123elec-mcp
Server Details
Interface MCP officielle du site marchand 123elec, catalogue, stock, panier, commandes.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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.
Tool Definition Quality
Average 3/5 across 5 of 5 tools scored.
Each tool has a clearly distinct purpose with no overlap: check_stock verifies inventory, get_categories lists categories, get_product retrieves product details, get_recommendations provides suggestions, and search_products enables keyword searches. The descriptions clearly differentiate their functions, making misselection unlikely.
All tool names follow a consistent verb_noun pattern using snake_case: check_stock, get_categories, get_product, get_recommendations, and search_products. This predictable naming convention enhances readability and usability across the tool set.
With 5 tools, this server is well-scoped for an e-commerce domain. Each tool earns its place by covering essential operations like inventory checks, product retrieval, recommendations, and searches, without being overly sparse or bloated.
The tool set covers core e-commerce workflows such as browsing, searching, and recommendations, but lacks CRUD operations for managing products or categories (e.g., create_product, update_category). Agents can work around these gaps, but they represent minor omissions in lifecycle coverage.
Available Tools
5 toolscheck_stockCInspect
Vérifie le stock de plusieurs produits
| Name | Required | Description | Default |
|---|---|---|---|
| skus | Yes | Liste des SKUs |
Tool Definition Quality
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. It states the action ('vérifie') but doesn't describe what 'checking stock' entails operationally—whether it's a read-only query, requires authentication, has rate limits, returns real-time vs cached data, or what happens on errors. The description is minimal and lacks 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 a single, efficient sentence in French ('Vérifie le stock de plusieurs produits') with zero waste. It's appropriately sized and front-loaded, clearly stating the core function without unnecessary elaboration.
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 (a read operation with one parameter) and lack of annotations/output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., read-only nature, error handling) or output details (e.g., stock levels, formats), leaving gaps for an agent to infer usage correctly.
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 schema description coverage is 100%, with the parameter 'skus' documented as 'Liste des SKUs'. The description adds no additional meaning beyond this, as it doesn't explain SKU format, constraints (e.g., max items), or how stock is returned. Baseline 3 is appropriate since the schema does the heavy lifting.
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 'Vérifie le stock de plusieurs produits' clearly states the tool's purpose (checking stock for multiple products) with a specific verb ('vérifie') and resource ('stock de plusieurs produits'). It distinguishes from siblings like get_product (single product) and search_products (searching), but doesn't explicitly contrast with get_categories or get_recommendations.
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 alternatives. It doesn't mention prerequisites, when-not-to-use scenarios, or compare with sibling tools like get_product (for single products) or search_products (which might include stock info). Usage is implied but not articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_categoriesBInspect
Liste hiérarchique des catégories
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
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. While 'Liste' implies a read operation, it doesn't specify whether this is a safe, read-only function, potential side effects, authentication needs, rate limits, or return format. The description adds minimal behavioral context beyond the basic action.
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, efficient phrase ('Liste hiérarchique des catégories') that conveys the core purpose without unnecessary words. It's appropriately sized for a simple tool and front-loaded with key information, making it highly concise and well-structured.
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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It specifies the hierarchical nature of categories, which adds value, but lacks details on behavioral traits, usage context, or output format. For a read-only list tool, this is minimally viable but not fully comprehensive.
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, and the schema description coverage is 100% (though empty). The description doesn't need to add parameter semantics, as there are none to document. A baseline of 4 is appropriate since no parameters exist, and the description doesn't mislead about inputs.
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 'Liste hiérarchique des catégories' clearly states the tool's purpose as listing hierarchical categories. It uses a specific verb ('Liste') and resource ('catégories'), and the adjective 'hiérarchique' adds useful detail about the structure. However, it doesn't explicitly distinguish this from sibling tools like 'get_product' or 'search_products', which prevents a perfect 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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_product' or 'search_products', nor does it specify contexts where listing categories is appropriate (e.g., for navigation vs. product lookup). This leaves the agent without explicit usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_productCInspect
Détails complets d'un produit par SKU
| Name | Required | Description | Default |
|---|---|---|---|
| sku | Yes | SKU du produit | |
| store | No | store_id Magento (contexte tarifaire B2C/B2B) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as read-only nature, authorization requirements, rate limits, or side effects. For a data retrieval tool, additional transparency is expected.
