Project Tessera

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

No annotations are provided, so the description carries the full burden. It discloses that the tool extracts decisions, preferences, and facts, which adds behavioral context beyond basic import functionality. However, it lacks details on permissions, rate limits, error handling, or what happens to the imported data (e.g., storage location, indexing).

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 is front-loaded with the core purpose, followed by usage instructions and benefits. Every sentence earns its place: the first defines the action, the second explains parameters, the third describes processing, and the fourth states the use case. No wasted words.

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 tool's moderate complexity (importing and processing external data), no annotations, and the presence of an output schema, the description is mostly complete. It covers purpose, parameters, and processing outcome, but could benefit from mentioning authentication needs, data limits, or output format since the output schema is not described here.

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?

With 0% schema description coverage, the description compensates by explaining both parameters: 'data' as 'exported JSON data' to paste and 'source' with enumerated values ('chatgpt', 'claude', 'gemini', 'text'). This adds meaningful semantics beyond the bare schema, though it could specify JSON structure or validation rules.

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 clearly states the tool's purpose with specific verbs ('import', 'extracts') and resources ('conversations from ChatGPT, Claude, or Gemini exports'). It distinguishes from siblings by focusing on importing external conversation data, unlike tools like 'import_memories' or 'import_from_ai' which may handle different data types or sources.

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?

The description provides clear context for when to use this tool ('to recover knowledge from past AI interactions') and specifies the supported sources. However, it does not explicitly state when NOT to use it or mention alternatives like 'import_memories' or 'import_from_ai' for comparison.

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