Find keyword mentions in AI model outputs from ChatGPT and Google AI. Returns mention context and sources.
MIT
256,169 tools. Last updated 2026-07-04 08:33
"Optimizing AI Model Thinking, Token Usage, and Context Size" matching MCP tools:
- Execute a single AI model call to test prompts before building full workflows. Returns output, token usage, estimated provider cost, and trace URL.MIT
- Execute multi-step AI workflows with reduced context usage by keeping intermediate results in the workflow engine, supporting multiple model calls and tool integrations.MIT
- Execute LLM requests by burning Shells to get AI-generated responses with calculated costs based on model and token usage.
- Query any AI model with a prompt and receive its response with metadata including latency and token usage. Optionally limit response tokens with automatic distillation.MIT
- Compare AI model performance by testing 1-5 models simultaneously with identical prompts. Get output text, latency, token usage, and cost estimates for informed model selection.MIT
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- Read compiled brand runtime to provide brand system context for AI agents. Supports slice options to optimize token usage.MIT
- Check remaining context capacity by viewing message count, token usage, and bloat indicators. Helps decide if pruning old messages is needed before continuing.MIT
- Record token usage and cost per task after each AI interaction to track spending and enable budget monitoring.MIT
- Runs all AI cost health metrics in one call, including prompt cache savings, context window waste, model sprawl, and prompt efficiency, delivering estimated monthly savings and specific remediation advice.Elastic 2.0
- Assess prompt complexity and token usage to select the best model, adjusting for budget pressure and quality needs.MIT
- Compare AI model pricing across Anthropic, OpenAI, Google, Meta, Mistral, and Cohere. Get input and output prices per 1M tokens, context window, and release date in one table to select the cheapest model for your budget and context.MIT
- Retrieve context consumption statistics for the current session, including byte counts, tool breakdowns, token estimates, and savings ratio.Elastic 2.0
- Calculate token count for Gemini AI prompts to estimate costs and ensure they fit within model context limits.Apache 2.0
- Get a paginated per-model breakdown of request count, cost, and token usage to compare model cost, popularity, and efficiency.MIT
- Analyze LLM costs and token usage by model from Langfuse. Understand which models drive spend and optimize model selection based on cost-per-1k-token efficiency.Elastic 2.0
- Retrieve HuggingFace model metadata including architecture, parameter count, size, hidden dimensions, layers, vocabulary size, and context length. No GPU required.MIT
- Chat with DeepSeek V4 models (flash for speed, pro for capability) offering 1M context, multi-turn sessions, function calling, thinking mode, JSON output, and multimodal input.MIT
- Project token usage costs across 1 to 10 AI models. Get daily, weekly, monthly, and yearly totals per model, ranked by cheapest monthly cost. Input your expected daily token volumes and select models to compare.MIT
- Estimate token usage for the current session, including schema size, call counts, and largest tool results.MIT