merge

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

With no annotations, the description takes full responsibility. It discloses that the operation is destructive (deletes aliases) and details the merge process. It is transparent about key behaviors, though it doesn't mention reversibility or error conditions.

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 that efficiently convey purpose, actions, and usage context. No redundant information. Front-loaded with the most important information.

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 no output schema and 3 parameters, the description covers the merge operation thoroughly, including prerequisites (use after find_candidates) and effects (deletion of aliases). Lacks details on edge cases but is sufficient for typical usage.

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 coverage is 100%, but the description adds meaning beyond schema definitions by explaining the roles of canonical and aliases in the merge process and how they relate to the tool's functionality.

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: merging duplicate or near-duplicate nodes. It specifies the actions: moves links from aliases to canonical, sums salience, and deletes aliases. This distinguishes it from sibling tools like dedup and find_candidates.

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?

Explicitly states to use after find_candidates reveals near-duplicates and provides an example. While it doesn't mention when not to use, the context is clear and helpful for an AI agent.

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