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Capture and index new knowledge discovered during conversations for immediate search and retrieval within Project Tessera's memory system.

Instructions

Auto-learn: save new knowledge and immediately index it for search. Use this to capture insights, patterns, or facts discovered during conversation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYes
tagsNo
sourceNoauto-learn

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions that knowledge is 'immediately indexed for search', which is useful behavioral context. However, it doesn't address important aspects like whether this is a write operation (implied but not stated), what permissions are needed, whether there are rate limits, what happens on duplicate content, or what the indexing process entails. For a tool that presumably modifies persistent storage, this is inadequate.

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 perfectly concise with two sentences that each earn their place. The first sentence states the core functionality, and the second provides usage context. There's zero wasted language, and the information is front-loaded appropriately.

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?

The tool has an output schema (which reduces the need to describe return values), no annotations, and relatively simple parameters. The description covers the basic purpose and usage context adequately but lacks important behavioral details for a write operation and provides no parameter guidance. Given the output schema exists, the description is minimally complete but could be significantly improved.

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 0%, so the schema provides no parameter documentation. The description doesn't mention any parameters at all, failing to explain what 'content', 'tags', or 'source' should contain or their significance. However, with only 3 parameters and one required, the baseline is 3 since the tool is relatively simple despite the lack of parameter guidance in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('save new knowledge', 'index it for search') and identifies the resource ('knowledge'). It distinguishes from siblings by focusing on immediate indexing of discovered insights, unlike tools like 'remember' or 'list_memories'. However, it doesn't explicitly contrast with all similar tools like 'import_memories' or 'ingest_documents'.

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 for when to use the tool ('to capture insights, patterns, or facts discovered during conversation'), which helps differentiate it from bulk import or search tools. It doesn't explicitly state when NOT to use it or name specific alternatives, but the context is sufficiently clear for typical usage scenarios.

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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