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Radiant-Core

Radiant MCP Server

Official
by Radiant-Core

radiant_estimate_tx_fee

Calculate transaction fee in satoshis from input/output counts and OP_RETURN sizes to budget before building a Radiant transaction. No network call required.

Instructions

Estimate transaction fee in satoshis given input/output counts. Pure arithmetic — no network call. Use this to budget before building a transaction. Supports OP_RETURN outputs (e.g., for token minting).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_countYesNumber of P2PKH inputs
fee_per_byteNoFee rate in photons/byte (default: 10000, the mainnet V2 min-relay floor). Lower values will be rejected by relaying nodes on mainnet.
output_countYesNumber of P2PKH outputs (include change output)
op_return_sizesNoSizes in bytes of any OP_RETURN data payloads (e.g., [80] for an 80-byte Glyph envelope)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.6.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explicitly states 'Pure arithmetic — no network call,' which tells the agent the operation is deterministic, side-effect-free, and requires no network state. This is valuable context, though it could further clarify edge-case behavior or return format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the core purpose, followed by key behavioral notes. Every sentence earns its place: the main function, the no-network-call guarantee, the usage context, and the OP_RETURN capability. No filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a low-complexity arithmetic tool, the description covers the key aspects: what it computes, the unit (satoshis), the main inputs, and one important extension (OP_RETURN). There is no output schema, but the description's phrasing implies the return is the estimated fee in satoshis. It could be more explicit about how fee_per_byte is applied, but the schema carries that detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all four parameters. The description adds a bit of meaning by connecting OP_RETURN outputs to 'token minting' and framing the tool around input/output counts, but it does not substantially go beyond the schema, warranting the baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Estimate transaction fee in satoshis') and specifies the inputs ('given input/output counts'). It also distinguishes itself from siblings through 'Pure arithmetic — no network call' and the explicit OP_RETURN support, separating it from tools like radiant_estimate_fee or network-dependent operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a clear usage context: 'Use this to budget before building a transaction.' It does not explicitly name alternative tools or state when not to use it, but the use case is concrete and unambiguous, which is sufficient for a focused estimation utility.

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