An MCP server that reads CSV data and integrates with Google Gemini API for data analysis
This server has a single tool with a minimalist definition that lacks rigor across multiple dimensions. The tool name is appropriately verb-prefixed ('get_'), but the description is generic and under-detailed. Most critically, the input schema is completely empty ({}), which is a hard FAIL per the rubric. There is no documented output schema, no parameter descriptions (none exist), no error handling guidance, and no input validation. The tool reads a hardcoded file path ('sales_data.csv'), returning a string representation of a CSV, appropriate for a demo, but the lack of structured output, pagination, and error recovery patterns makes this unsuitable for production use.
Reads the content of sales_data.csv and returns it as a string.
Empty input schema: tool accepts no parameters but schema field is {} instead of being omitted or explicitly documented as having no inputs
No output schema documented. Callers cannot know the structure of the returned string (it is a pandas DataFrame.to_string() representation). This prevents LLMs from planning downstream operations or extracting structured data.
Tool description is generic (23 chars) and lacks actionable context. It does not explain WHEN to use this tool, what the CSV structure is, what columns are available, or any prerequisites.
Hardcoded file path 'sales_data.csv' is not parameterizable. This prevents agents from reading different CSV files or checking availability before calling the tool.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 33 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 0 | - | v1 |
Error handling returns a plain string 'Error: sales_data.csv not found.' instead of a structured error response with guidance. LLM cannot distinguish error from success or know what to do next.
No input validation. Tool does not check whether sales_data.csv exists or is readable before attempting to read it, leading to potential runtime failures.
Unstructured output (string representation of CSV). Should return structured JSON with column names, data types, and row-level objects to support agent reasoning and field extraction.
No pagination or result limiting. If the CSV is large (thousands of rows), the entire dataset is returned as a single string, potentially exhausting context window.