telos.learning.forge

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

With no annotations, the description carries the full burden and does a reasonable job: it declares read-only behavior, zero-auth, and no external side effects, plus the return format (a JSON packet). It stops short of idempotency or determinism guarantees, but the safety profile is explicit rather than inferred.

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?

Two short sentences, front-loading the usage trigger before the behavioral and return facts. Efficient, though the dense domain terms ('receipt-backed learning labs before synthesis') slightly obscure an otherwise tight phrasing.

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?

For a zero-param tool with no output schema, the description covers purpose, trigger, safety, and return type. But given the very large sibling surface, it leaves the relationship to telos.learning.labs and the proof tools unexplained, which is the main remaining gap for correct tool selection.

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?

The tool takes zero parameters (empty schema, additionalProperties false), so the per calibration baseline of 4 applies. There is nothing parameter-level for the description to explain or omit.

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 names a domain resource ('receipt-backed learning labs') and an output ('JSON Learning Forge packet'), but the operative verb ('leads need to become...') is diffuse jargon rather than a clean verb+resource statement. It does not distinguish itself from the sibling telos.learning.labs, which an agent would have to disambiguate on its own.

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?

It supplies an explicit trigger ('Use when video, channel, paper, and benchmark leads need to become...before synthesis'), which implies pre-synthesis usage. However, it names no when-not conditions and no alternatives, even though telos.learning.labs and the telos.proof.* family are plausible competing choices.

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