get_ai_summary

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

It discloses credit consumption, which is a behavioral trait. However, with no annotations, it fails to state whether the operation is read-only or destructive, or describe side effects. It partially fulfills the burden for a tool with no annotations.

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 short and front-loaded with the title. It includes essential information in a compact form. The trailing '[query]' is slightly distracting but does not harm comprehensibility.

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 8 parameters and no output schema or annotations, the description is insufficient. It does not explain the output format, error conditions, or how parameters like 'days' and 'hours' interact. The agent lacks context for proper invocation.

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%, so the description adds no new parameter meaning beyond the schema. It repeats the enum translations already in the schema but does not explain parameter relationships or constraints. Baseline 3 is appropriate.

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 generates an AI summary of a conversation and lists three summary types. However, it does not differentiate from sibling summary tools like get_conversations_summary or get_daily_summary, which weakens its specificity.

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 description mentions credit consumption but provides no guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. It lacks explicit context for choosing among summary types or when to use this over other summary tools.

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