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Finance and payments · MCP server

Tradememory protocol: MCP server, setup and security

Tradememory protocol is an open-source MCP server that lets an AI agent maintain layered trading memory for trades, patterns and strategies. Its repository has 1,431 stars and its last commit is from October 23, 2026 and it is licensed under MIT.

Score
82/100
Adoption (stars) 32/40
Maintenance 30/30
Framework (licence, registry, official) 20/30
Actively maintainedTop 10% by stars

What Tradememory protocol does

According to its repository: “Layered memory for AI trading agents (trades, patterns, strategy), supporting MT5, Binance and Alpaca.”

An MCP server gives an AI agent extra tools it calls during the conversation: the agent reads their description, decides to use them and gets the result back. Tradememory protocol is listed under Finance and payments, a category of 202 servers in the directory, where it ranks #3 by stars.

Topics declared by the repository: claude, forex, memory, mt5, trading, ai-agents.

Installing and connecting Tradememory protocol

Distribution modes declared in the official MCP registry:

  • PyPI package (Python, started with uvx or pip)

Like any MCP server, you declare it in your client's configuration (Claude Desktop, Claude Code, Cursor, VS Code, Codex…): a name, the command that starts it or its URL, and any environment variables. The client then starts it and lists its tools. Project language: Python.

Security: what to check

An MCP server runs code with the rights you give it, and the model reads its tool descriptions: a malicious or compromised server can hijack the agent. Read the code, or pick a maintained and widely used server, before connecting it.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Tradememory protocol. That does not replace reading the code.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on February 23, 2026, the mnemox-ai/tradememory-protocol repository has 1,431 stars and 170 forks, with 5 open issues. It has more stars than 94% of the servers in the directory. It received changes this month. Version published in the registry: 0.5.6 (October 2, 2026).

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.

Finance ToolkitFinance Toolkit : 5.5K5.5KTradingMCPTradingMCP : 5.1K5.1KTradememory protocolTradememory protocol : 1.4K1.4KCNEquityCNEquity : 846846Metatrader MCP serverMetatrader MCP server : 837837EVMoleEVMole : 473473OpenAccountantsOpenAccountants : 460460AkTools MCP ServerAkTools MCP Server : 391391
GitHub stars of the most followed servers in the category (log scale); Tradememory protocol in orange. Checked October 25, 2026.

Verdict

Tradememory protocol is one of the most solid servers in its category: followed, maintained and well framed.

Strengths

  • Active project: recent commit
  • Widely followed: 1.4K stars
  • Listed in the official MCP registry
  • Permissive licence (MIT)

Frequently asked questions

How do I install Tradememory protocol?

Tradememory protocol is distributed as PyPI package (Python, started with uvx or pip). Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of mnemox-ai/tradememory-protocol.

Is Tradememory protocol free?

The code is published under the MIT licence and can be used free of charge.

Is Tradememory protocol safe?

No risk signal found (system access, secrets, licence, abandonment). As with any MCP server, keep rights minimal and read the code before sensitive use.

Is it an official server?

Nothing says so: it is a project published by mnemox-ai on GitHub. Check who maintains it before giving it access.

What are the alternatives to Tradememory protocol?

In the same category: TradingMCP, Finance Toolkit, Tapetide Indian Stock Market MCP, and DexPaprika.

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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score