Borrowing it
Nothing to install: this file belongs to Yuchenhui/mcp-redis. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Yuchenhui/mcp-redis/main/GEMINI.mdgit clone --depth 1 https://github.com/Yuchenhui/mcp-redisWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/yuchenhui/mcp-redis/gemini-md)<a href="https://agentmods.dev/instructions/yuchenhui/mcp-redis/gemini-md"><img src="https://agentmods.dev/badge/instructions/yuchenhui/mcp-redis/gemini-md.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.01534 | $0.01534 |
| Opus 5 | $0.00767 | $0.00767 |
| Sonnet 5 | $0.00307 | $0.00307 |
| Haiku 4.5 | $0.00153 | $0.00153 |
Grade A, and why
mcp-redis GEMINI.md scanned grade A with 0 findings 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 7d ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to mcp-redis GEMINI.md — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Redis MCP Server Extension
This extension provides a natural language interface for managing and searching data in Redis through the Model Context Protocol (MCP).
What this extension provides
The Redis MCP Server enables AI agents to efficiently interact with Redis databases using natural language commands. You can:
- Store and retrieve data: Cache items, store session data, manage configuration values
- Work with data structures: Manage hashes, lists, sets, sorted sets, and streams
- Search and filter: Perform efficient data retrieval and searching operations
- Pub/Sub messaging: Publish and subscribe to real-time message channels
- JSON operations: Store, retrieve, and manipulate JSON documents
- Vector search: Manage vector indexes and perform similarity searches
Available Tools
String Operations
- Set, get, and manage string values with optional expiration
- Useful for caching, session data, and simple configuration
Hash Operations
- Store field-value pairs within a single key
- Support for vector embeddings storage
- Ideal for user profiles, product information, and structured objects
List Operations
- Append, pop, and manage list items
- Perfect for queues, message brokers, and activity logs
Set Operations
- Add, remove, and list unique set members
- Perform set operations like intersection and union
- Great for tracking unique values and tags
Sorted Set Operations
- Manage score-based ordered data
- Ideal for leaderboards, priority queues, and time-based analytics
Pub/Sub Operations
- Publish messages to channels and subscribe to receive them
- Real-time notifications and chat applications
Stream Operations
- Add, read, and delete from data streams
- Event sourcing, activity feeds, and sensor data logging
JSON Operations
- Store, retrieve, and manipulate JSON documents
- Complex nested data structures with path-based access
Vector Search
- Manage vector indexes and perform similarity searches
- AI/ML applications and semantic search
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.
- 7d ago First seen · 185 lines · 1,534 tokens per session scan A ca016d06cb58
mcp-redis GEMINI.md is an instructions file published in the GitHub repository Yuchenhui/mcp-redis (0 stars, last pushed 10mo ago), licensed MIT. It adds 1,534 tokens to every session, about $0.0077 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to mcp-redis GEMINI.md, differing in 0 lines, and is treated as a copy.
Other instructions, from other repositories
plan-forge caching.instructions.md
Caching patterns for .NET — IDistributedCache, Redis, in-memory, cache-aside, TTL strategies.
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.