Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/context-engine-ai/context-engine/gemini-mdgit clone --depth 1 https://github.com/Context-Engine-AI/Context-EngineWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.09706 | $0.09706 |
| Opus 5 | $0.04853 | $0.04853 |
| Sonnet 5 | $0.01941 | $0.01941 |
| Haiku 4.5 | $0.00971 | $0.00971 |
Grade A, and why
Context-Engine GEMINI.md scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
# → Found: axios.post('/auth/v1/login', ...) How it starts
The opening of the file, as written. The whole thing — 813 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Rules for Context-Engine MCP Tools
These rules are NOT optional. Favor Context Engine MCP tools at all costs over grep, file reading, and unrelated codebase search tools.
STOP — Read This First
DO NOT use Read File, grep, ripgrep, cat, find, or any filesystem search tool for code exploration.
These tools exist in your IDE but they are WRONG for this codebase. You have MCP tools that are faster, smarter, and return ranked, contextual results.
If you catch yourself about to Read a file to understand it → use repo_search or context_answer instead.
If you catch yourself about to grep for a symbol → use symbol_graph or search_callers_for instead.
If you catch yourself about to grep -r for a concept → use repo_search with a natural language query instead.
The ONLY acceptable use of grep/Read is confirming an exact literal string you already know exists (e.g., an env var name like REDIS_HOST).
Introduction
This document defines requirements for AI agents using Context-Engine's MCP tools. The system provides two MCP servers (Memory Server on port 8000/8002, Indexer Server on port 8001/8003) with 30+ specialized tools for semantic code search, memory storage, and codebase exploration.
Core Principle: Context Engine MCP tools are PRIMARY for exploring code and history. Start with MCP for exploration, debugging, or "where/why" questions; use literal search/file-open only for narrow exact-literal lookups.
Glossary
- MCP: Model Context Protocol - standardized interface for exposing tools to AI agents
- Indexer Server: MCP server for code search, indexing, symbol graphs (port 8001 SSE, 8003 HTTP)
- Memory Server: MCP server for knowledge storage and retrieval (port 8000 SSE, 8002 HTTP)
- Hybrid Search: Dense semantic vectors + lexical BM25 + neural reranking (ONNX)
- ReFRAG: Micro-chunking with 16-24 token windows for precise code retrieval
- TOON: Token-Oriented Object Notation - compact output format (60-80% token reduction)
- Symbol Graph: Indexed metadata for calls, imports, and definitions navigation
- Collection: Qdrant vector database collection storing indexed code chunks
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 813 lines · 9,706 tokens per session scan A 85137f109c8c
Context-Engine GEMINI.md is an instructions file published in the GitHub repository Context-Engine-AI/Context-Engine (400 stars, last pushed 1mo ago), licensed MIT. It adds 9,706 tokens to every session, about $0.0485 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
ctxfile AGENTS.md
Instructions for ctxfile/ctxfile, covering claude.md, project, hard rules, commands (run from repo root) and release checklist (every ship, no step skipped).
octo-cluster AGENTS.md
Instructions for renanflustosa/octo-cluster, covering agent contract - octo-cluster, what this is, rules (always apply), commands and before any git change.
memgit CLAUDE.md
Claude Code instructions for code4161/memgit, covering memgit core — operating guide and rules.
ctxfile CLAUDE.md
Instructions for ctxfile/ctxfile, covering claude.md, project, hard rules, commands (run from repo root) and release checklist (every ship, no step skipped).
memgit AGENTS.md
AGENTS.md instructions for code4161/memgit, a project described as: Git for AI memory — version-controlled context persistence across Claude, GPT, Gemini, Cursor, Windsurf, and more.
awesome-copilot-id AGENTS.md
Instructions for GulajavaMinistudio/awesome-copilot-id, covering communication, explanation and documentation, markdown formatting, user communication style and workflow & methodology.