compute_dissimilarity

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

The description discloses that the result is stored in tdata.obsp[key_added] and returns a short confirmation. With no annotations provided, this adds some behavioral context but lacks details on side effects, permissions, 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?

The description is concise: three sentences plus parameter list. It is front-loaded with the main purpose and provides necessary details without extra words. Every sentence adds value.

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 has 4 parameters and an output schema, the description covers the main functionality, parameter purposes, and output storage. However, it does not mention prerequisites (e.g., dataset must have characters in obsm) or the exact format of the confirmation, leaving minor gaps.

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%, but the description explains all parameters: dataset_id (handle), method (examples of metrics), characters_key (obsm key), key_added (obsp key). This fully compensates for the schema's lack of descriptions.

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?

Clearly states the tool computes a pairwise dissimilarity map over cells, with a specific use case (useful before nj/upgma). This distinguishes it from siblings like reconstruct_tree or calculate_parsimony.

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 mentions the tool is useful before neighbor-joining or UPGMA tree reconstruction, providing clear context for when to use it. However, it does not discuss when not to use it or mention alternative tools for similar purposes.

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