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Cachly — AI Cognitive Brain

smart_recall

Retrieve cached context by meaning using natural language queries, with fallback to exact key matching for reliability.

Instructions

Semantically search cached context using natural language. Instead of exact key matching, finds context by meaning. Example: smart_recall("how does authentication work") → returns cached auth architecture summary. Falls back to remember_context keys if no semantic match is found.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesUUID of the cache instance
queryYesNatural language query to find relevant cached context
thresholdNoSimilarity threshold 0-1 (default: 0.78)
Behavior3/5

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

With no annotations, the description discloses key behaviors: semantic search, fallback to exact keys, and threshold-based matching. However, it omits read-only nature, performance traits, and limitations like data freshness.

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 concise sentences plus an example; front-loaded with purpose, no wasted words.

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 the complexity and missing output schema, the description lacks details on return format and structure, which would help agents parse results. It partially completes the picture but leaves gaps.

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 covers all 3 parameters with descriptions; the tool description adds contextual meaning (e.g., query as natural language, threshold default 0.78) but does not significantly extend beyond schema.

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?

Clearly states 'semantically search cached context using natural language' and distinguishes from exact key matching, with an example that illustrates the semantic retrieval capability.

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?

Implicitly guides when to use by contrasting with exact key matching and describing fallback behavior, but does not explicitly state when not to use or list alternatives among siblings.

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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