m365_chat

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a Q&A tool that synthesizes answers from multiple sources, supports multi-turn conversations via conversation_id, and includes web search grounding with sensitivity considerations. However, it lacks details on rate limits, authentication needs, or error handling, which are important for a tool interacting with enterprise data.

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 core purpose, followed by bulleted lists for usage guidelines, making it highly scannable and efficient. Every sentence earns its place by providing essential information without redundancy, such as distinguishing from siblings and explaining parameter implications in a structured way.

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 complexity (interactive Q&A with enterprise data), no annotations, and no output schema, the description does a good job of covering purpose, usage, and behavioral context. However, it could be more complete by mentioning potential limitations (e.g., response format, error cases) or prerequisites, which would help the agent handle edge cases better.

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 well. The description adds value by explaining the tool's purpose and usage context, which helps interpret the parameters (e.g., 'message' for questions, 'conversation_id' for follow-ups, 'web_search' for grounding). It doesn't add specific parameter details beyond the schema, but provides meaningful context that compensates for the lack of output schema.

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: 'Quick Q&A with M365 Copilot' that 'Gets synthesized answers from email, calendar, Teams, SharePoint, OneDrive' and 'Supports multi-turn conversation.' It specifies the exact resources (email, calendar, Teams, SharePoint, OneDrive) and distinguishes it from sibling tools like m365_retrieve by emphasizing synthesized answers versus raw source text.

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 explicit guidance on when to use this tool ('Use for: - People questions, - Meeting schedules and availability, - Email summaries, - Enterprise facts and policies') and when not to use it ('Use m365_retrieve instead when: - You need raw source text, - You want to control reasoning, - You need cross-document analysis'). This includes clear alternatives and exclusions, helping the agent choose correctly among siblings.

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