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Silver Price Per Gram MCP

Calculate junk silver coin value

calculate_junk_silver_value
Read-onlyIdempotent

Melt value of US silver coins at the live silver spot: pre-1965 90% dimes, quarters and halves, 1965-1970 40% Kennedy halves (4.6 g silver, Coinage Act of 1965), 1942-1945 35% war nickels, Morgan and Peace dollars and American Silver Eagles. Pass coins with counts, or face_value_90 for a bag of mixed 90% coin (1 dollar face = 22.5 g silver). Returns silver ounces per coin, totals and melt per dollar face.

Input Schema

NameRequiredDescriptionDefault
coinsNoCoins and counts, for example [{"coin":"quarter_90","count":40},{"coin":"morgan","count":3}].
currencyNoISO 4217 currency code, for example USD, EUR, GBP, INR, CAD, AUD, MXN. Default USD.USD
face_value_90NoDollars of face value in mixed pre-1965 90% dimes, quarters and halves, for example 10.
dealer_spread_percentNoPercent a buyer keeps below melt, for example 15 for an offer at 85% of melt. Default 0.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/openWorld, so the burden is lighter; the description still adds that valuation is at the *live* spot price (explaining the openWorld hint) and what the response contains (ounces per coin, totals, melt per dollar face). It doesn't discuss spot-price staleness or failure modes, keeping it below a 5.

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?

Two dense sentences: the first establishes scope and formulas, the second tells the agent how to call it and what comes back. Front-loaded and waste-free, though the coin-family enumeration is close to sentence-length overload.

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?

No output schema exists, and the description compensates by summarizing the return payload. With zero required parameters, the 'Pass coins with counts, or face_value_90' sentence is the critical line stating at least one input path—covered adequately, though it never says whether both may be combined.

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

Parameters4/5

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

Schema coverage is already 100%, yet the description adds substantive meaning: the 90% face-value conversion (1 dollar face = 22.5 g silver) and the 4.6 g silver content of 40% Kennedy halves. That is real domain math beyond the field descriptions, though dealer_spread_percent is left to the schema.

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?

Names a specific verb (melt value) and resource (US junk silver coins at live spot) and enumerates exactly which coin families are covered, including date ranges and silver percentages. An agent can tell this is a coin-specific melt calculator rather than a generic metal-weight calculator.

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 by distinguishing coin-based input from a bag of 90% face value, but it never names the obvious sibling 'calculate_silver_value' or states when to prefer one over the other. The agent must infer the routing itself.

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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