get_conversation_text

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

No annotations exist, so the description carries full burden. It discloses that output is plain text, compact, and filtered, but does not mention error handling (e.g., missing session), performance, or output structure details beyond 'plain text'. The schema covers parameter defaults, but behavioral traits like logging or throttling are omitted.

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?

Two sentences front-load the key purpose and value proposition ('compact text optimized for LLM context'). No fluff, but could be more structured (e.g., separate when-to-use). Still efficient.

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?

No output schema exists, so description should clarify output format. It states 'plain text' and 'compact' but does not specify line structure, timestamps, or whether it includes metadata. For 7 parameters with defaults (e.g., max_messages=100), the description trusts the schema, leaving some gaps for an agent.

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 marginal value. It mentions 'filtering' and 'compression' generically, which relate to parameters like start_time, max_messages, and merge_consecutive, but does not explain how they interact or add meaning beyond the schema 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?

The description clearly states the verb 'Get', resource 'conversation in plain text format', and distinguishes from siblings like get_full_conversation (JSON) and get_conversation_between by highlighting 'compact text optimized for LLM context'. It also mentions filtering and compression, making the purpose specific and differentiated.

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 when to use this tool: when you need token-efficient text for LLM context, contrasting with JSON alternatives. It does not explicitly state when NOT to use it or list alternative tools, but the context of sibling tools and the phrasing 'saves tokens vs JSON' provides clear guidance for selection.

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