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counter-measure

Everclear MCP Server

convert_tickerhash_to_name

Convert a tickerhash into its readable token name, such as USDC or WETH. Use this before showing token data in route quotes or invoices.

Instructions

Convert a tickerhash to its human-readable token name.

      WORKFLOW INSTRUCTIONS:
      - Use this to make token information more readable
      - Tickerhashes are long strings, names are like "USDC", "WETH"
      - Returns "Unknown Token (hash)" if tickerhash not found
      
      DATA FORMATTING:
      - Always use this before displaying token information to users
      - Essential for route quotes and invoice analysis

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerhashYesThe tickerhash to convert

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral burden. It usefully discloses the fallback behavior: 'Returns "Unknown Token (hash)" if tickerhash not found.' It does not explicitly state that the tool is read-only or has no side effects, but for a pure conversion utility this is a minor omission and the fallback detail adds real value.

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 purpose is front-loaded in the first sentence, followed by structured workflow and formatting notes. There is mild redundancy between 'Use this to make token information more readable' and 'Always use this before displaying token information to users,' but the extra guidance is generally useful for a simple conversion tool.

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 simple one-parameter conversion with no output schema, the description supplies the needed context: purpose, usage timing, and fallback return value. It does not mention error handling beyond not-found or confirm read-only behavior, but those gaps are minor given the tool's simplicity.

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%, and the single parameter already has a description. The tool description adds only illustrative context ('Tickerhashes are long strings, names are like "USDC", "WETH"') without new syntax or format constraints. Baseline 3 is appropriate when the schema already documents the parameter fully.

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 first sentence states a precise verb+resource: converting a tickerhash to a human-readable token name. It implicitly distinguishes itself from the sibling convert_chain_id_to_name by specifying tickerhash input rather than chain ID. An agent can identify the tool's purpose without opening the schema.

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 explicit usage conditions: 'Always use this before displaying token information to users' and 'Essential for route quotes and invoice analysis.' It does not name an alternative tool or a when-not-to-use case, but the usage context is clear and actionable.

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