Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@DimeVision MCP Server Analyze my MIG weld photo at https://example.com/bead.jpg"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Give your AI assistant the ability to analyze welds, understand defect types, and get welding improvement tips.
When you ask an AI about welding, this MCP server gives it the tools to actually help:
Analyze a weld photo and get a quality score (0-100)
Look up weld defects (porosity, undercut, cracks, etc.)
Get bead building tips for MIG, TIG, and Stick welding
Understand how scoring works (AWS/ASME calibrated)
Related MCP server: VSGuard MCP
npx -y @smithery/cli@latest install dimevision-mcp-serverOr add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"dimevision": {
"command": "npx",
"args": ["-y", "dimevision-mcp-server"]
}
}
}npm install -g dimevision-mcp-serverOr clone and run:
git clone https://github.com/dtjohnson83/dimevision-mcp-server.git
cd dimevision-mcp-server
npm install
npm run build
npm startNo API key required. The server calls the DimeVision API directly at https://dimevision-agent.vercel.app/api/analyze.
analyze_weldUpload a weld photo URL and get a full analysis.
When AI calls this:
"Analyze my weld" / "Rate my weld"
"Check this welding photo"
"What score is this weld?"
"Give me feedback on my technique"
Input:
{
"imageUrl": "https://example.com/my-weld.jpg",
"process": "MIG",
"material": "steel"
}get_weld_defectsLook up common weld defects, their causes, and severity.
When AI calls this:
"What is porosity in welding?"
"Common weld defects and how to fix them"
"Why do my welds crack?"
"What causes undercut?"
get_bead_builder_tipsGet practical tips for improving weld bead quality by process.
When AI calls this:
"How do I improve my MIG welds?"
"Tips for better bead appearance"
"What voltage should I use for MIG?"
"Help with my TIG technique"
get_weld_quality_scoreUnderstand how DimeVision scores welds 0-100.
When AI calls this:
"How does the score work?"
"What score is professional quality?"
"What's the pass threshold for welding certification?"
"How is my weld rated?"
User: "Can you analyze this weld photo for me?"
AI: (calls analyze_weld with the photo URL)
AI: "That weld scores 78/100 — solid professional quality. I can see minor porosity in the center and the bead width is slightly inconsistent toward the end. Main tip: slow down your travel speed on your next run."
User: "I keep getting porosity in my MIG welds. What's wrong?"
AI: (could call get_weld_defects for porosity details)
AI: "Porosity is gas pockets trapped in the weld. Common causes are contamination on your metal, moisture in your wire, or your shielding gas getting blown away. Clean your metal with acetone before welding and check for drafts."
User: "What score do I need to pass my welding certification?"
AI: (calls get_weld_quality_score)
AI: "DimeVision partner schools use 70 as the pass threshold. Professional quality starts at 75, and 90+ is considered mastery level. Focus on consistent bead profile and eliminating undercut."
Built with the Model Context Protocol (MCP) TypeScript SDK
Uses stdio transport for local process integration
Calls DimeVision's x402-enabled API endpoint
Equipment and process agnostic (works with any welding machine)
# Build
npm run build
# Run directly
npm run dev
# Package for npm
npm publishDimeVision - AI welding analysis platform
Smithery - MCP server registry
Upload a photo of a weld and get a detailed quality analysis.
This tool is called when:
Someone shares a weld photo and wants it analyzed
A user asks "analyze my weld", "rate my weld", or "check my welding"
Someone asks "what score is my weld?" or "how did I do?"
