Tool description is trivial ('Say hello'), under 20 characters and lacks WHEN to use it, WHAT it returns, or any context for LLM selection. Per pattern:tool-description, descriptions must be 10 - 1024 chars and answer what, when, and return value.

Parameter 'name' has no description in the tool definition. LLMs cannot infer that it expects a person's name vs a project name vs a filename. Per pattern:tool-description, every input parameter must have a non-empty description.

No output schema documented. The response returns text 'Hello, {name}!' but there is no schema definition specifying return type, fields, or structure. Per pattern:tool, LLMs need documented output schemas to plan downstream calls and extract data.

Tool name 'hello' does not follow verb_noun convention. 'greet_user', 'send_greeting', or 'generate_greeting' would be clearer action verbs. Per pattern:tool, names starting with verbs (get_, create_, search_) help LLMs parse intent from the name alone.

No error handling guidance. If an invalid 'name' is passed (e.g. null, empty string, non-string), there is no error response or recovery hint. Per pattern:recovery-guide, errors must tell the LLM what to do next.

Generated templates not visible in source. The mcp-toolkit scaffolding uses Jinja2 templates (python_server.py.j2, manifest.json.j2) but the template content is not provided. Cannot verify quality of generated server boilerplate.