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

Annotations indicate non-read-only and non-idempotent behavior; the description adds critical operational context not in annotations: cost model (3 credits), latency expectations (2-3 minutes for complex queries), return value structure (AI response text + conversation_id), and capability scope (live tool use). No contradictions with 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?

Four sentences with zero waste: opening establishes purpose/cost/capabilities, second sentence covers return values (crucial given no output schema), third explains conversation threading, fourth covers context linking and latency. Information is front-loaded and densely packed.

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?

Compensates effectively for missing output schema by documenting return values (text + conversation_id). Covers essential operational constraints: credit cost, time complexity, and integration points with sibling tools (atlas_list_contexts). Would benefit from explicit differentiation from atlas_advisor_chat, but remains highly complete for invocation purposes.

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?

Input schema has 100% description coverage, establishing a baseline of 3. The description largely restates schema semantics for context_id and conversation_id, though it usefully emphasizes the 'omit to start new' pattern for conversation_id. It does not significantly enhance understanding of the 'message' parameter beyond the schema's 'User message' definition.

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?

Clearly states the action (send message to Atlas AI assistant), specifies cost (3 credits), and enumerates specific capabilities (workforce intelligence, salary benchmarks, competitor intel). However, it does not explicitly differentiate from sibling tool 'atlas_advisor_chat', leaving potential ambiguity about which conversational AI to use.

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?

Provides explicit guidance on conversation lifecycle (omit conversation_id to start new, include to continue) and references sibling tool 'atlas_list_contexts' for obtaining valid context_id values. Lacks explicit comparison to alternative chat tools (e.g., atlas_advisor_chat) but effectively explains the threading and context-aware usage patterns.

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