ecommerce-custom-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation2/5
add_product and add_product_smart overlap significantly since both create a product, differing only by optional AI description generation. An agent could easily select the wrong one. The other tools are distinct enough.
Naming Consistency4/5Names mostly follow a clear verb_noun snake_case pattern: add_product, get_product_by_id, delete_product. add_product_smart is a minor deviation but remains readable and predictable.
Tool Count4/5Four tools is a reasonable size for a focused product management server. However, add_product_smart feels like a variant rather than a distinct capability, so the set is slightly less tight than it could be.
Completeness3/5The server covers create, read, and delete for products, but misses obvious update and list/search operations. Agents can do basic workflows but will hit dead ends when trying to modify products or view all products.
Average 3.6/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
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
- Behavior2/5
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 states the mutation ('Create') but does not disclose side effects such as duplicate SKU handling, idempotency, permission requirements, or whether existing data is affected. The agent knows it is a write operation but nothing about its behavioral edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the action and efficiently lists all fields in a compact order. No redundant words, no filler. Every part contributes to conveying the core operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter create tool with no annotations and no sibling differentiation, the description is minimally viable but incomplete. It correctly identifies the operation and fields, and an output schema exists to document return values. However, it lacks critical operational context such as duplicate handling, required permissions, or when to prefer the sibling add_product_smart.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It lists all five parameters and marks description as optional, which clarifies the confusing optionality expressed in the schema's convoluted anyOf. However, it adds no deeper semantic meaning beyond the parameter names, and the schema already defines types and constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Create') and resource ('a new product'), and enumerates the key fields (sku, name, optional description, price, quantity). It is unambiguous about the operation, but it does not differentiate from the sibling tool add_product_smart, both of which appear to create products.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 add_product_smart or how it differs from get_product_by_id or delete_product. There is no mention of conditions, alternatives, or exclusions, leaving the agent to infer usage from the tool name alone.
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 of explaining behavior. It discloses the key non-obvious trait: a missing description will be AI-generated. It does not discuss failure modes or side effects, but the core create behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that front-loads the action and then adds the key qualifier. There is no filler or redundant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple and an output schema exists, but the description does not differentiate add_product_smart from add_product or clarify behavior when a description is actually supplied. These are meaningful gaps for a tool with no annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning for the description parameter by stating it is optional and auto-generated when missing, but the other parameters (sku, name, price, quantity) rely on their self-explanatory names without additional context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create a new product') and the distinguishing behavior ('smart ai generated description if missing'). It does not explicitly contrast with the sibling add_product tool, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'if missing' implies that this tool is useful when a description is not provided, which is a light usage cue. However, it never mentions alternatives like add_product or explains when the plain version should be preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It states the delete action and the return value, but it does not mention irreversibility, idempotency, behavior for non-existent ids, or permission requirements, which is a significant gap for a destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no filler. The action is front-loaded and the return type is given directly, making it concise and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple single-parameter destructive call with an output schema, but the description omits behavior for non-existent ids (e.g., false vs error), permanence, and side effects. For an agent to confidently invoke it on real data, this is only partially complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. Saying 'by id' clarifies that the sole parameter 'id' refers to the product identifier, adding meaning beyond the bare schema. This fully covers the parameter's role, though it doesn't discuss id sourcing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Delete') and a resource ('product by id'), making the operation unambiguous. It clearly distinguishes itself from siblings like add_product and get_product_by_id, and the return value is also explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for deleting a product, but provides no explicit guidance about when to choose this tool over alternatives or any exclusions. With siblings like get_product_by_id, an agent must infer that delete is intended for removal, which is straightforward but not directly stated.
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 provided, the description carries the full behavioral disclosure burden. 'Fetch' clearly conveys a read-only operation, which is helpful, but it does not mention behavior for missing IDs, error responses, or potential side effects. This is adequate but not deeply transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. 'Fetch a single product by numeric ID' efficiently states the action, target, and key constraint, earning every word.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single parameter and an existing output schema, the description plus schema provides the essential information for a correct call. Minor gaps like error behavior and explicit usage routing remain, but they do not block an agent from invoking this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema documentation coverage is 0%, so the description must compensate for the parameter 'id'. It only adds 'numeric ID', which essentially restates the schema's integer type. It does not explain what the ID represents or provide any meaning beyond the property name and schema constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Fetch') with a clear resource ('product') and a clear qualifier ('single', 'by numeric ID'), which makes it immediately distinct from sibling tools like add_product and delete_product. An agent can tell this is a read-by-ID operation without needing to inspect schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through the contrast between 'Fetch' and the sibling add/delete tools, but it never explicitly states when this tool should be chosen or when another tool would be more appropriate. The guidance is present only by inference, not direct routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/santhansai11/MCPmerce'
If you have feedback or need assistance with the MCP directory API, please join our Discord server