Handles 10M+ token contexts with chunking, sub-queries, and local Ollama inference.
Handle massive contexts (10M+ tokens) with chunking, sub-queries, and free local inference via Ollama. Based on the Recursive Language Model pattern. Inspired by richardwhiteii/rlm. Instead of feeding massive contexts directly into the LLM: 1. Load context as external variable (stays out of prompt) 2. Inspect structure programmatically 3. Chunk strategically (lines, chars, or paragraphs) 4.…
Inferred from the transports this listing declares (stdio). A client not listed here hasn’t been ruled out — it just isn’t something Forge can confirm.
Verification confirms publisher identity (repo ownership), not code safety. The security scan covers known CVEs and suspicious install scripts.
Forge read 0 source files from the published package tarball and matched no MCP tool registrations. Extraction is pattern-based over shipped source: a server that builds its tool list at runtime, or that ships only bundled or minified code, registers nothing this can see. Treat it as “not detected”, not as “exposes none”.
Handle massive contexts (10M+ tokens) with chunking, sub-queries, and free local inference via Ollama. Based on the Recursive Language Model pattern. Inspired by richardwhiteii/rlm. Instead of feeding massive contexts directly into the LLM: 1. Load context as external variable (stays out of prompt) 2. Inspect structure programmatically 3. Chunk strategically (lines, chars, or paragraphs) 4. Sub-query recursively on chunks 5. Aggregate results for final synthesis Option 1: PyPI (Recommended)…
Forge's dependency resolver reads npm metadata only, so this PyPI package has no resolved tree. That is a gap in coverage, not a clean bill of health.