MCP server for secure AI agent authentication — lets Claude, Claude Code, and other LLM agents call APIs without exposing raw tokens or credentials to the model
An MCP server that lets Claude, Claude Code, and other AI agents call authenticated APIs — without exposing raw API tokens, keys, or credentials to the LLM.
When an AI agent needs to call an authenticated API, most MCP setups put the secret directly in the client configuration:
This means the raw credential lives in mcp.json, .claude.json, or shell environment — visible to the agent's configuration layer, potentially logged, and exposed if the configuration is shared or leaked.
secure-actions keeps credentials inside a trusted execution boundary. The AI agent references a secret by name using a placeholder (). The actual credential is resolved, decrypted, and injected at request time — inside the MCP server process — and never returned to the model's context.
The agent sees in tool inputs and outputs. The decrypted token value exists only transiently during HTTP request execution or MCP tool forwarding, and is never logged or included in responses.
1. Store a secret — The MCP client (e.g., Claude Code) presents a secure input form via MCP elicitation. The user enters the credential directly. The value is encrypted with AES-256-GCM and stored in MongoDB. The LLM never sees it.
2. Use a secret in HTTP requests — The agent calls httprequest with placeholders like Authorization: Bearer . secure-actions decrypts and injects the credential at request time.
3. Use a secret to call other MCP tools — The agent calls mcptoolrequest to invoke any MCP server, with secrets injected into the spawned process environment. This lets you securely use tools like @modelcontextprotocol/s
From the project README.
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