ai_agent_research

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It states the tool returns synthesized research with citations, indicating a read-like behavior, and the depth parameter implies varying effort. However, it does not mention authentication needs, rate limits, statefulness, or any potential side effects beyond the implied non-destructive nature of research.

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 highly concise: two well-crafted sentences. The first sentence front-loads the core purpose and scope, listing specific covered areas. The second sentence describes the output format. There is no redundant or gratuitous information; every word earns its place.

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 low complexity (2 simple parameters, full schema coverage, no output schema), the description covers essentials: purpose, scope, output format, and example topics. It does not detail the exact structure of the synthesized research beyond 'with citations', but this is sufficient for an AI agent to infer reasonable usage. The lack of annotations is compensated by the clear research nature. Some minor missing details (e.g., maximum results, time constraints) prevent a 5.

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 both parameters described. The description adds value by giving concrete examples of valid topics (e.g., 'agent monetization', 'MCP servers'), which enhances the schema's generic 'Research topic' description. However, the depth parameter gains no new meaning beyond what the enum values already provide. Overall, the description modestly supplements the schema but does not significantly elevate understanding.

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?

Description clearly states the tool researches the AI agent ecosystem, listing specific frameworks like LangGraph, CrewAI, AutoGen, MCP, and ACP. It uses a specific verb-resource combination ('Research the AI agent ecosystem') and naturally distinguishes itself from sibling tools (defi_analysis and market_research) by focusing on AI agents, leaving no ambiguity about its domain.

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 when to use this tool through its explicit focus on AI agent topics and examples, but does not provide explicit when-not-to-use guidance or mention alternatives. It clearly sets context for research on frameworks, tools, monetization, and best practices, which differentiates it from general market research or DeFi analysis siblings.

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