Jet3D MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool maps to a distinct capability: catalog browsing (list shapes/fonts), technical reference (get specifications), and purchase generation (create checkout). There is no semantic overlap or ambiguity between these actions.
Naming Consistency5/5All three tools follow a consistent verb_noun pattern: list_..., get_..., create_.... The naming clearly expresses the operation and resource, making the tool surface predictable.
Tool Count5/5Three tools is at the lower bound but well-scoped for a narrow product/purchase domain: discover options, reference specs, and create a checkout. Each tool covers a distinct step in the workflow and none feel redundant.
Completeness4/5The core custom-cutter workflow is covered: browse options, retrieve compliance/spec details, and generate a paid checkout link. The only notable gap is order/payment status lookup after checkout, but this can be handled outside the MCP server or by a later addition.
Average 3.9/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose that the tool generates a checkout URL and is transactional in nature, but it does not mention side effects like order creation, idempotency, authorization requirements, or whether customer contact details are mandatory for certain order types. This is a partial disclosure with clear gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence contains the core action and outcome with no filler. It is front-loaded with the primary purpose and reads naturally, making it easy for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 12 parameters and no output schema, yet the description only provides a one-line summary. It does not explain the expected response shape, prerequisites, or notable parameter combinations (e.g., physical vs. digital order requirements). An agent would struggle to understand the full call context without opening the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents every parameter, including enums, defaults, and constraints. The description adds no parameter-specific meaning beyond the high-level 'personalized' cookie cutter concept, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific, compound action: design a personalized 3D-printed cookie cutter and generate an Instant BLIK checkout URL. It clearly identifies the tool's output (payment URL) and distinguishes it from the sibling lookup tools, which retrieve specifications or lists.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance about when to choose this tool over its siblings, such as 'use list_available_shapes_and_fonts first' or 'for available specs use get_cutter_specifications.' The intended workflow must be inferred solely from the tool's name and the sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral disclosure burden. It does convey read-only listing behavior and the scope of what is listed, but it says nothing about ordering, availability/static nature, or return shape. There is no contradiction, but behavioral detail is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
It is a single, front-loaded sentence that names the action and the full set of resources without filler. 'Typography font styles' is slightly repetitive but does not hurt clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter listing tool, the description is nearly complete: it enumerates exactly what the agent will receive. The only gap is some explicit guidance about how the listed options connect to create_custom_cutter_checkout, though the sibling names and 'for personalized cutters' supply enough context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema already exhaustively documents them (100% coverage). The description adds scope context by naming the categories returned, which is all that parameter semantics can do here; baseline 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb 'List' and names three concrete resources: supported 3D shapes, typography font styles, and recommended occasions for personalized cutters. This clearly distinguishes the tool from create_custom_cutter_checkout and get_cutter_specifications, which imply different actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for personalized cutters' gives some context, but the description never states when to choose this tool over siblings or what the output should be used for. Usage is only implied by the name and the available sibling list, not explicitly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden. It explicitly states 'Retrieve', indicating a read-only operation, and mentions 'official' specifications, suggesting verified data. No side effects are disclosed, but the action is clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with no redundancy, directly conveying the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description provides a general sense of the content returned (technical, food safety, material specs) but does not specify the format or structure. This is adequate for a simple retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so there is nothing to describe. The description fully accounts for the input aspect.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the action (Retrieve), the resource (technical, food safety, and material specifications for Jet3D cookie cutters and stamps), and it distinguishes from sibling tools which handle checkout and listing shapes/fonts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when specifications are needed) but does not explicitly state conditions or contrast with alternatives. No guidance is provided on when not to use it, though the purpose is self-explanatory.
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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