Astria-Index

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

With no annotations provided, the description carries full behavioral disclosure: it explains persistence ('persists forever'), retrieval mechanics ('searching for its meaning — not just exact words'), storage architecture ('knowledge graph with semantic embeddings'), auto-categorization behavior ('If omitted, auto-categorized'), and error conditions ('Returns an error message if the content is empty').

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description uses a standard docstring structure (summary, technical context, usage examples, Args, Returns) with zero redundant sentences. Technical details about semantic embeddings are essential for correct usage, and the examples are concise yet concrete. Front-loaded with the core action in the first sentence.

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

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

Given the complexity of the memory system and complete lack of schema descriptions, the description is comprehensive: it covers input parameters with examples, explains the output ('Confirmation with the assigned memory ID'), documents error states, and establishes the relationship with sibling tool `recall`. No gaps remain for an agent to use this tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the Args section fully compensates by documenting all three parameters: `content` (semantics and best practices), `category` (explicit enum values: preference, fact, decision, idea, project, person, general), and `importance` (enum values and ranking behavior). The description adds critical constraints not present in the schema.

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

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

The description opens with a specific verb ('Save') and resource ('long-term memory'), then distinguishes from sibling tool `recall` by stating saved items 'can be found later with `recall`'. It further clarifies the unique semantic storage mechanism ('knowledge graph with semantic embeddings') that differentiates this from simple note-taking tools like `save_note`.

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

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

Contains an explicit 'Use this when:' section with four concrete scenarios (facts, decisions, preferences, ideas), each including realistic code examples. This provides clear guidance on when to select this tool over siblings like `save_note` (for general notes) or `record_insight` (for analytics).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.