Analyze a conversation

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

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds a valuable behavioral trait: 'without calling a model', which tells the agent this is deterministic extraction with no external model dependency. This goes beyond the annotation and helps set expectations for performance and side effects. It does not contradict any 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 a single, well-structured sentence that front-loads the primary action ('Extracts...') and lists the outputs. There is no filler, and every word contributes to understanding. It is concise without sacrificing clarity.

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 a single parameter and no output schema, the description covers the core purpose and lists the extracted entity types, which imply the return structure. It does not describe error handling or the exact output schema, but for a simple read-only tool, this is sufficient. The 'without calling a model' detail also addresses a potential performance concern. Overall, an agent has enough context to call it correctly.

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?

There is only one parameter, conversationId, and the schema provides its type (string) and format (uuid). The description mentions 'one stored conversation' which implicitly ties the parameter to that conversation, but it does not explicitly describe the parameter's role. With schema_description_coverage at 0%, the description offers minimal compensation, though the meaning is obvious from context. A score of 3 reflects adequate but not rich semantics.

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 verb 'extracts' and the resource 'one stored conversation', and enumerates the specific output types (decisions, open questions, action items, cited facts, topics). It also adds a distinguishing detail 'without calling a model', which sets it apart from any AI-based analysis siblings. This is specific and unambiguous.

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 implies the tool is for analyzing a single conversation (from one stored conversation) and that it produces structured extractions, but it does not explicitly say when to use it over alternatives like get_conversation or search_conversations, nor does it mention exclusions (e.g., when you need raw text or multiple conversations). The usage context is implied but not fully articulated.

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