hebbrix_ask

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

With no annotations, the description carries full burden. It discloses key behaviors: it searches, synthesizes with an LLM, cites memory ids, conditionally returns graph/profile, and falls back to raw search hits if the reasoning backend is unavailable. It does not explicitly state read-only status or potential latency/cost implications.

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 concise but information-dense, with the primary purpose front-loaded. It efficiently covers motivation, example, return format, and fallback behavior in a few sentences without fluff.

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 having an output schema, the description usefully elaborates on return design and fallback behavior. It covers the composite nature and optional outputs, but omits collection_id semantics and explicit read-only clarification, leaving small gaps for a complex tool.

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 0%, so the description must explain parameters. It implicitly explains include_graph ('graph (when include_graph)') but never explains collection_id, which is a non-obvious optional parameter. The question parameter is self-explanatory, but the coverage gap hurts.

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 opens with a specific verb and resource: 'Answer a natural-language question from memory in ONE call.' It clearly distinguishes itself from sibling tools by stating it replaces orchestrating hebbrix_search + hebbrix_graph_query + profile, and provides a concrete example question.

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?

It explicitly says to use for natural-language synthesis questions and mentions the orchestration it avoids, naming alternatives. However, it does not give explicit when-not-to-use cases (e.g., simple lookups) or direct comparisons to other siblings.

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