Estimate the total cost of a crypto trade in basis points by factoring in taker fees on both legs and order-book impact against live Binance depth. Get a clear cost figure before executing.
Round-trip cost in basis points for a given notional: taker fees both legs plus order-book impact both legs, measured against mid from the live Binance depth.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | ||
| notional_usd | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explains the calculation methodology thoroughly: taker fees and order-book impact on both legs, measured against mid from live Binance depth, with output in basis points. It does not discuss latency, error cases, or whether the estimate is indicative, but it gives a strong account of what the tool does.
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 with no filler. Every clause adds useful information: output unit, input notional, fee components, impact components, and data source. It is concise without sacrificing meaningful detail.
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 two-parameter tool with no output schema and no annotations, the description covers the central semantics well: it defines the output, the unit, the calculation inputs, and the data source. It is missing minor but useful details such as edge-case behavior, but overall an agent has enough context to invoke the tool 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 description coverage is 0%, so the description must compensate. It clarifies that 'notional' is the quantity for which the cost is estimated and implies 'symbol' selects the Binance market. However, it does not explicitly describe either parameter, their formats, or unit constraints beyond what the parameter names already suggest.
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 what the tool computes: a round-trip cost in basis points for a given notional, specifying the components (taker fees and order-book impact on both legs) and the reference (live Binance depth mid). It is specific enough to distinguish the tool from generic siblings, though it does not explicitly differentiate from sibling tool names like base_rates.
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 phrase 'for a given notional' implies the tool should be used when the user wants a cost estimate for a specific trade size. However, there is no explicit guidance on when to use this tool versus alternatives, no exclusion criteria, and no mention of sibling tools or fallback conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.