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Cachly — AI Cognitive Brain

remember_context

Save important context information to cache for quick later recall, avoiding re-computation. Ideal for caching codebase overviews, file summaries, and analysis results.

Instructions

Save context information to the cache so you can recall it later without re-computing. Perfect for caching: codebase overviews, file summaries, project structure, frequently-accessed data, or "thinking" results like dependency analysis. The AI assistant can use this to avoid re-reading the entire codebase every time. Overwrites any existing value stored under the same key. Returns { key, stored_at, ttl } confirming the saved context. Example: remember_context("project overview", "This is a Next.js app with...") then later: recall_context("project overview"). Use recall_context to retrieve; use list_remembered to see all stored keys.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesUUID of the cache instance
keyYesDescriptive key like "project_overview", "auth_architecture", "file:src/index.ts"
contentYesThe context/summary/analysis to remember
categoryNoCategory for organization (default: custom)
ttlNoTime-to-live in seconds (default: 86400 = 24h, use 0 for no expiry)
Behavior4/5

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

With no annotations, the description discloses key behaviors: overwrites existing values, returns a confirmation object with key, stored_at, ttl. Missing details on error handling or access controls, but sufficient for a cache write operation.

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?

Concise single paragraph with purpose, use cases, behavior, and example. Slightly verbose but all information earns its place. Front-loaded with primary function.

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

Completeness4/5

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

Despite no output schema, the return format is described. Covers main function, overwrite, and companion tools. Lacks discussion of error cases but adequate for a simple cache write tool given context complexity.

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%, baseline 3. The description adds value by explaining the category and ttl defaults, and provides an example usage pattern that clarifies parameter semantics beyond the 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?

The description clearly states the tool saves context to cache for later recall, with specific examples like codebase overviews and file summaries. It distinguishes from siblings by mentioning retrieval via recall_context and listing via list_remembered.

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

Usage Guidelines5/5

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

Provides explicit when-to-use scenarios (caching context, avoiding re-computation) and names companion tools (recall_context, list_remembered) for retrieval and listing. The overwrite behavior is stated, giving clear usage boundaries.

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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