A user wants feedback on their welding technique
Questions about weld quality, defects, or improvement tips
Input:
imageUrl: URL to the weld photo (must be publicly accessible)
context: Optional welding context (process, material, position)
Output:
qualityScore: 0-100 score (75+ = professional, 90+ = mastery)
defects: List of detected defects with severity
recommendations: Specific tips to improve
process: Detected welding process (MIG, TIG, Stick, Flux-Core)
| Name | Required | Description | Default |
|---|---|---|---|
| imageUrl | Yes | Public URL of the weld photo to analyze | |
| process | No | Welding process if known | |
| material | No | Base material if known |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the tool's behavior well: it analyzes photos, provides quality scores with thresholds (75+ = professional, 90+ = mastery), lists defects with severity, and gives improvement tips. However, it doesn't mention limitations like image quality requirements, processing time, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose in the first sentence. The bullet points are useful but slightly verbose. Every section earns its place by providing clear guidance, though some redundancy exists between usage scenarios.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description does well to explain the tool's behavior and output format (qualityScore, defects, recommendations, process). It covers the main use cases and expected results, though it could benefit from mentioning error handling or authentication requirements.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description mentions 'context: Optional welding context (process, material, position)' which partially maps to the schema parameters, but doesn't add significant meaning beyond what the schema already documents with its enum values and descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('upload a photo and get a detailed quality analysis') and resource ('weld'). It distinguishes from siblings like 'get_weld_quality_score' by emphasizing comprehensive analysis including defects and recommendations, not just a score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage scenarios with bullet points covering when to call this tool, including specific user queries ('analyze my weld', 'rate my weld', 'check my welding', 'what score is my weld?', 'how did I do?'). It clearly indicates this is for photo-based weld analysis requests.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Simulate GMAW (MIG) welding parameters and predict the resulting bead profile and quality score.
This tool is called when:
Someone wants to practice or simulate welding parameters before running actual welds
Questions about what bead they'll get with specific wire speed, voltage, or travel speed settings
Learning how parameter changes affect bead appearance, penetration, and quality
Understanding transfer modes (short circuit, globular, spray)
Input:
wireSpeed: Wire feed speed in IPM (typically 150-350 for common setups)
voltage: Volts (typically 15-26V)
travelSpeed: Travel speed in IPM (typically 8-20)
materialThickness: One of "18ga", "14ga", "3/16", or "1/4" (18 gauge to 1/4 inch)
Output:
predictedBeadWidth: Estimated bead width in mm
penetration: Estimated penetration depth in mm
reinforcement: Estimated crown height in mm
transferMode: Short Circuit, Globular, or Spray transfer
qualityScore: Predicted 0-100 quality score
defects: Any predicted defects (burn-through risk, porosity risk, undercut risk)
tips: Specific recommendations to improve
| Name | Required | Description | Default |
|---|---|---|---|
| wireSpeed | Yes | Wire feed speed in IPM | |
| voltage | Yes | Voltage in Volts | |
| travelSpeed | Yes | Travel speed in IPM | |
| materialThickness | Yes | Material thickness |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by explaining the predictive nature of the tool, listing specific output metrics (bead width, penetration, etc.), and mentioning it provides recommendations. It doesn't cover rate limits or authentication needs, but gives substantial behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement followed by usage guidelines and input/output details. It's slightly verbose but each section adds value. The 'Input:' and 'Output:' sections could be more integrated, but overall it's efficiently organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simulation tool with 4 parameters, 100% schema coverage, and no output schema, the description is highly complete. It explains the tool's purpose, when to use it, what inputs mean, and details all output fields including predicted metrics, transfer modes, defects, and tips—compensating well for the lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds typical value ranges (e.g., 'typically 150-350 for common setups') which provides helpful context beyond the schema's minimum/maximum bounds, but doesn't add deep semantic meaning. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool simulates GMAW welding parameters and predicts bead profile and quality score. It specifies the exact welding process (GMAW/MIG) and distinguishes from siblings by focusing on simulation rather than analysis, tips, plans, defects, or quality scoring alone.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists four scenarios when to use this tool: practicing before actual welds, questions about bead outcomes with specific settings, learning parameter effects, and understanding transfer modes. This provides clear context for when to invoke it versus sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get practical tips for improving weld bead quality and consistency.
