brain_recall

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

Annotations already mark it readOnly, idempotent, and non-destructive, and the description adds genuinely new behavioral detail: results are 'ranked deterministically' and 'token-capped to stay within 250 tokens', plus the current file/package scoping behavior. It also names the memory sources (git history and AI sessions), which the schema does not convey.

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 sentences, front-loaded with the primary action and then adding return contents, scoping, and output constraints. Every sentence adds a distinct piece of information with no wasted words.

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 read-only search tool with no output schema, the description is self-sufficient: it states the source corpus, optional scope filter, result ranking determinism, and the 250-token cap. Combined with fully documented parameters, an agent has everything needed to decide whether and how to call it.

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%, so the schema already documents all six parameters, including defaults and enums. The description adds context about search scope and result types but no per-parameter semantics that the schema doesn't already provide; the baseline 3 applies.

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?

Description opens with 'Semantically search your local codebase memory', a specific verb-resource pair, and enumerates the content types returned: lessons, bug fixes, architecture decisions, and conventions. This clearly distinguishes it from sibling write/delete/lifecycle tools like brain_learn, brain_forget, and brain_prune.

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 gives clear context—use when you need to retrieve prior lessons or decisions from git history and AI sessions, optionally scoped to the current file/package—without explicitly naming exclusions or alternatives. It lacks an explicit 'when not to use' or a comparison to brain_trace, so it stops short of a 5.

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