Synplex provides inventory health scoring, landed cost calculation, and quick inventory diagnosis tools for Shopify merchants. It enables AI assistants to assess stock performance, calculate true product costs including duties and shipping, and instantly surface inventory issues.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.4/5 across 3 of 3 tools scored. Lowest: 3.7/5.
Each tool has a clearly distinct purpose: precise inventory scoring, estimated inventory scoring, and landed cost calculation. The quick_inventory_diagnosis description explicitly differentiates it from inventory_health_score, removing any ambiguity.
All tool names use snake_case and follow a consistent descriptive noun-phrase pattern (e.g., inventory_health_score, landed_cost_calculator). While they don't use a verb_noun convention, the style is uniform and predictable.
Three tools is well-scoped for the server's purpose of providing inventory health diagnostics and landed cost analysis. Each tool covers a distinct need without unnecessary overlap or bloat.
The surface covers the core domain: precise and estimated health scores plus a detailed cost calculator. For this analytics-focused server, there are no obvious missing operations or dead ends.
inventory_health_scoreAInspectCalculates a comprehensive inventory health score for a Shopify merchant. Returns GMROI, annual turnover, days inventory outstanding, dead stock exposure %, carrying cost leakage, a 0–100 composite score with per-dimension breakdown (gmroi/turnover/dead_stock), benchmark comparisons by industry segment, risk flags, plain-language diagnosis, and prioritised recommended actions.
| Name | Required | Description | Default |
|---|---|---|---|
| segment | Yes | ||
| annualRevenue | Yes | ||
| grossMarginPct | Yes | ||
| deadStockCostValue | Yes | ||
| targetWeeksOfCover | Yes | ||
| avgInventoryCostValue | Yes | ||
| annualCarryingCostRate | Yes | ||
| deadStockThresholdDays | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It extensively details what the tool returns: GMROI, turnover, DIO, dead stock %, carrying cost leakage, composite score, benchmarks, risk flags, diagnosis, and recommended actions. This gives the agent a clear picture of the tool's behavior and outputs, though it does not explicitly state side effects (e.g., whether it is read-only) or data access requirements, which are assumed for a calculation tool.
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 a single sentence that is front-loaded with the core action and then enumerates outputs. It is efficient, with no fluff, but it is dense and might be easier to parse if broken into shorter sentences. Every phrase adds value, but the overall structure is a long run-on, slightly harming readability.
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?
The description lists numerous output dimensions, giving substantial coverage of what the tool returns, but leaves gaps: it does not explain how the composite score is calculated, what benchmark sources are used, or how to interpret the risk flags. It also omits usage context (when to choose this over quick_inventory_diagnosis) and parameter semantics. Given the tool's complexity (8 params, no output schema), the description is moderately complete but not fully self-sufficient.
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 8 required parameters with 0% schema_description_coverage (no parameter descriptions in the schema). The tool description also fails to explain any parameters—it only lists outputs. Since the description does not compensate for the lack of parameter semantics, the agent must rely solely on names, types, and constraints, which are insufficient for understanding how each input affects the calculation.
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 function: 'Calculates a comprehensive inventory health score for a Shopify merchant.' It specifies a concrete verb ('calculates'), a resource ('inventory health score'), and lists detailed outputs (GMROI, turnover, DIO, dead stock exposure, etc.), distinguishing it from siblings like quick_inventory_diagnosis by emphasizing 'comprehensive' and the extensive result set.
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 implies the tool is for in-depth health analysis but does not explicitly state when to use it versus alternatives. It provides no references to sibling tools (landed_cost_calculator, quick_inventory_diagnosis) or exclusions. The word 'comprehensive' hints at a use case, but concrete guidance is missing, qualifying as implied usage rather than explicit directives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
landed_cost_calculatorAInspectCalculates total landed cost per unit for a Shopify merchant importing goods. Applies 2026 regulatory surcharges (US Section 122 +10%, EU de minimis €3/item), incoterm responsibility adjustments (EXW/FOB/DDP), CIF insurance, duty and brokerage, and financial carry cost over lead time. Returns unitLandedCost, costUpliftRatio, marginErosionPct (only when unitSellingPrice is provided), full cost breakdown by layer, sensitivity drivers, risk warnings, and recommended actions. Pass baseDutyRate as a decimal (e.g. 0.12 for 12%) — surcharges are applied automatically.
