LLM token board
tokens_boardPaid (0.10 USDC per call). Every model's input, output and blended fixing for one day, plus the frontier basket.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| day | No | YYYY-MM-DD; default latest. |
tokens_boardPaid (0.10 USDC per call). Every model's input, output and blended fixing for one day, plus the frontier basket.
| Name | Required | Description | Default |
|---|---|---|---|
| day | No | YYYY-MM-DD; default latest. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, covering safety. The description adds the cost (0.10 USDC per call) and the data scope (input, output, blended fixing, frontier basket). However, it does not disclose other potential behaviors like rate limits or response size, so it meets the baseline but adds limited extra context.
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 a single concise sentence with no fluff, and the cost is front-loaded. However, it is quite terse and could have been more explicit about what 'fixing' means, but it earns a 4 for efficiency.
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?
Given the tool's simplicity (1 optional param, no output schema), the description covers the core data scope and cost. But terms like 'fixing' and 'frontier basket' are not explained, and without an output schema, agents may not know the exact return structure. The annotations fill in safety, so a 3 is reasonable, but it's not fully self-sufficient.
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?
The schema fully documents the single parameter 'day' with format and default behavior, so coverage is 100%. The description adds no additional meaning for the parameter; it only mentions 'for one day' which aligns with the schema. Baseline 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 identifies the resource: token fixing data for models (input, output, blended) for a given day, plus the frontier basket. However, it lacks an explicit verb and does not distinguish it from siblings like tokens_fixing or tokens_models, which are similarly named and likely related.
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 gives no guidance on when to choose this tool over alternatives. It does not mention that tokens_history is for historical trends, tokens_quotes for real-time quotes, or that this is a daily snapshot. The paid nature is mentioned, but not in a way that guides selection.
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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