Digikala MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
The tools have mostly distinct purposes, with clear boundaries: get_optimized_keywords_and_categories for keyword/category optimization, get_product_details for detailed product info, get_product_recommendations for similar products, search_products for category-based searches, and search_text_lenz for semantic visual searches. However, search_products and search_text_lenz both involve searching, which could cause minor confusion, though their descriptions clarify different use cases (category-based vs. semantic/visual).
Naming Consistency4/5
Tool names follow a consistent verb_noun pattern with underscores (e.g., get_optimized_keywords_and_categories, search_products), making them predictable and readable. There is a minor deviation with search_text_lenz, which includes a brand name 'Text-Lenz' in the noun part, but it still fits the overall naming convention without mixing styles like camelCase.
Tool Count5/5
With 5 tools, this server is well-scoped for an e-commerce product search and information domain. Each tool serves a specific function (e.g., optimization, details, recommendations, searches), and none feel redundant or unnecessary, providing a balanced set for typical agent workflows in this context.
Completeness3/5
The tool set covers key operations like searching, getting details, and recommendations, but there are notable gaps. For example, there are no tools for user account management (e.g., login, cart operations), purchase-related actions (e.g., add to cart, checkout), or content management (e.g., reviews, ratings). While the domain appears focused on product discovery and information, these omissions could limit agent functionality in full e-commerce scenarios.
Average 4.4/5 across 5 of 5 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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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate this is a read-only, non-destructive, open-world operation. The description adds valuable behavioral context: the API responds differently to Persian vs. English queries, with Farsi yielding better results. This language-specific behavior isn't covered by annotations, enhancing transparency about how the tool behaves in practice.
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 and well-structured: one sentence stating the purpose, followed by a crucial usage instruction. Every word earns its place, with no redundancy or unnecessary information. The important guidance is appropriately emphasized with 'IMPORTANT:' and placed upfront.
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 the tool has comprehensive annotations (readOnlyHint, openWorldHint), 100% schema coverage, and an output schema exists, the description provides excellent contextual completeness. It adds crucial language-specific usage guidance that wouldn't be captured in structured fields, making it complete for agent understanding despite the structured data richness.
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 100%, so the schema fully documents the single 'query' parameter. The description doesn't add parameter-specific details beyond what's in the schema. However, it does imply language considerations (Persian/English) that relate to how the query parameter should be used, though not explicitly about the parameter itself.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get optimized search keywords and relevant categories.' It specifies both the output (keywords and categories) and the optimization aspect. However, it doesn't explicitly differentiate from sibling tools like 'search_products' or 'search_text_lenz' which might also involve keywords or categories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Always call this with BOTH Persian (Farsi) and English queries separately, as the API responds differently to each language and Farsi typically yields better, more comprehensive results.' This gives clear instructions on when and how to use the tool, including language-specific behavior and performance expectations.
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?
Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds value by specifying the scope of information returned (specifications, reviews, feedback, buy suggestions) and the requirement for a specific product, which goes beyond what annotations convey about behavior.
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 two sentences, front-loaded with the core purpose and followed by usage guidance. Every word earns its place, with no redundancy or fluff, making it highly efficient and easy to parse.
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 the tool's simplicity (1 parameter, 100% schema coverage), rich annotations (readOnly, openWorld, idempotent, non-destructive), and the presence of an output schema, the description is complete. It clearly states what the tool does and when to use it, which is sufficient for an agent to select and invoke it correctly in context.
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 100%, with the single parameter 'product_id' well-documented as a 'Unique product identifier'. The description doesn't add any additional parameter semantics beyond what the schema provides, such as format examples or constraints, so it 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get comprehensive product information') and resource ('product'), with specific details about what information is included (specifications, expert reviews, customer feedback, buy suggestions). It distinguishes from sibling tools like 'search_products' by focusing on detailed information for a specific product rather than searching or listing.
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 provides explicit guidance on when to use this tool ('when you need detailed information about a specific product'), which helps differentiate it from siblings like 'get_product_recommendations' or 'search_products'. However, it doesn't explicitly state when NOT to use it or name specific alternatives for edge cases.
