Quick question to GLM - no tools, fast response.

Use for: explanations, analysis, brainstorming, quick answers. Does NOT have access to files or tools - pure generation.

Cost: ~10x cheaper than Claude Haiku. Use liberally.

Args: question: Your question or prompt model: "haiku" (fastest) or "sonnet" (better quality)

Returns: GLM's response

Summarize text using GLM.

Use for: document summaries, meeting notes, code explanations.

Args: text: The text to summarize style: "concise", "detailed", "bullet-points", "executive" model: "haiku" or "sonnet"

Returns: Summarized text

Explain code or a concept using GLM.

Use for: understanding code, learning concepts, documentation.

Args: code_or_concept: Code snippet or concept to explain context: Additional context (e.g., language, framework) model: "haiku" or "sonnet"

Returns: Explanation

Analyze codebase using GLM with read access.

Use for: understanding code structure, finding patterns, architecture analysis, dependency mapping.

Has READ-ONLY access: Read, Glob, Grep, LS, Bash (safe commands).

Args: task: Analysis task to perform working_directory: Project directory (defaults to current) model: "haiku" or "sonnet"

Returns: Analysis results

Code review by GLM agent.

Use for: security review, performance analysis, best practices.

Args: code_or_file: Inline code or file path to review review_focus: "general", "security", "performance", "style", "bugs" working_directory: Directory context for file paths model: "haiku" or "sonnet"

Returns: Code review with findings and suggestions

Find potential bugs in code using GLM.

Use for: bug detection, edge case analysis, error prone patterns.

Args: code_or_file: Inline code or file path to analyze working_directory: Directory context for file paths model: "haiku" or "sonnet"

Returns: List of potential bugs with explanations

GLM agent with write access for implementation tasks.

Use for: writing code, creating files, making changes, refactoring.

Has FULL access including Write and Edit. WARNING: This can modify files.

Args: task: Implementation task to perform working_directory: Project directory (REQUIRED) allowed_tools: Tools to allow (default: full coding set) model: "haiku" or "sonnet"

Returns: Agent's output including changes made

Refactor code using GLM.

Use for: improving code structure, applying patterns, cleanup.

Args: file_path: Path to file to refactor instructions: Refactoring instructions working_directory: Project directory model: "haiku" or "sonnet"

Returns: Refactored code and explanation

Generate unit tests for a file using GLM.

Use for: test generation, coverage improvement, TDD support.

Args: file_path: Path to file to test test_framework: "pytest", "jest", "vitest", "unittest" working_directory: Project directory model: "haiku" or "sonnet"

Returns: Generated test file

Add or update documentation for a file using GLM.

Use for: adding docstrings, generating README, documenting APIs.

Args: file_path: Path to file to document style: "google", "sphinx", "numpy", "javadoc" working_directory: Project directory model: "haiku" or "sonnet"

Returns: File with added documentation

Generate a README.md for a project using GLM.

Use for: creating project documentation, onboarding.

Args: working_directory: Project directory style: "standard", "comprehensive", "minimal" model: "haiku" or "sonnet"

Returns: Generated README.md content

Check GLM MCP server status and configuration.

Returns: Status information about the GLM server setup

Compare costs between Claude and GLM.

Args: tokens_input: Input tokens to compare tokens_output: Output tokens to compare

Returns: Cost comparison table