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remember_server_finding

Persist hidden server discoveries (quirks, gotchas, root causes) that aren't visible in normal probes. Save locally, sync encrypted, and auto-inject into future context when confidence is high.

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 Returns {finding_id, instance_id, title, auto_inject, superseded, secret_warning}.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
bodyYesFull finding text, evidence, and context (≤8000 chars).
tagsNoOptional tags for filtering (max 12, lowercased).
titleYesShort, searchable title for this finding (≤200 chars). This is the primary recall key — make it specific.
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.
instance_idYesInstance ID, name, or custom-server name.
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 The old finding is marked superseded; pass the finding_id returned by a previous remember_server_finding call.
Behavior3/5

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

No annotations provided, so description carries full burden. It explains immediate local save, queued sync, return fields, and auto-injection based on confidence. However, it omits error behavior, idempotency, rate limits, and does not explain 'secret_warning' field.

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

Conciseness4/5

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

Description is well-structured, front-loading purpose then detailing behavior and return values. Slightly verbose but every sentence adds value; could be tightened without losing clarity.

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

Completeness3/5

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

Given 6 parameters, no output schema, and no nested objects, the description adequately covers purpose and key behaviors. Missing details on failure modes and secret_warning; but for an AI agent, the information is sufficient to use the tool correctly in most scenarios.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. Description adds useful extra context for title (searchable recall key), confidence (threshold for auto-inject), and supersede_id (marks old finding superseded). No significant gaps or contradictions.

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?

Description clearly states the tool persists non-obvious, hard-won discoveries about instances, with concrete examples (misconfigured cron, hidden dependency). It distinguishes from sibling tools like list_server_memories and recall_server_findings which are retrieval-focused.

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?

Provides context on when to use (for non-obvious findings not visible in fresh probes), describes auto-inject behavior, and reminds to keep title specific. Lacks explicit when-not-to-use or alternatives, but the context is sufficient for an agent to infer use cases.

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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