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

With no annotations, the description carries the full burden for behavioral disclosure. It signals autonomous mutation-like behavior but does not state whether the input lattice is modified in place, what side effects occur, or what constraints exist. This partial disclosure is insufficient for a tool that 'evolves' and 'discovers'.

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 one compact sentence with no filler words, and the core idea is front-loaded. However, it is telegraphic and relies on unexplained technical vocabulary, so it is concise but not fully clear.

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 tool with four parameters, no annotations, and 0% schema description coverage, the description is too thin to support confident invocation. It identifies high-level inputs but leaves data shapes, defaults, and expected behavior unresolved.

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?

Schema description coverage is 0% and the description does not map its high-level ideas to any of the four parameters. It never mentions lattice_id, lattice_data, default_rate, or how feedback_traces relates to 'execution feedback', so it adds no parameter-level meaning.

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 identifies a specific resource (lattice), a clear verb (evolve/refine), and an input source (execution feedback). It is distinguishable from siblings like cognitive.compile_invariant_lattice by the feedback-driven nature, though jargon like 'higher-order invariants' and 'cliques' is unexplained.

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 phrase 'from execution feedback' implies this tool should be used when execution feedback is available, but there is no explicit when-to-use guidance or mention of alternatives. The usage context is implied rather than clearly stated.

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