Gemini MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions behavioral traits like 'weighted voting, confidence scoring, and conflict resolution,' but does not disclose critical details such as how models are accessed, potential rate limits, authentication needs, or what happens in case of conflicts. This leaves significant gaps in understanding the tool's operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads key features without unnecessary words. Every phrase ('advanced multi-model consensus,' 'weighted voting,' etc.) adds value, making it appropriately sized and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity implied by 'advanced' features and no output schema, the description is incomplete. It lacks details on return values (e.g., consensus result format, confidence scores), error handling, or prerequisites. Without annotations or output schema, this leaves the agent with insufficient context for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters (question, models, min_confidence, weight_strategy). The description adds no additional meaning beyond the schema, such as explaining the impact of different weight strategies or confidence thresholds. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as performing 'advanced multi-model consensus' with specific features like weighted voting, confidence scoring, and conflict resolution. It uses a specific verb ('consensus') and resource ('multi-model'), but does not explicitly differentiate from sibling tools like 'mcp__gemini__thinkdeep_enhanced' or 'mcp__gemini__chat_plus', which might have overlapping AI-related functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It lacks context such as specific scenarios (e.g., complex decision-making, high-stakes queries) or comparisons to sibling tools, leaving the agent to infer usage based on the name and features alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.