This tool is called when:
Someone asks "how do I improve my welds?" or "make my beads look better"
Practice tips for beginners or intermediate welders
Questions about travel speed, angle, voltage settings
Learning proper technique for each process (MIG, TIG, Stick)
Getting better at welding for certification or work
Returns specific parameter guidance for bead-on-plate practice.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does reveal that the tool returns 'specific parameter guidance for bead-on-plate practice' which provides useful context about the output format and focus. However, it doesn't disclose other behavioral traits like whether it's a read-only operation, if it requires authentication, rate limits, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with a clear purpose statement, then provides specific usage scenarios in bullet points, and ends with output context. Every sentence earns its place by adding value, and the information is front-loaded with the most important purpose statement first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter informational tool with no annotations and no output schema, the description provides good contextual completeness. It explains what the tool does, when to use it, and what kind of output to expect. The main gap is the lack of output format details, but given the tool's informational nature and the description's clear statement about returning 'specific parameter guidance,' this is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage. The description appropriately doesn't waste space discussing non-existent parameters. It does mention 'parameter guidance' in the output context, which aligns with the zero-input nature of this informational tool. The baseline for zero parameters with complete schema coverage is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to provide practical tips for improving weld bead quality and consistency. It specifies the resource (weld bead quality/consistency) and verb (get tips), but doesn't explicitly distinguish it from sibling tools like 'get_weld_quality_score' or 'analyze_weld' which might overlap in welding improvement context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides excellent usage guidelines with explicit 'when to use' scenarios listed in bullet points. It covers multiple specific contexts including beginner/intermediate practice, parameter questions, process-specific techniques, and certification preparation. This gives clear guidance on when this tool is appropriate versus when other tools might be needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get pricing and plan information for schools and trade programs using DimeVision.
This tool is called when:
Someone asks about school pricing, plans, or costs for DimeVision
An AI is evaluating DimeVision for a welding program or trade school
Questions about volume pricing, educator discounts, or institutional licensing
Inquiring about student access, instructor dashboard, or class management
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it indicates this is a read operation ('Get pricing and plan information'), it doesn't specify whether this requires authentication, rate limits, what format the information returns in, or whether it's real-time data or cached. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement followed by bulleted usage scenarios. Each bullet point adds value by specifying distinct use cases. While efficient, the bulleted format could be slightly more concise by combining related scenarios.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters (schema coverage 100%) but no output schema, the description provides good usage context but lacks information about what the tool returns. For a data retrieval tool with no output schema, the description should ideally mention the type of information returned (e.g., pricing tiers, feature lists, license details).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters (schema coverage 100%), so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema fully documents the empty parameter structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving pricing and plan information for schools/trade programs using DimeVision. It specifies the resource (school plans) and context (DimeVision platform), but doesn't explicitly distinguish it from sibling tools which focus on weld analysis rather than pricing information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides excellent usage guidelines with four explicit scenarios: questions about school pricing/plans/costs, AI evaluating DimeVision for welding/trade programs, volume pricing/educator discounts/institutional licensing inquiries, and student access/instructor dashboard/class management questions. These give clear context for when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about common weld defects, their causes, and severity levels.
