.5-turbo), returning per-model token counts and cost attribution. Queries Z.ai's usage analytics API with model filtering parameters and aggregates results into a structured breakdown, enabling developers to identify which models are consuming quota most heavily.
Provides GLM model-specific disaggregation rather than treating quota as a monolithic pool, leveraging Z.ai's native usage analytics API to attribute consumption to individual model variants with cost mapping.
More granular than generic cloud billing tools, and specific to GLM model economics rather than generic LLM cost tracking
mcp tool usage statistics aggregation
Medium confidenceCollects and aggregates statistics on which MCP tools (function calls) are consuming quota within the Z.ai GLM Coding Plan, returning call counts, average token consumption per tool, and total quota attribution. Implements tool-level telemetry collection by intercepting MCP function call invocations and correlating them with Z.ai API usage logs.
Correlates MCP tool invocations with Z.ai quota consumption at the tool level, providing visibility into which integrations are most expensive rather than treating all tool calls as equivalent. Implements telemetry collection at the MCP protocol layer.
More specific to MCP tool economics than generic function call profiling, and integrated into the OpenCode workflow rather than requiring external observability tools
quota limit alert threshold configuration
Medium confidenceAllows developers to set custom warning thresholds (e.g., alert when 80% of quota is consumed) and receive notifications when consumption crosses those thresholds. Implements a polling-based monitor that periodically queries current quota usage and compares against configured thresholds, triggering IDE notifications or webhook callbacks when limits are approached.
Integrates quota alerting directly into the OpenCode IDE workflow with configurable thresholds and multi-channel notification support, rather than requiring separate monitoring dashboards. Implements client-side threshold logic rather than relying on Z.ai server-side alerts.
More proactive than manual dashboard checks, and more integrated than generic cloud cost monitoring alerts because it's aware of GLM Coding Plan semantics
quota consumption trend analysis and forecasting
Medium confidenceAnalyzes historical quota consumption patterns over configurable time windows (7 days, 30 days) and projects forward to estimate when quota will be exhausted at current burn rate. Implements time-series analysis by fetching historical usage snapshots from Z.ai API, fitting a linear or exponential regression model, and computing projected depletion date with confidence intervals.
Applies time-series forecasting to GLM quota consumption rather than treating usage as a static snapshot, enabling proactive quota management. Implements regression-based projection with confidence intervals rather than naive linear extrapolation.
More sophisticated than simple 'days remaining' calculations, and specific to GLM quota semantics rather than generic cloud cost forecasting
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Individual developers using Z.ai GLM models with OpenCode IDE
- ✓Teams managing shared GLM Coding Plan quotas across developers
- ✓LLM application builders needing quota awareness in their development loop
- ✓Teams optimizing LLM costs by choosing between model tiers
- ✓Developers debugging unexpected quota consumption spikes
- ✓Engineering leads allocating shared quota budgets across projects
- ✓Developers building complex MCP tool chains with multiple integrations
- ✓Teams managing shared tool libraries and needing usage accountability
Known Limitations
- ⚠Requires active Z.ai account with valid Coding Plan subscription
- ⚠No historical quota tracking — only returns current snapshot, not usage trends over time
- ⚠Polling-based approach means quota data may lag 30-60 seconds behind actual API consumption
- ⚠No quota forecasting or burndown prediction based on historical patterns
- ⚠Breakdown granularity limited to model variant level — no per-endpoint or per-feature attribution
- ⚠Requires Z.ai API to support model-level usage filtering; older plan tiers may not expose this data
Requirements
Input / Output
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OpenCode plugin to query Z.ai GLM Coding Plan usage statistics including quota limits, model usage, and MCP tool usage
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