get_opinion_text

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

With no annotations, the description carries the disclosure burden. It transparently explains that this tool returns text in chunks and how pagination behaves by moving offset forward by limit. It does not discuss edge cases like empty results or invalid citations, but the core read-only, paginated behavior is clearly communicated.

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?

Three short sentences with the purpose front-loaded and usage instructions following. The second sentence slightly restates the first, but overall the description is tight and every sentence contributes actionable guidance.

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 paginated text reader, the description covers the key operational concern (how to page through long text) and the output schema removes the need to explain return values. It is slightly light on error/edge-case guidance, but nothing essential is missing for a correct first call.

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 baseline is 3. The description adds practical semantic value beyond the schema by explaining the relationship between offset and limit for pagination, turning the parameters into a usable sequence rather than just individual fields.

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?

States a specific verb ('read') and resource ('opinion text'), and the chunking behavior clearly distinguishes it from sibling tools like lookup_opinion, get_opinion_figures, and get_opinion_tables. An agent can immediately understand what this tool is for.

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 clear context for when to use the tool (long opinions) and gives an explicit pagination recipe: start at offset=0 and advance by the limit. It does not name alternatives or state exclusions, but the usage guidance is unambiguous.

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