| Name | Required | Description | Default |
|---|---|---|---|
| incoterm | Yes | ||
| quantity | Yes | ||
| unitCost | Yes | ||
| destination | Yes | ||
| baseDutyRate | Yes | ||
| brokerageFee | Yes | ||
| leadTimeDays | Yes | ||
| insuranceRate | Yes | ||
| originCountry | Yes | ||
| originPortFees | Yes | ||
| freightCostTotal | Yes | ||
| unitSellingPrice | No | ||
| annualCostOfCapital | Yes | ||
| destinationPortFees | Yes | ||
| itemsPerConsignment | Yes | ||
| isMarketplaceCollected | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: regulatory surcharges, incoterm adjustments, CIF insurance, duty/brokerage, financial carry, and conditional output (marginErosionPct only when unitSellingPrice is provided). It also lists exact return fields, making the tool's behavior highly transparent.
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 a single, well-organized paragraph that front-loads the purpose, then explains calculation layers, then lists outputs, and ends with a critical parameter tip. Every sentence adds value and no information is wasted.
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?
The tool has 16 parameters, no output schema, and no annotations; the description compensates by explaining the full scope of calculations, conditional outputs, and return content. It provides enough context for an agent to invoke it correctly and interpret results, while parameter details are available in the 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 0%, so the description must add parameter meaning. It explicitly explains baseDutyRate format ('Pass baseDutyRate as a decimal'), notes automatic surcharge application, and mentions incoterm types and destination-specific behavior. While not every parameter is elaborated, the most non-obvious ones are addressed and schema names carry the rest.
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 uses a specific verb ('Calculates') and clearly identifies the resource ('total landed cost per unit for a Shopify merchant importing goods'). This distinguishes it from sibling tools (inventory_health_score, quick_inventory_diagnosis), which are inventory-focused rather than cost-calculation-focused.
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 clearly states the context for use (importing goods with Shopify, applying regulatory and incoterm adjustments), but it does not explicitly mention when not to use it or name alternative tools. Since siblings are unrelated, this clear contextual framing earns a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
quick_inventory_diagnosisAInspectLow-friction inventory health estimate for Shopify merchants. Use this when the merchant doesn't have precise inventory figures — requires only monthly revenue, SKU count, and industry segment. Inventory value and dead stock are estimated from industry benchmarks; all assumptions are returned transparently. Returns a 0–100 health score, risk flags, plain-language diagnosis, and prioritised recommended actions. Ideal for AI-assisted lead qualification and first-contact diagnostics. For a precise score using actual inventory figures, use inventory_health_score instead.
| Name | Required | Description | Default |
|---|---|---|---|
| segment | Yes | Industry segment — used to select benchmark targets | |
| skuCount | Yes | Number of active SKUs | |
| grossMarginPct | No | Gross margin % (0–100). Uses industry default if not provided. | |
| monthlyRevenue | Yes | Monthly revenue in USD |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that inventory value and dead stock are 'estimated from industry benchmarks,' that assumptions are 'returned transparently,' and it enumerates the output contents (score, flags, diagnosis, actions). This gives a clear picture of a read-only diagnostic operation with no hidden side effects.
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 four sentences, front-loaded with the core purpose, then usage, outputs, and alternative. Every sentence earns its place with no redundancy or filler, making it appropriately sized and easy to scan.
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 output schema, the description comprehensively covers return values (0–100 health score, risk flags, diagnosis, actions) and the estimation methodology. It also clarifies when to use this tool versus the alternative, making it complete for the tool's complexity.
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 reinforces which parameters are required ('requires only monthly revenue, SKU count, and industry segment') but doesn't add additional semantic detail beyond what the schema already provides for each parameter.
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: 'Low-friction inventory health estimate for Shopify merchants.' It specifies the verb 'estimate' and the resource 'inventory health,' and explicitly differentiates from the sibling tool inventory_health_score by noting 'For a precise score using actual inventory figures, use inventory_health_score instead.'
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?
Provides explicit when-to-use guidance: 'Use this when the merchant doesn't have precise inventory figures.' It also names the alternative tool for precise figures and adds an ideal use case ('AI-assisted lead qualification and first-contact diagnostics'), making the usage context unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
