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354,277 tools. Last updated 2026-07-31 22:25

"KLM" matching MCP tools:

  • Run repository coding tasks end-to-end using GLM as an agent with file tools. Provides a concise summary and stats; use dry_run to preview changes before applying.
    MIT
  • Handle text-only subtasks via GLM subagent for reduced cost. Provide complete context in the task description since file access is unavailable.
    MIT
  • Monitor local GLM configuration health, active model, peak window, and token usage ledger with zero API calls.
    MIT
  • Recommends optimal engine (GLM vs Opus) for a task by analyzing its complexity, vision needs, language, and other parameters. Avoids overpaying for simple tasks or underpowering complex ones.
    MIT

Matching MCP Servers

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    Enables Claude Code to use GLM (Zhipu) as a cheap, full-capability subagent for file editing, code generation, and bash commands, with automatic routing between Opus and GLM based on task complexity.
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    MIT
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    An MCP server that lets Claude Code consult stronger AI models (o3, Gemini 2.5 Pro, DeepSeek Reasoner) when you need deeper analysis on complex problems.
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    MIT

Matching MCP Connectors

  • Swiss weather data for AI assistants — forecasts, measurements, stations, pollen.

  • Run a prompt through a LangChain (system + human) chain over Gemini on Vertex AI; optional LangSmith

  • Override the last routing decision to correct wrong model choices and record feedback for routing quality improvement.
    MIT
  • Route tasks to Google's Gemini models via local CLI agent. Uses your Google One AI Pro subscription as a fallback when Claude limits are reached, or for tasks benefiting from Gemini's capabilities.
    MIT
  • Routes AI tasks to the best model while tracking cumulative savings across sessions and hosts, even without client-side hooks.
    MIT
  • Displays real-time budget pressure across configured AI providers, using pressure bars to indicate quota exhaustion risk and help manage spending.
    MIT
  • Provide feedback on routing decisions to retrain the local classifier and improve model selection over time.
    MIT
  • Classifies task complexity and routes to the optimal LLM. Uses a cheap classifier to select from budget, balanced, or premium models based on difficulty.
    MIT
  • Evaluate and benchmark all available models on reasoning and code tasks to determine quality, speed, and accuracy, then optimize routing priorities.
    MIT
  • Apply bulk edits across multiple files by generating JSON edit instructions from a natural language task. Use for cross-file refactors and pattern updates.
    MIT
  • Completes a prompt by trying OpenAI, Anthropic, then Gemini, with automatic fallback and retry on rate limits or errors, returning the response and attempt log.
    MIT
  • Routes task prompts to the recommended agent and model by classifying complexity and applying your budget profile, enabling correct session-level setup.
    MIT