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Gemini-powered MCP (Model Context Protocol) Client - integrates Gemini AI capabilities with MCP servers
This server exhibits significant quality gaps across naming, descriptions, schemas, and composition. While tool names generally follow verb conventions (echo, add_numbers, chat_with_gemini), many descriptions are vague or incomplete. Several tools lack detailed parameter descriptions. Most critically, the server suffers from tool duplication (add_numbers appears twice, echo appears twice with different names) and unclear responsibility boundaries. Schema quality varies: some tools have properly typed parameters with descriptions (chat_with_gemini, gemini_model_comparison), but others lack depth. Error handling is not evident in the provided code. The FastMCP framework handles basic schema inference, but critical details like return types, pagination support, and recovery guidance are absent across most tools. No evidence of idempotency, output field documentation, or chaining-ID inclusion in responses.
Add two numbers together.
Add two numbers together.
Chat with Gemini AI model. Args: prompt: The message to send to Gemini model: The Gemini model to use (default: gemini-2.0-flash-lite)
Echo back the provided message.
Echo back a message.
Compare responses from multiple Gemini models.
Get the current time.
Tool duplication: 'add_numbers' defined twice (examples/echo_server.py and servers/simple_test_server.py) and 'echo' defined twice (echo and echo_message in different files). This violates single-responsibility and confuses LLMs about which variant to call.
Parameter descriptions lack detail. 'model' in chat_with_gemini states 'The Gemini model to use (default: gemini-2.0-flash-lite)' but does not explain valid model names, constraints, or when to use specific models. Baseline: 72 chars average param description; most here ~30-50 chars.
Output schemas not documented. Tools like list_gemini_models and gemini_model_comparison return data but no schema documentation visible in code. LLMs cannot plan downstream operations or extract required fields (e.g., model IDs for chaining).
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 47 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 32 | - | v1 |
Get information about this server.
List available Gemini models.
Set the default Gemini model to use.
No error handling guidance. No evidence of try-catch, validation, or recovery messages in source. If chat_with_gemini fails (API down, auth error, invalid model), the agent gets no actionable next step.
Tool descriptions too vague. 'Get information about this server' (get_server_info) does not explain what fields are returned or when to call it. 'List available Gemini models' does not state if results are paginated or how many models exist.
No pagination metadata. Tools returning lists (list_gemini_models, gemini_model_comparison) have no visible limit, offset, page, or cursor parameters. Large result sets will bloat context and break agent reasoning.
No input validation rules visible. Parameters like 'prompt' (string) and 'model' (string) lack length constraints, format patterns, or enum definitions. LLMs may pass invalid values (e.g., empty prompt, nonexistent model) with no pre-call validation.
Unclear tool semantics. 'set_gemini_model' is marked WRITE but has no return value documented, does it return the old model, the new model, or a success confirmation? Agents cannot verify state changes.