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

Annotations already declare this a safe, closed-world read operation, so the safety burden is covered. The description adds a return-value hint ('RETURNS: 细胞群与比例'), which matters since there is no output schema, but says nothing about algorithmic behavior, determinism, or defaults for the optional grid_size/n_clusters.

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?

Pipe-delimited sections keep it compact and front-loaded: name, use condition, aliases, returns. The alias list is somewhat padded but serves real disambiguation value across bilingual queries.

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 3-parameter, all-optional tool with no output schema, the description covers purpose and rough output shape but leaves key call-correctness questions open: all parameters are optional yet `events` is clearly required for meaningful clustering, and no defaults are stated.

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 100%, so baseline is 3. However the schema strings themselves are minimal ('网格大小', '聚类数') and the description adds no meaning about how grid_size relates to FlowSOM metaclusters or what happens when n_clusters is omitted.

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 states a specific verb+resource: unsupervised clustering of flow cytometry data into cell populations, with a clear domain qualifier (flow cytometry) and FlowSOM as an alias. It does not differentiate from nearby siblings such as scrna_cluster or flow_gate, so an agent must infer the boundary itself.

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 'USE WHEN: unsupervised clustering of flow cytometry data into cell populations' clause gives an explicit trigger condition. It stops short of the 5 bar because it names no alternatives or exclusions — nothing tells the agent when to prefer scrna_cluster or flow_gate instead.

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