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

The description discloses many non-obvious behaviors beyond the annotations, such as deduplication on identical fact text, keyword merging versus replacement, updated_in_place confidence changes, refusal of text matching retired facts, decisions always appending, preferences never leaving the machine, and redaction of credentials. The annotations only indicate read/write/idempotency hints, so this behavioral detail is valuable and non-redundant.

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 dense but every clause earns its place: it front-loads the core action, then covers lifecycle, deduplication, security handling, and return values without repeating the schema. Given the tool's complexity and 10 parameters, the length is justified and the structure remains scannable.

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Despite having no output schema, the description enumerates return values (neuron_id, action, superseded count, near_duplicates, supersedes_unmatched, totals) and covers edge cases like retired text refusal, unmatched supersedes, and credential redaction. For a complex write tool with many siblings, this is a complete and self-sufficient description.

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. The description adds workflow-level meaning beyond individual parameter descriptions, such as how neuron_id skips name matching, how supersedes performs a combined replace operation, and how keywords/confidence behave on exact duplicates. It does not list every parameter, but it clarifies the interactions between them.

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 uses a specific verb ('store') with a clear resource: a fact, decision, pattern, preference, error, or debt on a topic. It explicitly differentiates from siblings by naming crbro_revise and crbro_forget for related but different lifecycle actions, so an agent can distinguish this tool from them immediately.

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 description explicitly tells the agent when to use this tool versus alternatives: use crbro_learn with supersedes to replace an old truth, use crbro_revise to retire without replacement, use crbro_forget to delete from disk, use crbro_secret for credentials, and call crbro_recall first because the neuron may already exist. This is clear, actionable routing guidance.

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