Context7 Semantic Indexer & Local Snippet Search

Governs the use of Context7 for indexing the Sovereign Markdown Palace and retrieving semantic context or local code snippets via MCP.

Purpose

This skill governs the integration of Context7 within the DSOM architecture. Context7 acts as both an external semantic retrieval engine (RAG) and a local code snippet database for the Sovereign Markdown Palace (docs/). It parses markdown documentation, chunks it, and generates searchable code/command snippets using LLM extraction.

Integration Guidelines

1. The Indexing Process

Based on our project structure, Context7 targets the docs/ folder:

• Context7 clones the main branch.
• It bypasses non-RST and non-code configurations to focus directly on Open Knowledge Format (OKF) markdown documentation.

2. Utilizing Context7 via MCP

For AI agents (Cursor, Google Jules, Claude) to query this semantic index:

1. Ensure the agent’s MCP (Model Context Protocol) is configured to connect to https://mcp.context7.com/mcp.
2. The agent must inject the CONTEXT7_API_KEY directly into the MCP client configuration via environment variables.

3. Triggering Re-indexing

Context7 automatically syncs with the configured repository branch. If a massive structural change occurs in the Palace, ensure the changes are fully committed and pushed to main so Context7’s webhook can detect the diff.

4. Direct LLM Endpoint & MCP Stream Access

For external AI tools (Cursor, Claude, ChatGPT, Gemini, Google Jules), Context7 exposes the live, compiled token stream:

• Canonical Stream Endpoint: https://context7.com/gitlab_linuxmalaysia/deep-state-of-mind-for-my-ai/llms.txt?tokens=83688
• MCP Native Tool: FastMCP exposes the fetch_context7_stream tool inside tools/mcp/server.py to stream or query Context7 snippets dynamically via stdio.
• Token Adjustments: Adjust the ?tokens=... parameter (e.g. 10000, 30000, 83688) based on the target LLM’s context window budget.

5. Local Code Snippet Search (search_code_snippets)

To avoid token bloat and query executable terminal commands quickly without external network round-trips:

• FastMCP exposes the search_code_snippets(query, limit) tool in tools/mcp/server.py.
• It searches the parsed blocks in references/llms-from-context7.txt and returns the most relevant code blocks and instructions.
• A human-readable compiled cheatsheet is maintained in docs/reference/CLI-QUICK-REFERENCE.md.

6. Offline Fallback & Local Snapshot Maintenance

When operating offline or in air-gapped environments:

• The local snapshot references/llms-from-context7.txt serves as the primary fallback.
• Calling fetch_context7_stream(return_offline_sample=True) provides immediate local context preview without internet access.

7. Automated & Manual Snapshot Updating

• Manual Invalidation & Fetch: Run the dedicated tool with desired token budget:
  python tools/fetch_context7_snapshot.py --tokens 250000
  
• Automated Scheduled Sync: .github/workflows/context7-sync.yml automatically executes every Sunday at 02:00 UTC (or on-demand via workflow_dispatch), pulling the latest 250k token snapshot using the provisioned CONTEXT7_API_KEY secret.

Security (Rule 24 Mandate)

• NEVER write the CONTEXT7_API_KEY (e.g., ctx7sk-...) to a local .env file, .git/config, or any file tracked by Git.
• GitLab CI/CD: If Context7 requires integration via GitLab CI, the API key MUST be stored in GitLab CI/CD Settings as a masked and protected variable.

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Deep State of Mind (DSOM) For My AI Protocol | Harisfazillah Jamel (LinuxMalaysia) | 2026-08-23 Standard: UK English | DBP-standard Bahasa Melayu Malaysia (Piawai) | GNU General Public License v3.0