Multi-MCP agent system integrating knowledge base retrieval, code execution, and AWS documentation with Strands agent framework
Three tools with minimal schema documentation and weak descriptions. Tool names lack action verbs (retrieve, repl_coder, repl_drawer are vague). Input schemas present but parameters lack type constraints and validation guidance. Descriptions are generic and do not explain when/why to use each tool or what happens on error. No output schemas documented. No error handling guidance. No parameter constraints (enums, ranges, formats). Security risk: repl_coder executes arbitrary Python code with no sandboxing or permission checks visible.
Use this to execute python code and do math. If you want to see the output of a value, you should print it out with `print(...)`. This is visible to the user.
Execute a Python script for draw a graph. Since Python runtime cannot use external APIs, necessary data must be included in the code. The graph should use English exclusively for all textual elements. Do not save pictures locally bacause the runtime does not have filesystem. When a comparison is made, all arrays must be of the same length.
Query the keyword using RAG based on the knowledge base.
Tool names lack action verbs. 'retrieve', 'repl_coder', 'repl_drawer' do not clearly signal intent. Should be 'search_knowledge_base', 'execute_python_code', 'generate_graph'.
No output schemas documented. LLMs cannot plan downstream calls or extract structured data. Each tool must declare what fields it returns.
repl_coder executes arbitrary Python code with no visible sandboxing, permission checks, or rate limits. Critical security risk: agent can access filesystem, network, or system resources.
Parameter descriptions are minimal. 'the keyword to query' and 'the Python code was written in English' lack context on format, constraints, or error recovery.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 40 | <=2025-11-25 | v2 |
No error handling guidance. Tools do not explain what to do if a query fails, code crashes, or graph rendering fails. LLM has no recovery path.