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list_remembered

View cached context entries to decide whether to recall existing knowledge or refresh it for your project.

Instructions

List all cached context entries for this project. Shows what knowledge the AI assistant has already cached, so you can decide whether to recall existing context or refresh it. Returns: key, category, size, TTL remaining, and a content preview.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesUUID of the cache instance
categoryNoFilter by category (default: all)
limitNoMax entries to return (default: 50)
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well by disclosing behavioral traits: it lists cached entries, returns specific fields (key, category, size, TTL remaining, content preview), and implies a read-only operation without destructive effects. It does not mention rate limits, authentication needs, or pagination details, but covers core functionality 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?

The description is appropriately sized and front-loaded: the first sentence states the purpose, the second explains the use case, and the third details the return values. Every sentence adds value without redundancy, making it efficient and well-structured.

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?

Given no annotations and no output schema, the description compensates well by explaining the tool's purpose, usage, and return fields. It covers the essentials for a read operation with filtering parameters, though it could benefit from mentioning error cases or example outputs to be fully complete.

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 the schema already documents all parameters (instance_id, category, limit) with descriptions and enums. The description does not add meaning beyond this, such as explaining parameter interactions or default behaviors beyond what's in the schema, meeting the baseline for high coverage.

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 verb ('List') and resource ('cached context entries for this project'), specifying it shows cached knowledge to help decide between recalling or refreshing context. It distinguishes from siblings like cache_get (retrieves specific entries) and cache_stats (provides statistics rather than detailed listings).

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 clear context: use it to see what knowledge is already cached for deciding whether to recall existing context or refresh it. However, it does not explicitly mention when not to use it or name specific alternatives among siblings, such as cache_keys (which might list keys without details) or recall_context (which retrieves content).

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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