get_poll_historyGet the raw poll history — every model's pick over time — behind a category's verdict. The credibility/audit trail. Optionally filter by model.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Category slug, e.g. best-llm-observability | |
| limit | No | Optional: max rows (default 100). | |
| model | No | Optional: only this model's picks. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds context such as 'raw poll history' and 'credibility/audit trail', clarifying the nature of the data retrieved (historical, per-model picks) beyond the 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?
The description is two sentences, front-loaded with the main action. It is concise with no unnecessary words, though the second sentence is somewhat redundant.
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?
The description lacks details about the return value structure (e.g., format of picks, timestamps). With no output schema, it should provide more behavioral context. It is moderately complete but leaves gaps.
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% with all parameters described. The description mentions optional model filtering but adds no new detail beyond the schema. 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 tool retrieves raw poll history, specified as 'every model's pick over time behind a category's verdict'. It distinguishes from siblings like 'get_best_in_category' by focusing on the audit trail rather than the current best.
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 usage for historical audit data but does not explicitly state when to use this tool over alternatives like 'get_best_in_category' or 'list_categories'. No exclusions or when-not-to-use guidance provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct operation: listing categories, searching, getting a category leaderboard, polling history, or product details. There is no functional overlap, ensuring an agent can clearly differentiate between them.
All tools follow a consistent verb_noun pattern (get_best_in_category, get_poll_history, get_product, list_categories, search_best), with clear verbs and descriptive nouns, promoting predictable usage.
With 5 tools, the server is well-scoped for a domain focused on ranking retrieval and exploration. Each tool serves a clear purpose without unnecessary bloat or inadequacy.
The tool set covers the full workflow: discover categories (list_categories), search for best (search_best), view detailed rankings (get_best_in_category), audit trail (get_poll_history), and cross-category product info (get_product). There are no obvious gaps for the intended use case.