Aedifion MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the action ('grant or revoke') but does not specify permissions required, side effects (e.g., impact on AI features), rate limits, or response behavior. The output schema exists but is not described, leaving gaps in understanding the tool's effects.

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 front-loaded with the purpose in the first sentence, followed by a concise 'Args' section. Every sentence earns its place with no wasted words, making it easy to scan and understand quickly.

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 tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is minimally adequate. It covers the purpose and parameters but misses behavioral context and usage guidelines. The output schema helps, but the description does not reference it, leaving completeness gaps for a mutation tool.

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 0%, but the description compensates by explaining both parameters: 'project_id: The project's numeric ID' and 'consent: True to grant, False to revoke.' This adds clear meaning beyond the schema's basic types. However, it lacks details like ID format or consent implications, keeping it from a perfect score.

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: 'Grant or revoke consent for the AI Assistant on a project.' It specifies the verb ('grant or revoke'), resource ('consent for the AI Assistant'), and scope ('on a project'). However, it does not explicitly differentiate from sibling tools like 'ai_chat' or 'ai_get_threads', which are related to AI interactions but serve different purposes.

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 prerequisites (e.g., needing project access), exclusions (e.g., not for general AI settings), or comparisons to siblings like 'ai_chat' or 'create_user'. Without this, users must infer usage from the purpose alone.

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