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Cavalera Forge MCP

Cavalera Forge MCP

by jbillochon
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

What uses an ingredient

ingredient_uses
Read-onlyIdempotent

Find products that use a specific ingredient, with quantities and a breakdown by product type. Filters results by product type to answer Star Citizen crafting questions.

Instructions

Products that use an ingredient, with quantities and a breakdown by product type.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ingredient": "<ingredient>"
    },
    "name": "ingredient_uses"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
ingredientYesIngredient name, e.g. 'Stileron', 'Taranite'.
product_typeNoOptional product type filter.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, openWorld), so the description's job is to add context — and it does, disclosing that results carry quantities and a breakdown by product type, which matters because there is no output schema. It does not address result ordering, pagination, or partial-match behavior, which keeps it from 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.

Conciseness5/5

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

A single front-loaded sentence that identifies the resource first and appends the return shape; nothing is redundant or padded.

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

Completeness3/5

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

For a simple read-only lookup with full annotation coverage, the description is close to sufficient, and the return shape is covered. The gap is the product_type filter, which has no enum and no documented value space — an agent cannot reliably construct that filter from the description or schema.

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 coverage is 67% and the two described parameters already carry inline explanations ('Ingredient name, e.g. Stileron, Taranite', 'Optional product type filter'). The description's phrase 'breakdown by product type' faintly echoes the filter but adds no accepted-value or format detail for product_type (no enum exists) and says nothing about the undocumented limit parameter.

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 states a specific verb+resource — products that use a given ingredient — plus the shape of the result (quantities and a per-product-type breakdown). It is clear enough to distinguish from things like search_products or product_recipe, though it never explicitly contrasts itself with the similarly named sibling craftable_from.

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

Usage Guidelines2/5

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

There is no guidance on when to pick this tool over alternatives such as craftable_from or product_recipe, nor any stated preconditions or exclusions. The agent must infer the routing decision from the name alone.

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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