Market Fiyatı MCP Server

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
Several tools have overlapping functionality, particularly around AI recommendations (get_ai_recommendations, analyze_prices, compare_markets) and price comparison (compare_prices, find_cheapest, analyze_prices). While search tools are distinct, the boundaries between analysis tools are unclear.
Naming Consistency4/5
Most tools follow a verb_noun snake_case pattern (search_products, compare_prices, filter_by_market). Minor deviations include 'chat' (verb only) and 'find_cheapest' (verb + adjective), but overall the convention is consistent.
Tool Count4/5
10 tools is appropriate for a market price comparison server, though there is some redundancy in the AI-related tools. The count is within a reasonable range and each tool has a stated purpose.
Completeness4/5
The server covers search, price comparison, filtering, and AI-driven analysis. It lacks explicit product detail retrieval beyond search_by_id and has no price history or alert features, but the core workflows for price comparison are present.
Average 2.6/5 across 10 of 10 tools scored. Lowest: 1.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and fails to disclose any behavioral traits—no mention of how latitude/longitude are used, what distance means, result ordering, or 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.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, but it offers no value beyond the tool name. It is under-specified rather than efficiently concise; the sentence does not earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, 3 required, no output schema, and no annotations, the description is drastically incomplete. It provides no context for how the search works, how coordinates factor in, or what the response looks like.
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?
The input schema documents all parameters with descriptions (100% coverage), so the baseline is 3. The description itself adds no parameter-level meaning, but the schema already covers the semantic burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Ürün arama işlemi yapar' is tautological, essentially restating the tool name 'search_products' without specifying scope, behavior, or distinguishing from sibling tools like search_by_id or find_cheapest.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 versus alternatives, no prerequisites, exclusions, or typical scenarios are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only says the tool 'compares' and 'gives AI analysis.' It does not reveal whether the operation is read-only, how location affects behavior, what the output structure is, or any side effects. This is insufficient for a tool with no other behavioral metadata.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (a single sentence) and contains no fluff, but it is under-specified. It communicates the core action yet lacks the detail needed to be genuinely helpful. This is not mere conciseness but a sparse specification that fails to earn a higher score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, no annotations, and several overlapping siblings. The description does not explain the nature of the 'AI analysis,' the expected return format, or how coordinates are used. This is insufficient for an agent to select and invoke the tool correctly in a broader 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?
The input schema provides full descriptions for all three parameters, so the description adds no additional parameter-level semantics. The schema already explains keywords, latitude, and longitude, meeting the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('compares') and resource ('markets') but is vague about what 'markets' refers to and does not differentiate this tool from siblings like compare_prices or get_ai_recommendations. The mention of 'AI analysis' adds some distinction, but it is not specific enough to fully clarify the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as compare_prices or find_cheapest. The description lacks any context about prerequisites, typical use cases, or what makes this tool preferable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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 says only 'searches by product ID', with no information about required coordinates (latitude/longitude), return format, filtering behavior, or side effects. This is a significant gap for a tool with three required parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise. However, it is so minimal that it borders on under-specification, similar to the 'Process' example. It is not inappropriately long, but it lacks structured detail that would make it more informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three required parameters, no output schema, and no annotations, the description should explain more about the expected behavior, such as why latitude/longitude are needed or what the search returns. The current description is incomplete for a tool of this complexity, leaving important context unaddressed.
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 100%: all three parameters (productId, latitude, longitude) have descriptions in the schema (e.g., 'Kullanıcı enlem koordinatı' for latitude). The description adds no additional parameter meaning beyond the schema, so the baseline score of 3 applies.
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 'Ürün ID'si ile arama yapar' clearly states the tool searches by product ID. It uses a specific verb ('arama yapar') and resource (product ID), making its purpose clear. However, it does not explicitly distinguish itself from sibling tools like search_products, though the name and description imply a specific ID-based lookup.
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?
