get_top_pick
Free: the board's current top LLM pick and best open-weights pick for one language, with the public benchmark source it is based on.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| lang | Yes | Language code: zh, ko, nb, de, nl, es or pt |
Free: the board's current top LLM pick and best open-weights pick for one language, with the public benchmark source it is based on.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | Yes | Language code: zh, ko, nb, de, nl, es or pt |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so safety is covered. The description adds real context beyond that: the call is 'Free', it covers one language, and it returns two distinct picks plus the public benchmark source they are based on. It does not contradict the annotations, and no permission or rate-limit caveats are mentioned (nor likely needed for a read-only lookup).
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?
A single front-loaded sentence leads with the cost signal ('Free') and then the return contents; nothing is redundant or padded. Slightly dense, but every clause earns its place.
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 one-parameter, read-only lookup with no output schema, the description usefully previews the return contents (top LLM pick, best open-weights pick, benchmark source), which is the main thing an agent needs. What is missing is a note on how the 'free' tier relates to any paid alternative, and the unstated (but low-risk) behavior when a language has no pick.
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% — the lang parameter's schema entry already enumerates the accepted language codes (zh, ko, nb, de, nl, es, pt). The description only restates 'for one language' and adds no syntax, default, or constraint beyond the schema, so the baseline 3 applies.
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 gives a clear verb+resource ('get ... top pick') and goes further by naming the exact return contents: the top LLM pick, the best open-weights pick, and the benchmark source. Sibling tools (list_languages, list_products) are enumeration tools with obviously different purposes, so no explicit differentiation is required. A minor ambiguity remains about what 'the board' refers to.
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?
Usage is only implied: the caller infers it should call this tool when it needs the leading model for a single language, and 'Free:' hints that this is the no-cost path. There is no explicit when-to-use, when-not-to-use, or named alternative (e.g., a paid/pro tier).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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