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remember_server_finding

Save and recall server-specific findings like misconfigured crons, hidden dependencies, or port blocks not visible in fresh probes. Automatically inject relevant findings into future context based on confidence.

Instructions

Persist a hard-won, non-obvious discovery (quirk, gotcha, root-cause, constraint) about an instance that is NOT visible in a fresh probe — e.g. a misconfigured cron, a hidden dependency, a port blocked by an upstream policy, a bug triggered only under load. Saved locally immediately and queued for end-to-end encrypted sync. The title is the searchable recall key — keep it short and specific. Returns {finding_id, instance_id, title, auto_inject, superseded, secret_warning}. auto_inject=true means the title will be surfaced automatically in future context (confidence >= threshold); false = recall-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesInstance ID, name, or custom-server name.
titleYesShort, searchable title for this finding (≤200 chars). This is the primary recall key — make it specific.
bodyYesFull finding text, evidence, and context (≤8000 chars).
tagsNoOptional tags for filtering (max 12, lowercased).
confidenceNoConfidence score 0.0–1.0. Values >= threshold (default 0.6) cause the title to be auto-injected into future context; lower values are recall-only.
supersede_idNoID of an existing finding this corrects or replaces. The old finding is marked superseded; pass the finding_id returned by a previous remember_server_finding call.
Behavior4/5

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

With no annotations, the description carries full burden. It discloses persistence ('Saved locally immediately and queued for end-to-end encrypted sync'), behavior of supersede_id, and auto-injection mechanism. It does not mention rate limits or auth needs, but covers core behavior adequately.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two focused sentences front-load the purpose and key details. No wasted words; every sentence adds value. Returns list of fields succinctly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 6 params and no output schema, the description covers purpose, parameters, return values, and behavior. It is complete enough for an AI to select and use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds context beyond schema: e.g., explains title as recall key, confidence threshold for auto-injection, and supersede_id behavior. Provides additional meaning for usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Persist') and resource ('hard-won, non-obvious discovery') and clearly distinguishes from sibling tools like recall_server_findings (retrieval) and build_server_memory (different purpose).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance with examples (misconfigured cron, hidden dependency) and explains auto-injection based on confidence. It does not explicitly list when not to use or compare with alternatives, but the context is clear.

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

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