explain_indicator

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

No annotations exist, but the description discloses the underlying AI model (Groq LLaMA 3.3-70B with NVIDIA NIM fallback) and implies a read-only, non-destructive action. It adds useful context beyond the schema.

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 three focused sentences plus a technical note. It front-loads the main purpose and every sentence contributes value without redundancy.

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 simple explanation tool with no output schema, the description adequately covers input parameters, use case, and technology. It could optionally describe the output format, but the current level is sufficient for an AI agent to use 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?

Schema coverage is 100% and each parameter has a description. The tool description does not add further semantic detail beyond what is already in the schema, meeting the baseline for full schema coverage.

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 it provides a plain-English AI explanation of any technical indicator value, including meaning and bullish/bearish interpretation. This distinctly separates it from sibling tools like get_technical_indicators which likely return numeric data.

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 specifies it is 'great for learning or explaining to non-experts', giving a clear use case. However, it does not explicitly state when not to use it or mention alternative tools for different contexts.

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