sellercloud-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct resource or action: product, inventory, order, channel listing, channels list, and two searches. No overlap in purpose.
Naming Consistency5/5
All tools follow a consistent `sellercloud_verb_noun` pattern (get_, list_, search_), with clear verb choice for the operation type.
Tool Count5/5
7 tools is well-scoped for a seller cloud integration covering core data retrieval: product, inventory, order, and channel information.
Completeness3/5
The tool set is read-only, covering get and search for products, orders, inventory, and channels. Missing update/create operations that agents might need for full workflow support.
Average 4.1/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under MIT License.
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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?
No annotations provided, so description bears full burden. It discloses that it returns null if order not found, but does not mention authentication requirements, rate limits, or any side effects. Minimal behavioral info.
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?
Extremely concise: four lines total. Front-loaded with purpose, then structured parameter and return descriptions. No redundant sentences.
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 simple tool with 1 parameter and an output schema, the description covers the core functionality, parameter meaning, and return behavior. Missing usage guidance and behavioral details (rate limits, auth) but acceptable given low complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (no param descriptions in schema). Description adds 'SellerCloud OrderID (integer)', which gives context beyond the schema's 'Order Id' title and integer type. However, it does not elaborate on format or possible values.
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?
Clearly states 'Fetch full order detail including line items.' Identifies specific verb and resource. Distinguishes from siblings like sellercloud_search_orders (search vs fetch) and sellercloud_get_product (different resource).
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?
Does not explicitly state when to use this tool vs alternatives. Usage is implied by requiring an order_id, but no guidance on context (e.g., use when you have an ID, vs search_orders when you don't).
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?
Describes return structure as 'JSON envelope with data.channels'. Given no annotations, it adds some context (scoping lookup by company_id) but lacks details on side effects, rate limits, or behavior with null company_id.
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?
Two sentences in a clean Args/Returns format. No superfluous information.
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 simple list operation, the description covers purpose, parameter role, and return structure. Lacks only minor details like read-only hint or error cases.
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?
Only parameter 'company_id' is explained as 'CompanyID scoping the lookup'. Adds meaningful context beyond the schema title, compensating for 0% schema description coverage.
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?
Clearly states 'List configured channel feeds for the given company'. Verb (List) and resource (channel feeds) are specific. Differentiates from sibling 'sellercloud_get_channel_listing' by being a list operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. No mention of when not to use or when to prefer a sibling like 'sellercloud_get_channel_listing'.
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?
Despite no annotations, the description discloses the return format (JSON envelope with data field) and the null case when SKU is not listed. This provides adequate transparency for a read-only operation.
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 extremely concise, using three sentences to convey purpose, parameters, and return value. Every sentence is meaningful and front-loaded.
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 an output schema exists, the description sufficiently covers the tool's functionality, input requirements, and output behavior (including null). It is complete for the task of selecting and invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description carries the burden. It reiterates the types (integer, exact SKU) which matches the schema, but does not add additional meaning or constraints beyond what is obvious from names and types.
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 clearly states it fetches the per-channel listing record for a single SKU, using a specific verb and resource. It distinguishes itself from sibling tools like sellercloud_get_inventory or sellercloud_get_product by focusing on channel listings.
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 implies usage for retrieving a listing by channel and SKU, but does not explicitly state when not to use it or mention alternative tools. No guidance on prerequisites or context is provided.
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 carries the full burden. It discloses the return format as a JSON envelope and the possibility of null for missing SKU, but lacks details on idempotency, side effects, or rate limits.
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 concise with a structured Args and Returns section, front-loaded with the main purpose, and no unnecessary words.
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?
Given the tool's low complexity and the presence of an output schema, the description adequately covers purpose, parameters, and return shape. However, it could mention the absence of pagination or limits on the record size.
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 has 0% description coverage, so the description compensates by clarifying 'sku' as 'Exact SKU / ProductID' and 'company_id' as 'CompanyID scoping the lookup', adding meaning beyond the raw types.
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 uses the verb 'Fetch' and specifies the resource as 'full catalog record for a single SKU', clearly distinguishing it from sibling tools like 'sellercloud_search_products' which implies search functionality.
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 states 'for a single SKU', implying use when exact SKU is known. However, it does not explicitly mention when not to use or compare with alternatives like 'sellercloud_get_channel_listing'.
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 adequately discloses behavioral traits: pagination is handled transparently, limit caps (default 200, max 1000), default company behavior for company_id. This goes beyond basic parameter names.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a brief purpose, clear args list, and returns section. It is slightly verbose but every sentence adds value. No superfluous content.
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?
Given 5 parameters and an output schema, the description covers date range, optional filters, pagination, limit, and return envelope. It could mention error handling or rate limits, but overall it is sufficiently complete for effective tool use.
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 fully explain parameters. It does: date_from/date_to with ISO format, company_id with default behavior, query as free-text filter, limit with default and max. This adds significant meaning beyond the schema 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 clearly states 'Search orders created in the inclusive [date_from, date_to] window.' This specifies the verb (search), resource (orders), and scope (date range), distinguishing it from sibling tools like sellercloud_get_order (single order) and sellercloud_search_products (product search).
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 implies usage for browsing orders within a date window but does not explicitly state when to use this tool over alternatives like sellercloud_get_order or sellercloud_search_products. No when-not guidance or exclusions are provided.
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 full burden. It reveals behavioral details like page_size max 50 enforced server-side, pagination behavior, and the response envelope. It does not mention authentication, rate limits, or case sensitivity, but for a search tool the provided info is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured using bullet points for parameters and a clear Returns section. It is concise but could be slightly more compact (e.g., removing the docstring formatting overhead). Still, every sentence adds value.
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?
Given the tool has 4 parameters, no annotations, and an output schema (though not provided), the description covers parameter semantics, pagination, and return format. It lacks cross-referencing to siblings or mention of authorization, but overall it is complete for a search operation.
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?
The schema has 0% description coverage, so the description must compensate. It does so excellently by explaining that query is free-text, company_id has a fallback default, page is 1-indexed, and page_size is capped at 50. This adds substantial meaning beyond the schema's type-only definitions.
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 clearly states 'Search the SellerCloud catalog by name, SKU, or attribute,' providing a specific verb and resource. It distinguishes itself from sibling tools like sellercloud_get_product (single product retrieval) and sellercloud_search_orders (different entity), making the tool's role unambiguous.
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 implies usage for general product searching but does not explicitly state when to avoid it or compare it to siblings like sellercloud_get_channel_listing or sellercloud_get_inventory. However, the parameter descriptions give clear context on how to use the tool effectively.
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?
No annotations are provided, so the description must cover behavioral traits. It states 'Fetch,' implying a read-only operation, and describes the return format including null for absent records. It does not mention authorization or side effects, but the context suggests a safe read.
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 highly concise with no wasted words. It front-loads the core action in the first sentence, then efficiently lists parameters and return values in a structured format.
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?
For a simple tool with two parameters and an output schema (though not explicitly shown), the description fully explains the purpose, parameters, and return value. It covers edge cases (null if absent) and key fields in the response.
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?
Schema coverage is 0% meaning no descriptions in the schema. The description adds meaning: 'sku' is described as 'Exact SKU / InventoryID' and 'company_id' as 'CompanyID scoping the lookup,' which clarifies their roles beyond the type-only schema.
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 clearly states it fetches inventory for a single SKU, using a specific verb ('Fetch') and resource ('inventory record'). This distinguishes it from sibling tools like sellercloud_get_order or sellercloud_get_product, which target different entities.
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 specifies it's for a single SKU and lists required parameters. However, it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. The purpose is clear enough for an agent to infer usage.
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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