GLM Vision
This MCP server by danilofalcao integrates GLM-4.5V vision model from Z.AI, providing image analysis capabilities through a single tool that supports both local files and URLs. Built with FastMCP and httpx for async HTTP operations, it features configurable parameters including temperature control, thinking mode for exposing model reasoning, and adjustable token limits up to 64K. The implementation includes comprehensive error handling, base64 encoding for local images, MIME type detection, and GitHub Actions workflows for CI/CD with PyPI publishing, making it valuable for developers who need AI-powered image analysis integrated into their workflows using Z.AI's GLM vision models rather than OpenAI's alternatives.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- version-control
- Source
- PulseMCP
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve GLM Vision from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.