memory_reflect

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

With no annotations, the description carries the full burden and does well: it discloses persistence ('persists insights and audit state'), the internal mechanism (stored graph clusters or a similarity fallback), a hard input constraint (exact project required), and the returned fields. It omits any mention of LLM cost, latency, or permission requirements, which would matter for a tool that invokes an external model.

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?

Three dense sentences, front-loaded with the core action, then constraints, then returns, then routing to siblings. No filler, though the return-field enumeration makes it slightly list-heavy.

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?

There is no output schema, so the description usefully enumerates the return shape (newInsights, reinforced, clustersProcessed, clustersSkipped, usedFallback) and covers the prerequisite of an enabled LLM. Combined with a fully documented 2-parameter schema, an agent has enough to call it correctly, with only cost/authorization behavior unaddressed.

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%, so the baseline is 3, but the description adds real meaning beyond the schema's terse 'Filter by project' by stating the value must be an exact project and that '*' is rejected. maxClusters is left entirely to the schema (default 10, max 20), so it is not fully compensated.

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?

States a specific verb pair (synthesize/reinforce) over a specific resource (project concept clusters) via an LLM, and explicitly names two siblings (memory_insight_list, memory_patterns) it is not. An agent can distinguish this from related memory tools without opening any schema.

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?

Names alternatives and their conditions ('Use memory_insight_list to read existing insights or memory_patterns for deterministic recurring file/error patterns'), and gives a when-not constraint ('rejects "*" or noop-provider mode'). It stops short of stating the positive trigger for choosing synthesis over consolidation or crystallize, so it is clear but not exhaustive.

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