search_messages

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 API and KQL syntax but lacks critical details like whether this is a read-only operation (implied by 'search'), potential rate limits, authentication requirements, or what the output format looks like (e.g., pagination, error handling). For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 efficiently structured in two sentences: the first states the purpose and scope, and the second adds key capabilities. Every sentence earns its place by providing essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.

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 (4 parameters, 100% schema coverage, no output schema, no annotations), the description is partially complete. It covers purpose and basic usage but lacks behavioral details (e.g., output format, error cases) and deeper contextual guidance. Without annotations or an output schema, the agent might struggle to fully understand how to interpret results or handle edge cases, leaving room for improvement.

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%, with each parameter well-documented in the schema (e.g., query supports KQL syntax, scope has enum values, limit has min/max). The description adds minimal value beyond the schema by mentioning 'advanced KQL syntax for filtering by sender, mentions, attachments, and more,' which slightly elaborates on the query parameter but doesn't provide new syntax or format details. Baseline 3 is appropriate when 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 specific action ('Search for messages') and resource ('across all Microsoft Teams channels and chats'), distinguishing it from siblings like get_channel_messages or get_chat_messages that retrieve messages from specific sources without search capabilities. It also mentions the underlying API (Microsoft Search API) for technical context.

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 implies usage context by stating it searches 'across all Microsoft Teams channels and chats' and supports 'advanced KQL syntax for filtering,' suggesting it should be used for comprehensive, filtered searches rather than retrieving specific message sets like sibling tools. However, it doesn't explicitly state when not to use it or name alternatives, such as get_recent_messages for non-search retrieval.

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