Static source inference · medium confidence · detected: Logging
Deprecated protocol patterns detected
Summary
This server exhibits major definition quality gaps across all tools. Tool names are acceptable but descriptions lack depth and parameter guidance. Input schemas are present but incomplete, parameters lack descriptions in several cases. Output schemas are undocumented. The execute_code and run_agent tools present significant safety risks without proper error recovery guidance. Overall, this follows fewer than half of the 54 agentic patterns and would not pass production code review.
Tools (3)
echoread onlysource verified52/100
Echo a message back to the caller.
execute_codeirreversiblesource verified57/100
Execute Python code inside DSpy's sandboxed interpreter.
This is a low-level tool that runs exactly what you give it.
For agentic problem solving, use 'run_agent' instead.
run_agentirreversiblesource verified55/100
Run an autonomous coding agent to solve a task.
The agent can:
1. Access upstream MCP tools (filesystem, memory, etc.)
2. Write and execute Python code to solve the problem
3. iterate if errors occur
Args:
task: The natural language task description (e.g., "Read /tmp/data.txt and summarize it")
execute_code and run_agent lack error recovery guidance. No description of what happens on failure, how to retry, or what errors are retryable vs fatal. Per pattern:recovery-guide, error responses must tell the LLM what to do next.
No output schemas documented for any tool. LLMs cannot plan downstream calls or extract data when they don't know what fields to expect. Per pattern:tool, all tools must document their return type structure.
execute_code and run_agent are high-risk tools (IRREVERSIBLE) with no confirmation, dry-run, or confirmation-request pattern. Agents can execute arbitrary code or spawn autonomous agents without guardrails. Per pattern:confirmation-request, irreversible operations should support confirmation.
execute_coderun_agent
Recommendations
Document complete output schemas for each tool. For execute_code: return {success: bool, output: str, error: str?, execution_time_ms: int}. For run_agent: return {success: bool, result: str, steps_taken: int, tool_calls: []}. For echo: return {message: str}.
Add error recovery guidance to execute_code and run_agent. E.g. 'On timeout, increase timeout param or break code into smaller steps. On SyntaxError, check Python version (requires 3.11+). On PermissionError, check sandboxed restrictions.'
Add confirmation-request pattern: before executing arbitrary code via execute_code, show the user/agent a preview of the code and require explicit confirmation. Alternatively, offer an explain_code tool that returns a summary without executing.
Rewrite tool descriptions to follow 50 - 200 character target and answer: What does it do? When use it? What risks? Example: execute_code: 'Execute Python code in a sandboxed DSpy interpreter. Use this only after writing and testing code elsewhere. Returns output, errors, and execution time. WARNING: arbitrary code execution, only call with trusted, reviewed code. Timeout 30s by default.'
Document the 'variables' parameter in execute_code with an example: 'variables (optional): Dict of Python variables to inject into execution context. E.g. {"data": [1,2,3], "multiplier": 2} makes data and multiplier available in the code.'
Add dependency hints. For run_agent: 'For filesystem tasks, the agent can access tools like read_file and write_file if available upstream. Provide task in natural language; agent will iterate and refine via code execution.'
Spec posture evidence
Inferred effective spec: <=2025-11-25.
Relies on Logging (deprecated) - log to stderr or use OpenTelemetry
run_agent description is verbose and lists numbered capabilities instead of stating WHAT (solves coding tasks), WHEN (when you need code execution), and WHAT IT RETURNS.
execute_code 'timeout' parameter has a default of 30 but no description of units (seconds assumed), no explanation of what happens on timeout (does it raise an exception? return partial results?), and no guidance on when to increase it.
run_agent and execute_code perform computation but no documentation of resource limits, cost implications, or expected latency. Agents need this context to decide whether to call these tools or find alternatives.
execute_coderun_agent
Specify resource limits in descriptions. E.g. 'Code execution is sandboxed and limited to 2GB RAM, 60s CPU time, and no network access.' This prevents agents from attempting resource-intensive computations.
Add actionable error messages in implementation. Instead of generic exceptions, catch common failures: 'Timeout after 30s. Code is likely in an infinite loop or performing expensive operations. Try breaking it into smaller chunks.' 'SyntaxError on line 5: missing colon after if statement.'
For run_agent, document what 'upstream MCP tools' means and whether the agent has access to the same tools the parent client does. Clarify: 'The agent inherits the client's tool set and can call any registered MCP tool recursively.'
Add enums or constraints to timeout parameters. E.g. 'timeout (int, 1 - 300): Execution timeout in seconds. Default 30. Longer tasks (data processing, ML training) may need 120 - 300.'
Document idempotency. E.g. 'Calling execute_code twice with identical code may produce different output if the code has side effects (writing files, API calls). If idempotency is required, wrap operations in transactions or use run_agent for supervised execution.'
Add tool composition hints. E.g. 'To solve a multi-step task, use run_agent (agent orchestrates steps). To execute a single code snippet, use execute_code (faster, no agent overhead).'
Return per-item success/failure for batch-like scenarios if run_agent executes multiple steps. E.g. 'steps: [{step: 1, action: "read file", success: true, result: "..."}, ...]' lets the LLM see where failures occurred without re-running successful steps.