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 concise sentence in French, matching the tool's purpose. It earns its place with zero redundancy, though it could include more information without becoming verbose.
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 2 parameters, no output schema, and no annotations, the description is incomplete. It does not explain what 'complete details' entails, return format, or edge cases. For a simple product lookup, more context is needed.
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 adequately describes both parameters (sku and store). The description adds minimal value beyond 'by SKU', which is already indicated by the schema. Baseline score of 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 states 'Complete details of a product by SKU', which clearly indicates the tool retrieves full product information for a given SKU. It differentiates from sibling tools like search_products (search) or check_stock (stock) but does not explicitly contrast them.
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?
No guidance is provided on when to use this tool vs alternatives (e.g., search_products for broader queries, check_stock for inventory). The description lacks context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_recommendationsCInspect
Obtient des recommandations de produits basées sur un panier (modèle Transformer WattHelper)
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | Nombre de recommandations à retourner (1-10) | |
| basket | Yes | Liste des SKUs dans le panier |
Tool Definition Quality
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 the model ('Transformer WattHelper'), which hints at AI-based recommendations, but doesn't describe key behaviors such as response format (e.g., list of SKUs with scores), potential latency, rate limits, or error conditions. For a tool with no annotations, this is a significant gap in transparency.
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, efficient sentence that directly states the tool's purpose and includes the model name for added context. It's front-loaded with the core function and avoids unnecessary details. However, it could be slightly more structured by separating usage hints, but it's concise and to the point.
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 complexity of a recommendation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., a list of recommended products with metadata), how recommendations are generated, or any prerequisites (e.g., valid SKUs). For a tool that likely returns structured data, this leaves critical gaps for an agent to use it effectively.
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%, with clear descriptions for both parameters: 'k' as the number of recommendations and 'basket' as a list of SKUs. The description adds minimal value beyond this, only implying that recommendations are based on the basket. It doesn't provide additional semantics like basket format examples or how the model uses the SKUs. Baseline 3 is appropriate since the schema does the heavy lifting.
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: 'Obtient des recommandations de produits basées sur un panier' (Gets product recommendations based on a basket). It specifies the verb ('obtient') and resource ('recommandations de produits'), and mentions the model ('modèle Transformer WattHelper'), which adds specificity. However, it doesn't explicitly differentiate from sibling tools like 'search_products' or 'get_product', which could also be used for product-related queries, so it doesn't reach a 5.
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 alternatives. It doesn't mention scenarios like when a user has items in a basket and wants suggestions, or contrast it with siblings such as 'search_products' for general queries or 'get_product' for specific product details. Without any context on usage, it leaves the agent to infer based on the name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_productsBInspect
Recherche de produits par mot-clé avec pagination
| Name | Required | Description | Default |
|---|---|---|---|
| store | No | store_id Magento (contexte tarifaire B2C/B2B) | |
| search | Yes | Terme de recherche | |
| pageSize | No | Résultats par page (max 50) | |
| currentPage | No | Numéro de page |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states the operation type (search with pagination) but does not disclose key behaviors such as read-only nature, authentication needs, rate limits, or any 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 a single sentence with no wasted words. It is appropriately sized and front-loaded.
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 lack of output schema and annotations, the description should provide more context about the return value (e.g., list of products, total count) or behavioral details. It is minimal and incomplete for a search tool with 4 parameters.
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 coverage is 100%, so the baseline is 3. The description adds the word 'pagination' which hints at pageSize and currentPage, but does not provide meaningful new semantics beyond what the parameter descriptions already convey.
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 verb (Recherche) and resource (produits) and includes the pagination aspect, which distinguishes this tool from siblings like get_product (single product) or get_categories.
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?
No usage guidelines provided. The description does not indicate when to use this tool versus alternatives, nor does it list prerequisites or exclusions.
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.
Discussions
No comments yet. Be the first to start the discussion!