The description gives no guidance on when to use this tool versus alternatives such as search_products or find_cheapest. It does not mention prerequisites, context, or when an ID-based search is preferred over other search methods.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read-like operation ('gets') but gives no details about side effects, required permissions, rate limits, or what the response contains. This lack of transparency is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words, but it is under-specified. While brevity is a virtue, the lack of essential information (like what makes it 'AI-supported' or how parameters are used) makes it feel minimal rather than appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given four required parameters, no annotations, and no output schema, the description should clarify the tool's purpose and behavior more comprehensively. It does not explain how inputs like userQuery and keywords contribute to the recommendation, nor what the output format is. This makes it incomplete for reliable tool selection.
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 documents all four parameters. The description adds no extra meaning beyond the schema's field descriptions, so the baseline score of 3 is appropriate.
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 'AI destekli ürün önerileri alır' clearly states the tool gets AI-supported product recommendations, providing a specific verb and resource. However, it does not distinguish itself from sibling tools like search_products or compare_prices, so it lacks differentiation.
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?
There is no guidance on when to use this tool versus alternatives. The description does not mention any context, prerequisites, or exclusions, leaving the agent without direction on choosing this tool over siblings.
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?
With no annotations, the description carries full responsibility for behavioral disclosure. It mentions that the tool provides an AI recommendation, but it does not indicate read-only status, side effects, or any other behavioral traits beyond that minimal detail.
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 a single concise sentence with no redundancy. It states the core functionality directly, though it is perhaps too terse to fully capture the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with three required parameters and no output schema, the description is minimal. It doesn't describe the output format, how the location-based analysis works, or the nature of the AI recommendation, leaving significant gaps for the agent.
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 baseline is 3. The description adds no parameter-specific meaning; it does not explain how latitude, longitude, or productId are used, providing no extra value beyond the schema.
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 that the tool performs price analysis and provides an AI recommendation, using specific verbs ('analiz yapar', 'öneri verir'). However, it does not distinguish it from sibling tools like compare_prices or get_ai_recommendations, so it lacks explicit differentiation.
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?
The description offers no guidance on when to use this tool versus alternatives. There is no mention of scenarios, exclusions, or alternative tools, leaving the agent without decision support.
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 the full burden of behavioral disclosure. It only says 'general chat with AI' and does not mention interaction behavior, context retention, response format, or limitations. This gives the agent little insight into what to expect when invoking the tool.
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 extremely concise, consisting of a single short sentence with no wasted words. It front-loads the core purpose effectively, though this brevity contributes to the under-specification noted in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no output schema, the description should at least indicate the nature of the response or typical usage scenarios. It currently provides minimal information, making it incomplete for an agent to properly invoke and interpret results, especially given the context of shopping-related sibling tools.
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?
The input schema already documents both parameters with descriptions (message and context), achieving 100% coverage. The description adds no additional parameter details, so it meets the baseline for schema-driven parameter understanding.
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 performs 'general chat with AI' (AI ile genel sohbet), which distinguishes it from the shopping-focused sibling tools like search_products and get_ai_recommendations. However, it lacks specificity about what kinds of conversations or tasks it supports, 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.
Usage Guidelines2/5
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 versus alternatives. With siblings like get_ai_recommendations and generate_shopping_list, the description does not clarify whether this is a fallback for non-shopping queries or if it can also handle shopping-related requests. This leaves the agent without clear selection criteria.
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?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It only states the basic action and does not explain what the comparison involves, how results are ordered, what data is returned, or any side effects. This is a significant gap for a tool with no output schema.
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 a single short sentence with no padding, front-loaded with the verb. It is concise and efficiently stated, though slightly too terse to fully capture the tool's purpose. Given the schema covers parameters, the minimalism is acceptable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and only a two-word description. It lacks details on return structure, the meaning of 'comparing prices' (e.g., across stores, sorted by distance?), and any context relative to similar tools. For a tool with moderate complexity, this is incomplete.
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?
The input schema provides 100% coverage with descriptive parameter descriptions (productId, latitude, longitude). The tool description adds no additional semantic value beyond the schema, so the baseline 3 is appropriate.
