This MCP server has a critical architectural flaw: it exposes a single tool 'dynamic_tools_from_flask' that acts as a meta-proxy, dynamically loading tool definitions from a Flask endpoint at runtime. The tool definition itself is NOT directly visible in the TypeScript source, it is inferred to exist based on code that fetches '/openai/function_definitions' from an external Flask service. Per HARD SCORING RULE: 'If you cannot see the actual tool definition in the source (only inferred): cap that tool's overall at 50.' The source code provided (http-server.ts) is truncated mid-import statement, preventing verification of actual tool schema registration, parameter definitions, and output documentation. The description provided in the evaluation metadata is generic and does not meet the pattern:tool-description standard (should be 10-1024 chars, actionable, and specific). No parameter schemas are visible. No input validation or error handling patterns are documented in the provided code. The tool operates as a write-capable proxy (Risk: WRITE) with no visible permission gates, audit trails, or secret injection patterns, violating pattern:secret-injection and pattern:permission-gate.

• Do not expose a meta-proxy tool. Instead, statically register the actual GPG tools (e.g., 'encrypt_message', 'sign_document', 'verify_signature') with full schemas in the MCP server at build time. Fetch the Flask function definitions during server startup and generate proper MCP tool registrations with complete descriptions, parameter schemas, and output documentation.
• Rename tool from 'dynamic_tools_from_flask' to an action-verb name like 'execute_gpg_operation' or individual tools like 'encrypt_text', 'sign_data', 'verify_signature'. Each should start with a verb and have a clear, specific name.
• For each actual GPG tool, write a 50-200 character description explaining WHAT it does (e.g., 'Encrypts a message using GPG public key encryption'), WHEN to use it ('Call when you need to send confidential data to a recipient with a known GPG key'), and any prerequisites ('Requires the recipient's public key to be in the keyring').
• Define complete input schemas for every parameter: type (string, number, boolean, object, array), format (email, uri, date-time, etc.), enum values where applicable, min/max/pattern constraints, and a description for each parameter. Example: { 'type': 'string', 'description': 'The GPG key ID or email address of the recipient', 'minLength': 8, 'pattern': '^[A-F0-9]{8,}$|.+@.+' }.
• Document output schemas. Specify what fields are returned (e.g., 'encrypted_message', 'encryption_algorithm', 'key_id_used'), their types, and formats. Include example output.
• Add permission gates: verify the calling agent has permission to perform GPG operations before proxying to Flask. Use pattern:permission-gate, declare required permissions (e.g., 'write:gpg', 'read:keyring') and validate at request time.