io.github.egoughnour/massive-context-mcp

MCPcommunity
v3.0.1io.github.egoughnourUnknownUpdated 7mo agoGitHub

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.…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
7mo agoLast update
Package
Authorio.github.egoughnour
LicenseUnknown
Version3.0.1
Sourcemcp-registry
GitHubegoughnour/massive-context-mcp
Trust Status
B
60/100Good
✓Listed in Forge index+10/10
—Publisher identity verified+0/20
→ Publisher: run `forge publish` from the package repo to claim ownership
—Ed25519 publish signature+0/5
→ Included automatically when the publisher runs `forge publish`
—Domain verification+0/5
→ Publisher: host /.well-known/forge.json on the package homepage with { "publisher": "<github-login>" }
—npm Trusted Publishing (Sigstore)+0/5
→ Publish from GitHub Actions with --provenance so the attestation binds this package to this repo
—npm maintainer match+0/5
→ Earned once your identity is verified above and that login is an npm maintainer of this package
✓CVE scan · clean+30/30
✓Static analysis · clean+20/20
Paste into Claude Code, Cursor, or any AI assistant to fix all gaps
StatusCommunity-indexed
PublisherUnverified
SignatureUnsigned
Domain—
Provenance—
DependenciesNot audited
Tool surface—
Security scan✓ Cleanv3.0.1 · 2mo ago
EvalsNone
IndexedJun 13, 2026

Verification confirms publisher identity (repo ownership), not code safety. The security scan covers known CVEs and suspicious install scripts — it cannot prove the absence of malicious code.

About

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)…

Keywords
mcp