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 action ('compares') and resource ('product prices'), making the basic purpose evident. However, it does not distinguish itself from sibling tools like 'compare_markets' or 'find_cheapest', and the role of latitude/longitude in the comparison is not implied.
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?
There is no guidance on when to use this tool versus alternatives. The description only states the action, with no mention of preferred scenarios, prerequisites, or exclusions. Users are left to infer usage from the parameter names, which is insufficient.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that it filters products and does not mention whether it is a read-only operation, how location coordinates are used, result format, pagination, or any side effects. The description adds minimal value beyond the tool name.
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 a single, concise sentence that front-loads the key action. It is not bloated or repetitive. However, it is arguably too terse, missing valuable context that could be added without harming conciseness. Still, it earns a 4 for efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has four required parameters including latitude/longitude and no output schema or annotations, yet the description only provides a one-line summary. It fails to explain the role of the coordinates, how keywords interact with market filtering, expected return values, or operational context. This is insufficient for an AI agent to use the tool correctly without additional inference.
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?
The schema already describes all four parameters with 100% coverage (keywords, marketName, latitude, longitude). The description adds no additional semantic detail beyond 'by market', which is already captured by marketName. Therefore, the description does not compensate or extend the schema, but the baseline of 3 is appropriate given the high schema coverage.
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 'Filters products by a specific market' uses a specific verb (filters), identifies the resource (products), and states the filtering scope (by market). This distinguishes it from broader sibling tools like search_products, though it does not explicitly name alternatives. It is clear and actionable.
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?
The description provides no guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It simply states what it does without contextualizing the intended use case or comparing it to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It only states a basic purpose and reveals nothing about how results are ordered, whether location is mandatory, or what the output format is. This is no more informative than the tool name itself.
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 a single, short sentence with no redundant words, making it concise and easy to parse. It is front-loaded and efficient, though it essentially restates the tool name without providing deeper insight.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has three required parameters, no annotations, and no output schema, making it moderately complex. The description is too bare—it does not explain the role of latitude and longitude, what a result looks like, or any edge cases. This leaves the agent under-informed.
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?
The input schema provides full descriptions for all three parameters (keywords, latitude, longitude) in Turkish, giving a schema coverage of 100%. The tool description adds no extra semantic value beyond what the schema already states, so the baseline score of 3 applies.
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 finds the cheapest products, using a specific verb and resource. It distinguishes from siblings like compare_prices by focusing on 'cheapest' rather than comparison, but does not explicitly mention how it differs from other price-related tools.
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?
Usage is implied by the description and tool name: use this tool when you want the cheapest products. However, no explicit guidance is given about when not to use it or which alternative tools (e.g., compare_prices, search_products) might be more appropriate in certain scenarios.
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 must disclose behavioral traits. It only states that it 'creates' a list, with no mention of side effects, required permissions, idempotency, or what happens to existing data. The impact of 'AI destekli' (AI-supported) is undefined, leaving the agent without a clear behavioral model.
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 a single, direct sentence with no filler or redundancy. It is front-loaded with the core purpose. While extremely brief, the conciseness is appropriate for a simple action, though the brevity leaves gaps in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should compensate by explaining return values or usage conditions. It fails to do so, providing only the most basic statement. The tool has three parameters and no output schema, making the description insufficient for an agent to predict behavior or outcomes.
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?
The input schema fully describes all parameters (items, budget, preferences) with 100% coverage. The tool description adds no additional parameter details, so it meets the baseline for high schema coverage. No extra semantic value is provided.
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 action: 'AI destekli alışveriş listesi oluşturur' (creates an AI-supported shopping list). This distinguishes it from siblings like search_products or compare_prices, as it focuses on list generation. However, the phrase 'AI destekli' is vague and lacks specificity about what AI adds.
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?
The description provides no guidance on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or exclusions. For a tool with siblings like get_ai_recommendations and find_cheapest, this absence of context makes it hard for an agent to decide when to invoke it.
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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