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

With no annotations, the description carries the full burden. It discloses performance traits ('zero LLM cost, instant') and the internal mechanism ('Recomputes confidence automatically via weighted-average of top-level nodes'), which goes beyond the schema. It lacks details on reversibility or error conditions, but the core behavior is transparent.

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?

Three sentences front-load the purpose, then usage, then behavioral consequence. There is no wasted wording, and every sentence earns its place. The example is compact but illustrative.

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 mutation tool with no output schema, the description covers primary purpose, invocation context, and a key behavior. It does miss describing the return value or side effects, but given the moderate complexity and rich schema, it is adequately complete for an agent to decide and invoke correctly.

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 input schema has 100% coverage with descriptions for all parameters, so the baseline is 3. The description adds the real-world use case example but does not elaborate on any specific parameter beyond what the schema already provides, yielding no extra semantic value.

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-object phrase ('Directly update causal tree node probabilities') and differentiates from sibling tools like update_thesis by focusing on node-level probability updates. The concrete example ('CPI came in at 3.2%') reinforces the exact action.

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 gives a clear when-to-use scenario ('when the agent observes a confirmed fact and wants to reflect it immediately') and contrasts with LLM-based alternatives via 'zero LLM cost, instant'. However, it does not explicitly name alternative tools for when NOT to use this tool, so it stops short of the full 5.

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