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?
The description adds valuable behavioral context beyond annotations: it specifies pagination ('Returns 20 products per page'), currency details ('All prices in TOOMAN'), and language handling ('Farsi often being more comprehensive'). While annotations cover safety (readOnlyHint=true, destructiveHint=false), the description enriches operational understanding without contradiction.
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 and front-loaded with core functionality, followed by critical workflow instructions. Each sentence adds value (e.g., pagination, currency, language tips), though it could be slightly more concise by integrating some details into the schema.
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 the tool's complexity (9 parameters, filtering/sorting), the description provides complete context: it explains prerequisites, workflow integration, language nuances, currency, and pagination. With annotations covering safety and an output schema presumably handling return values, no significant gaps remain.
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?
With 100% schema description coverage, the input schema already documents all 9 parameters thoroughly. The description adds minimal param-specific info (e.g., emphasizing category_id requirement and keyword usage), but most semantics are redundant. 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for products within a specific category with filtering and sorting.' It specifies the verb ('Search'), resource ('products'), and scope ('within a specific category'), distinguishing it from siblings like get_product_details (detail retrieval) and search_text_lenz (text-based search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when and how to use this tool: 'ALWAYS call get_optimized_keywords_and_categories FIRST to get the category_id' and 'Search with BOTH Persian (Farsi) and English keywords separately.' It also mentions workflow integration and language-specific behavior, offering clear alternatives and prerequisites.
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?
The description adds valuable context beyond annotations by explaining the tool's purpose ('explore similar alternatives') and use case. Annotations already cover safety (readOnlyHint=true, destructiveHint=false) and reliability (idempotentHint=true, openWorldHint=true), so the bar is lower. The description doesn't add behavioral details like rate limits or auth needs, but provides useful application context.
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 perfectly concise with two sentences that each earn their place. The first states the purpose, the second provides usage guidance. No wasted words, and it's front-loaded with the core functionality.
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 the tool's moderate complexity, comprehensive annotations (covering safety and reliability), 100% schema coverage, and the presence of an output schema, the description is complete enough. It explains what the tool does and when to use it, which is sufficient since annotations handle behavioral traits and schemas handle inputs/outputs.
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?
With 100% schema description coverage, the schema already fully documents both parameters (product_id and offset). The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3. It doesn't explain parameter interactions or provide additional semantic context.
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 the specific verb ('Get') and resource ('similar products and related category recommendations'), and distinguishes from siblings by specifying its unique use case ('when a product is close but not exactly what you need, to explore similar alternatives'). This differentiates it from tools like get_product_details (single product info) and search_products (general search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('when a product is close but not exactly what you need, to explore similar alternatives'), providing clear context for its application. While it doesn't name specific alternatives, it implicitly distinguishes from search_products (for general searches) and get_product_details (for single product info).
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?
Annotations already declare this as read-only, non-destructive, idempotent, and open-world. The description adds valuable behavioral context about the AI-powered semantic nature, domain specialization, and query format preferences (2-3 word visual descriptions), which goes beyond what annotations provide.
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?
Three tightly focused sentences with zero waste. The first establishes the core function, the second provides domain and usage guidance, and the third explains capabilities. Every sentence earns its place.
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 the tool has comprehensive annotations, 100% schema coverage, and an output schema exists, the description provides excellent contextual completeness. It explains the AI/semantic nature, domain specialization, and query approach that aren't captured in structured fields.
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 100%, so the schema already fully documents both parameters. The description reinforces the query format guidance ('2-3 word visual descriptions') but doesn't add significant semantic meaning beyond what's in the schema descriptions.
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 the tool performs 'AI-powered semantic search using Text-Lenz' with specific domain focus on 'clothing, accessories, wearables, and shoes'. It distinguishes from sibling tools like 'search_products' by emphasizing the AI/semantic nature and visual description approach.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided on when to use this tool: 'Exceptional for clothing, accessories, wearables, and shoes' and 'Use 2-3 word visual descriptions'. The description also distinguishes this from other search approaches by noting it 'understands natural language and context'.
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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