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Robyn — Gasless Cross-Chain for AI Agents

What would make this transfer cheaper?

robyn_improve_cost

Turns a price into a decision. Returns concrete, QUANTIFIED changes — a cheaper destination chain with the exact saving, the size at which a fixed payout cost stops dominating, whether the instant float lane applies, and whether rails that would have competed were unavailable. CRITICAL: suggestions with changesOutcome: true move WHERE THE MONEY LANDS. Never apply those silently as cost savings — confirm with the user that the alternative destination is acceptable first, or you will have saved gas by delivering to the wrong place. If nothing can be improved it says so rather than inventing filler.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amountYes
toChainYes
toTokenNo
fromChainYes
fromTokenNo
toAddressNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.2/5.0
Behavior4/5

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

With no annotations, the description carries the full transparency burden. It discloses the critical behavior that `changesOutcome: true` moves where money lands and requires user confirmation, and that the tool will honestly report no improvement rather than fabricating suggestions. This is meaningful beyond the schema.

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

Conciseness4/5

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

The description is dense and well-structured, with a clear list of output categories and a highlighted CRITICAL warning. The opening sentence 'Turns a price into a decision' is somewhat vague, but every subsequent sentence earns its place.

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

Completeness2/5

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

Given no output schema and 0% parameter coverage, the description is incomplete. It provides rich behavioral context but fails to explain input parameters or the exact return structure, leaving the agent without enough detail to reliably construct a correct invocation or parse the results.

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

Parameters1/5

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

Schema description coverage is 0% and the description provides no explanation of the six parameters (fromChain, toChain, amount, toToken, fromToken, toAddress). The agent is left without input semantics, which is a critical gap.

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

Purpose4/5

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

The description clearly states the tool's purpose: it turns a price into quantified improvement suggestions (cheaper destination chain, cost thresholds, float lane, rail availability). It distinguishes itself from sibling tools by focusing on cost-improvement decisions rather than quotes or route status, though it does not explicitly name alternatives.

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

Usage Guidelines3/5

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

The description implies usage when a transfer price already exists and cost improvements are desired, especially by noting 'if nothing can be improved it says so'. However, it does not explicitly state when to use this tool versus siblings like robyn_quote or robyn_fee_forensics, nor does it provide exclusions.

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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