synplex-mcp

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 clearly indicates this is a calculation/analysis tool (implied read-only, non-destructive), and details the comprehensive output including scores, benchmarks, and recommendations. However, it lacks information on performance characteristics, error handling, 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 efficiently structured as a single, information-dense sentence that front-loads the core purpose and then enumerates all output components. Every element serves a clear purpose with zero wasted words, making it easy for an agent to parse the tool's capabilities.

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 complex calculation tool with 8 required parameters, 0% schema coverage, and no output schema, the description does an excellent job detailing the comprehensive output but fails to address the input parameters. The lack of parameter semantics creates a significant gap in contextual understanding despite the rich output description.

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?

With 0% schema description coverage for 8 required parameters, the description provides no information about what the parameters mean or how they relate to the calculation. While it mentions output metrics like GMROI and dead stock exposure, it doesn't connect these to input parameters like 'grossMarginPct' or 'deadStockCostValue', leaving significant semantic gaps.

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 verbs ('calculates', 'returns') and resources ('inventory health score for a Shopify merchant'), and distinguishes it from siblings by mentioning comprehensive metrics like GMROI, turnover, and dead stock exposure that aren't implied in sibling tool names like 'landed_cost_calculator' or 'quick_inventory_diagnosis'.

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 no guidance on when to use this tool versus the sibling tools ('landed_cost_calculator' and 'quick_inventory_diagnosis'), nor does it mention any prerequisites, exclusions, or alternative contexts for usage, leaving the agent to infer based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 disclosing multiple behavioral aspects: it describes the complex calculation logic (regulatory surcharges, incoterm adjustments, cost components), output structure (unitLandedCost, breakdowns, warnings), and specific formatting instructions for baseDutyRate. However, it doesn't mention error handling, performance characteristics, 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 efficiently structured with key information front-loaded (purpose and scope) followed by calculation details and output explanation. While comprehensive, every sentence adds value with no redundant information, though it could be slightly more concise in listing all output components.

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 complex 16-parameter calculation tool with no annotations or output schema, the description provides substantial context about calculation methodology, output structure, and parameter semantics. It adequately covers the tool's purpose and behavior, though additional details about error cases or performance expectations would make it fully 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?

With 0% schema description coverage for 16 parameters, the description provides crucial semantic context that compensates fully. It explains what 'baseDutyRate' represents and how to format it, clarifies that surcharges are applied automatically, and mentions the conditional nature of 'marginErosionPct' output when 'unitSellingPrice' is provided, adding significant value beyond the bare schema.

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 specific action ('calculates total landed cost per unit'), target user ('Shopify merchant importing goods'), and scope of calculation. It distinguishes this tool from inventory-focused siblings by focusing on import cost calculation rather than inventory analysis.

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 usage for import cost calculation scenarios but doesn't explicitly state when to use this tool versus alternatives. No guidance is provided about prerequisites, limitations, or comparison with sibling tools, leaving usage context somewhat implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 key behaviors: it's an estimation tool using benchmarks, returns transparent assumptions, and outputs a health score with risk flags and recommendations. However, it doesn't mention potential limitations like accuracy concerns or rate limits, which would be helpful for a tool without annotations.

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. Every sentence adds value: explaining when to use it, what it returns, how it works, and differentiation from alternatives. There's no wasted text or redundancy.

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 tool with no annotations and no output schema, the description does an excellent job explaining what the tool does, when to use it, and what it returns. It covers the estimation methodology and output components. The only minor gap is not explicitly describing the format of the returned data, though it lists the components.

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 thoroughly. The description adds some context by mentioning the three required inputs and that gross margin uses a default if not provided, but doesn't provide additional semantic meaning beyond what's in the schema. This meets the baseline for high schema coverage.

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 action ('estimate'), resource ('inventory health'), and scope ('Shopify merchants'), and distinguishes it from the sibling tool 'inventory_health_score' by emphasizing it's for when precise figures aren't available.

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 ('when the merchant doesn't have precise inventory figures') and when to use an alternative ('For a precise score using actual inventory figures, use inventory_health_score instead'). It also mentions ideal use cases ('AI-assisted lead qualification and first-contact diagnostics').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.