DevDocs
This DevDocs MCP implementation, developed by llmian-space, provides a Python-based interface for AI assistants to interact with software documentation. Built using libraries like Pydantic, Hypothesis, and Trio, it offers tools for processing, indexing, and retrieving documentation from various sources. The implementation focuses on efficient documentation handling, version management, and content transformation, making it easier for AI models to access and utilize technical documentation. It's particularly useful for enhancing AI capabilities in software development workflows, enabling tasks like code assistance, API exploration, or technical writing support without requiring deep knowledge of specific documentation formats or structures.
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 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- ai-ml
- Source
- PulseMCP
- Repository
- github.com/llmian-space/devdocs-mcp
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve DevDocs 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.