recall_associative

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

With no annotations, the description carries the full burden, and it delivers. It discloses the internal algorithm (seeding, propagation along citation/semantic/shared-entity edges) and the return format (associative ranking plus the plain-cosine baseline for comparison). This goes beyond a simple 'recall' and lets the agent predict behavior and output.

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 compact: a one-sentence overview, a one-sentence contrast with recall, a one-sentence return description, and a bulleted Args list. No filler; every sentence adds critical information, and the most important details are front-loaded.

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 moderate complexity (graph propagation, multiple edge types) and that an output schema exists, the description covers what the tool does, how it differs from the main sibling, and what it returns. It doesn't need to replicate output schema details, and no critical gaps are apparent.

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's Args section is essential. It defines all three parameters: query as 'Natural language query', project with scope semantics ('None = whole brain'), and limit as 'Number of results'. This adds meaning beyond the schema's type-only definitions.

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+resource: 'Spreading-activation recall over the memory graph (HippoRAG-style).' It immediately distinguishes itself from sibling `recall` by contrasting its mechanism (propagating activation along edges) with pure cosine top-k, making the tool's unique purpose clear.

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 states when to prefer this tool: 'Unlike recall (pure cosine top-k), this seeds... so strongly-connected memories surface even when not textually similar.' This gives the agent a clear decision rule between recall_associative and recall, and implies that recall is the alternative for straightforward similarity searches.

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