n2_core_read

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

No annotations are supplied, so the description carries the full burden. It hints at lifecycle behavior (auto-loaded at boot) but says nothing about failure modes when the agent is unknown, whether an empty result is possible, or what the response shape looks like.

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 short sentences, front-loaded with the action and resource, with no filler. The second sentence is marginally tangential but still earns its place by explaining why core memory matters.

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?

For a one-parameter read tool with no annotations and no output schema, the description covers the essential what and a bit of lifecycle context. It leaves gaps on error behavior and return contents that an agent would want before calling.

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% and the single `agent` parameter is already documented as "Agent name." The description's phrase "agent-specific" reinforces scoping but adds no new detail such as format, case sensitivity, or default behavior.

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 ("Read") and resource ("agent-specific core memory"), which is enough to separate it from n2_core_write and from more general readers like n2_brain_read or n2_context_search. It stops short of explicitly naming a sibling or contrast, so differentiation is inferable rather than stated.

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 note that core memory is always loaded at boot for context injection implicitly tells an agent it may not need to call this manually, but there is no explicit when-to-use, when-not-to-use, or alternative named. The agent must infer the decision from context.

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