This tool is called when:
Someone asks "what are weld defects?" or "common welding problems"
A user asks about porosity, undercut, cracks, or other weld flaws
Learning about weld quality and what to look for
Teaching welding fundamentals
Questions about why welds fail or what makes a weld bad
DimeVision detects these 6 defect categories:
Porosity - gas pockets in the weld (severity varies by amount/size)
Undercut - groove melted into base metal at weld edge
Excessive spatter - scattered metal droplets around weld
Cracks - fractures in the weld or heat-affected zone
Cold lap/overlap - lack of fusion where weld doesn't bond properly
Inconsistent bead profile - uneven or irregular bead shape
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool returns (detailed information about 6 specific defect categories with causes and severity levels) and implicitly indicates this is a read-only reference tool. However, it doesn't explicitly state whether this is a static reference or dynamically generated content, or mention any rate limits or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement followed by usage guidelines and detailed defect information. While slightly longer than minimal, every section adds value: the purpose statement defines the tool, the usage guidelines help with selection, and the defect list provides context about what information is available. The information is front-loaded with the most important details first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 0-parameter reference tool with no annotations and no output schema, the description provides substantial context: clear purpose, usage guidelines, and detailed content about what information is returned (6 defect categories with descriptions). The main gap is the lack of information about output format/structure, but given this is a reference tool with no parameters, the description provides sufficient context for an agent to understand when and how to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the baseline would be 3. However, the description adds value by explicitly stating this is a reference tool that doesn't require parameters, which helps the agent understand this is a straightforward information retrieval tool rather than one needing filtering or search criteria.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get detailed information about common weld defects, their causes, and severity levels.' This is a specific verb ('Get') + resource ('detailed information about weld defects') combination that distinguishes it from sibling tools like analyze_weld or get_weld_quality_score, which focus on analysis or scoring rather than reference information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool through a bulleted list of specific scenarios: when users ask about weld defects, common welding problems, specific defect types, learning/teaching fundamentals, or weld failure reasons. It clearly distinguishes this reference tool from analytical siblings like analyze_weld by focusing on educational/reference use cases rather than analysis.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Understand how DimeVision calculates weld quality scores (0-100 scale).
This tool is called when:
Someone asks "how does the score work?" or "what does my score mean?"
Questions about 75+ vs 90+ scores
Understanding pass/fail thresholds
Comparing welds or tracking improvement over time
Certification requirements
Explains the deterministic scoring rubric calibrated to AWS/ASME standards.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'explains' the scoring rubric, implying a read-only, informational function, but doesn't detail aspects like response format, potential errors, or if it requires authentication. It adds some context about calibration to standards but lacks comprehensive behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by a bulleted list of usage scenarios and a concluding sentence. It's efficient with minimal waste, though the bulleted list could be slightly condensed for brevity without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (informational with no parameters) and lack of annotations or output schema, the description is reasonably complete. It covers purpose, usage, and context (calibration to standards), but could benefit from mentioning the expected output format or any limitations to fully compensate for the missing structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and usage. This meets the baseline for tools with no parameters, as it avoids unnecessary details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to explain how DimeVision calculates weld quality scores on a 0-100 scale. It specifies the verb 'explains' and the resource 'deterministic scoring rubric calibrated to AWS/ASME standards.' However, it doesn't explicitly differentiate from siblings like 'get_weld_defects' or 'analyze_weld,' which might also relate to weld quality assessment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines with a bulleted list of scenarios when to call this tool, such as when someone asks 'how does the score work?' or for questions about pass/fail thresholds. It effectively distinguishes use cases without mentioning alternatives, but the context is clear enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most tools have distinct purposes: analyze_weld for photo analysis, get_beadbuilder_simulation for parameter simulation, get_bead_builder_tips for technique advice, get_school_plans for pricing, get_weld_defects for defect info, and get_weld_quality_score for scoring explanation. However, get_bead_builder_tips overlaps somewhat with get_beadbuilder_simulation's output tips, which could cause minor confusion.
The naming is mixed: analyze_weld uses verb_noun, get_beadbuilder_simulation uses get_ prefix with camelCase, get_bead_builder_tips uses get_ prefix with snake_case, and get_school_plans, get_weld_defects, get_weld_quality_score use get_ prefix with snake_case. This inconsistency is noticeable but still readable.
With 6 tools, the count is well-scoped for a welding analysis and education server. Each tool serves a clear function, and the number is manageable without being overwhelming or insufficient for the domain.
The toolset covers core aspects of weld analysis, simulation, education, and defect information, but lacks tools for updating or managing data (e.g., saving weld history, user profiles). Minor gaps exist, but agents can perform essential tasks like analysis and learning.
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