deep_research_synthesize

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

No annotations exist, and the description does not disclose behavioral traits such as whether the action is read-only, modifies state, has side effects, or requires specific permissions. The mention of 'routes through the platform's domain-agent dispatcher' is an implementation detail, not a behavioral guarantee. The agent has little insight into the action's cost or impact.

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 short (three sentences) and front-loaded with the core action. The routing information in the second sentence is somewhat redundant for an MCP tool, but not overly verbose. Nearly every sentence adds some value, though the routing detail could be trimmed if it's standard for all tools in this domain.

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 existence of an output schema, return value detail is not needed, but the description lacks context about the operation's behavior (e.g., is it long-running? Does it require previous research? What are the input constraints?). The two parameters are minimally described, leaving the agent to guess the proper use. Completeness is low.

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?

The schema has 0% coverage, so the description must add meaning. It describes 'message' as 'Free-text objective' and 'inputs' as 'Optional JSON string of structured inputs'. This adds basic context beyond the schema's names and defaults, but 'Free-text objective' is vague and does not specify expected content or format. 'inputs' could be more descriptive (e.g., expected structure).

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 identifies it as running the 'synthesize' action of the deep_research domain agent, which suggests combining information. However, it does not specify what the action produces (e.g., a summary, report, or insight), leaving the outcome ambiguous. The verb 'synthesize' is specific enough to distinguish from siblings like chat or query, but lacks concrete output description.

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?

No guidance is provided on when to use this tool versus alternatives like deep_research_chat or deep_research_research_query. The description only mentions routing details, which are generic to all domain agent tools. An agent would need to infer the usage context from the action name alone.

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