Frequently Asked Questions about Specification MCP

Q: What is Specification MCP?

A: Specification MCP is an MCP server designed to help teams create, manage, and access structured project documentation using AI and a hierarchical document structure.

Q: What is MCP?

A: MCP stands for Model Context Protocol. It’s an open protocol that standardizes how applications provide context to Large Language Models (LLMs).

Q: How does Specification MCP use AI?

A: Specification MCP uses the Gemini API to auto-generate comprehensive documentation and assist with updating existing documents.

Q: What are the core document types in Specification MCP?

A: The core document types are: project brief, product context, system patterns, tech context, active context, and progress.

Q: Can I customize the location where Specification MCP stores the generated files?

A: Yes, you can choose the directory for generated files.

Q: How do I integrate Specification MCP with the Model Context Protocol (MCP)?

A: You add a configuration snippet to your mcp.json file, specifying the path to the built file and your Gemini API key (if applicable).

Q: Do I need a Gemini API key to use Specification MCP?

A: While optional, using a Gemini API key unlocks the AI-powered documentation generation features. You can still use Specification MCP without it, but you’ll need to manually create and update the documents.

Q: What is the initialize_memory_bank tool used for?

A: It creates a new Memory Bank structure, initializing your project’s knowledge base within Specification MCP.

Q: What kind of queries can I perform with the query_memory_bank tool?

A: You can perform context-aware searches across all documents within your Specification MCP knowledge base.

Q: Can I export the documents in Specification MCP?

A: Yes, you can export the documents to JSON or a folder.

Q: What license is Specification MCP released under?

A: Specification MCP is released under the MIT License.

Q: How does Specification MCP relate to UBOS?

A: Specification MCP aligns well with the UBOS platform as it provides a structured way to manage project knowledge that AI Agents on the UBOS platform can leverage for automation, insights, and data-driven decisions.