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recall_with_fallback

Search across memory types with automatic fallback from patterns to project facts, stopping when enough high-confidence results are retrieved.

Instructions

Recall with automatic fallback through memory types.

Tries searching in order: patterns -> project facts -> all types. Stops when min_results are found with medium+ confidence.

Use this when you're unsure which memory type contains the answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoRecall mode: 'precision', 'balanced', 'exploratory'
queryYesSearch query for semantic similarity
min_resultsNoMinimum results before trying next fallback

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
guidanceYes
memoriesYes
confidenceYes
gated_countYes
context_summaryNo
ranking_factorsYes
related_memoriesNo
formatted_contextNo
promotion_suggestionsNo
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It explains the fallback order (patterns -> project facts -> all types) and stopping condition (min_results with medium+ confidence). It does not mention side effects, but as a recall tool, it is likely read-only.

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?

Three concise sentences with no fluff. The description is front-loaded with the core functionality and ends with usage guidance.

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?

Given the presence of similar tools (recall, recall_by_tag) and the existence of an output schema, the description provides sufficient information for correct usage. It explains the fallback mechanism and when to use this tool.

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%, but the description adds value by specifying that the stopping condition is 'medium+ confidence', which is not in the schema. This adds meaning beyond the schema's parameter descriptions.

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 clearly states the tool's purpose: recall with automatic fallback through memory types in a specific order. It distinguishes itself from siblings by advising use when unsure which memory type contains the answer.

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 provides explicit when-to-use guidance: 'Use this when you're unsure which memory type contains the answer.' It does not explicitly list when not to use or name alternative tools, 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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