get_conversation_messages

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool reads messages but doesn't cover critical aspects like authentication requirements, rate limits, error conditions (e.g., invalid conversation ID), response format, or whether it's idempotent. For a read operation with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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, efficient sentence that front-loads the core purpose ('Get messages from a specific LinkedIn conversation thread'). There is no wasted verbiage, repetition, or unnecessary detail, making it easy for an agent to parse quickly.

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's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks behavioral details (e.g., authentication, errors) and usage guidelines. Without an output schema, the description doesn't hint at return values, leaving the agent uncertain about what to expect from the tool's execution.

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%, with clear documentation for all three parameters (conversation_id, start, count). The description adds no additional semantic context beyond what the schema provides—it doesn't explain parameter interactions (e.g., how start and count work together for pagination) or constraints (e.g., maximum count values). With high schema coverage, the baseline score of 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 action ('Get messages') and resource ('from a specific LinkedIn conversation thread'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'get_conversations' (which likely lists conversations rather than messages within one) and 'send_message' (which writes rather than reads). However, it doesn't explicitly mention pagination or filtering capabilities, which would make it more specific.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid conversation ID), exclusions (e.g., not for group chats if applicable), or comparisons to similar tools like 'get_conversations'. Without such context, the agent must infer usage from the name and schema alone.

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