Ask Fugu (second opinion)

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

No annotations given, so description handles all burden. Discloses that Fugu is a separate LLM, cannot see repo/files/conversation, and calls can be slow (tens of seconds). Does not mention read-only/destructive but it's implied as a LLM call. Adequate for safe use.

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?

Two well-structured paragraphs, front-loaded with purpose and key constraints. Every sentence adds value; no fluff. Efficiently conveys necessary information.

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?

Despite no output schema and no annotations, the description covers purpose, usage guidelines, parameter requirements, behavioral traits (slowness, self-contained), and limitations. Complete for an agent to select and invoke correctly.

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 coverage is 100% with descriptive parameter definitions. Description adds minor context (e.g., 'Include all context Fugu needs') but largely overlaps with schema. At baseline of 3 for high coverage; no significant value beyond 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?

Title and description clearly state it's for a second opinion from a separate LLM. Specifies exact use cases (hard self-contained problems, tricky algorithms, design trade-offs) and distinguishes from sibling by explicitly stating what it is not for (interactive repo work, commands, etc.).

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?

Explicitly lists when to use (hard problems, gut-check) and when not (iterative repo work, multi-step edits). Provides context that prompt must be self-contained, and advises doing other tasks yourself. No ambiguity.

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