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vandurmedries

print-advisor-mcp

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a different advisory question: material selection, print-bed compatibility, and cost/time estimation. Although fits_on_printer and print_estimate both take dimensions, their purposes are clearly separated and unlikely to be confused.

    Naming Consistency4/5

    All tool names use lowercase snake_case and are short, recognizable domain terms, so the set feels consistent. The forms are slightly mixed (two noun phrases and one verb phrase), but there is no camelCase or arbitrary naming to cause real friction.

    Tool Count5/5

    Three tools is a well-scoped size for a 3D-printing advisor; each tool covers a distinct decision and earns its place. The server avoids bloat while still providing useful breadth across materials, fit, and print estimates.

    Completeness4/5

    The set covers the main advisory workflow: choose material, check bed fit, and estimate cost/time. It might lack more specialized advice such as printability or orientation guidance, but those gaps are workaroundable and outside the apparent scope.

  • Average 4.3/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • 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 disclosure burden. It discloses the main lookup behavior and adds a specific behavioral detail (empty input returns the full table), but it does not say how unknown or invalid toepassing values are handled or whether the result is filtered versus scored.

    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?

    The description is one clear purpose line followed by a compact Args block. It has no filler and front-loads the main purpose before parameter details.

    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 single-optional-parameter material guide with an output schema, the description covers the core behavior and parameter semantics. It does not mention the sibling tools, but the tool is simple enough that the selection context is clear from the purpose and examples.

    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?

    The input schema provides only a name and default with no description, so the description must compensate. It defines toepassing as the part's purpose, lists six concrete example values, and explains that leaving it blank returns the full table, which is sufficient semantic guidance for a single optional parameter.

    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 description opens with a concrete imperative ('Kies') naming the resource ('het juiste 3D-printmateriaal') and the selection criterion ('voor een toepassing'). Examples of application types make the tool's purpose unambiguous and clearly distinguish it from sibling tools focused on printer fit and print estimation.

    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 gives concrete example applications and explicitly states the empty-input behavior, so an agent can infer when to use it. However, it does not explicitly contrast this tool with fits_on_printer or print_estimate, nor state when not to use it.

    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?

    With no annotations provided, the description carries the behavioral disclosure burden. It meaningfully discloses that 45° rotation is considered and that an empty printer value scans all known printers. It does not go into edge cases like invalid printer names, but the output schema likely covers return expectations.

    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?

    The description is compact and front-loaded: purpose first, then a valuable behavioral note, then a terse parameter list. Every sentence earns its place without unnecessary filler.

    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 fit-check tool with an output schema, the description covers the essential inputs, units, rotation behavior, and printer selection behavior. It could be slightly stronger with explicit guidance on choosing between this tool and its siblings, but nothing an agent needs to invoke it correctly is missing.

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

    Parameters5/5

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

    Schema description coverage is 0%, but the description documents all four parameters with units, examples, and default behavior. Each dimension is explained as 'in mm', and printer is illustrated with concrete names plus the all-printers default.

    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 description opens with a specific verb and resource: checking whether a part fits on a printer build plate. It also adds a distinctive detail (45° in-plane rotation), which clearly separates it from the sibling tools material_guide and print_estimate.

    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 intended use is implied through the description and parameter documentation, especially the note that leaving printer empty checks all known printers. However, it does not explicitly contrast this tool with material_guide or print_estimate, nor does it state when not to use it.

    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?

    With no annotations, the description carries the behavioral burden, and it does so well. It discloses key behavior: volume_cm3 takes precedence over dimensions, no file is needed, and the tool estimates material, weight, cost, and print time. It does not describe side effects, but an estimation tool is inherently non-destructive and the output schema is provided separately.

    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?

    The description is well-structured: a one-sentence summary, a short usage note, input-mode guidance, and a clean Args block. Every sentence provides actionable information and there is no fluff or repetition.

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

    Completeness5/5

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

    Given 8 parameters, no required fields, no annotations, and no schema descriptions, the tool is fully specified for correct invocation. The description covers all parameters, explains the precedence rule, lists material choices, and notes that no file is needed. An output schema exists, so the description does not need to explain return values.

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

    Parameters5/5

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

    Schema description coverage is 0%, so the description must compensate, and it does thoroughly. It documents all 8 parameters with units, allowed material values, infill range, and layer height guidance. This adds substantial meaning beyond the bare schema property titles.

    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 description opens with a specific verb and resource: 'Schat materiaalgebruik, gewicht, kosten en printtijd voor een 3D-print.' This clearly names the tool's function and output domains. The scope (estimating print metrics) is distinct from the sibling tools material_guide and fits_on_printer, even without naming them.

    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 clearly states when to use the tool: it works on dimensions or a known volume and requires no file. It also gives input strategy: provide dimensions OR volume_cm3. However, it does not explicitly mention alternatives or exclusions relative to the sibling tools, so it stops short of full routing guidance.

    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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