research

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 mentions 'AI-powered research' and 'project context' but doesn't describe what the tool actually does behaviorally: how it performs research, what sources it uses, whether it makes network calls, what the output format looks like, or any limitations/constraints. For a complex 10-parameter tool with no annotations, this is insufficient transparency.

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 states the core functionality without unnecessary words. It's appropriately sized for a tool description and front-loads the essential information. Every word earns its place in conveying the tool's purpose.

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 (10 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what 'research' means in this context, what kind of results to expect, how the AI-powered aspect works, or how project context integrates. For a tool that presumably returns research findings, the lack of output schema means the description should at least hint at return values, which it doesn't.

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 description coverage is 100%, so the schema already documents all 10 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema - it doesn't explain how parameters like 'filePaths', 'taskIds', or 'saveTo' relate to the research process. With complete schema coverage, the baseline is 3 even without additional param semantics in the description.

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 'Perform AI-powered research queries with project context', which specifies the action (perform research), method (AI-powered), and scope (with project context). It distinguishes from siblings like 'analyze_project_complexity' or 'generate' by focusing on research queries rather than analysis or generation tasks. However, it doesn't explicitly differentiate from all possible research-like siblings, keeping it at 4 rather than 5.

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 doesn't mention when this research tool is appropriate compared to other tools like 'analyze_project_complexity' for analysis or 'generate' for content creation. There's no indication of prerequisites, limitations, or typical use cases beyond the vague 'with project context'.

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