getir-mcp

Server Quality Checklist

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Latest release: v1.0.1
Disambiguation5/5
Each tool targets a distinct resource and action: orders (list, get, accept, reject), products (list, update availability, update price), and store status (get). No two tools overlap in purpose, so an agent can clearly distinguish them.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case: list_orders, get_order, accept_order, reject_order, list_products, update_product_availability, update_product_price, get_store_status. This makes the set predictable and easy to navigate.
Tool Count5/5
With 8 tools, the set is well-scoped for a Getir store management server. It covers orders, products, and store status without unnecessary redundancy or bloat.
Completeness4/5
The tool set covers the core workflows for managing orders (list, get, accept, reject) and products (list, update price, update availability). However, there is no way to update store status (e.g., open/close), which is a minor gap in lifecycle coverage.
Average 3.2/5 across 8 of 8 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
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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, but it adds no behavioral insight beyond what the name implies. It doesn't state that the operation is read-only, doesn't disclose pagination behavior, and gives no context on return values 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, short sentence with no wasted words, front-loading the core purpose. However, it's so minimal that it arguably under-specifies, though this is not a conciseness flaw but a completeness issue.
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 the tool has three parameters, an enum filter, and no output schema, the description is too thin. It doesn't explain pagination, status filtering, or what the response looks like, leaving agents with insufficient context for a list operation.
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 covers all three parameters with descriptions, so the schema already provides the parameter meanings. The tool description adds no extra semantics 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 tool lists orders ('List orders from your Getir store'), with a specific verb and resource. It implicitly distinguishes from siblings like get_order (singular) and accept/reject_order (mutations), though it doesn't explicitly name alternatives.
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 get_order or other sibling tools. There's no mention of filtering by status, pagination, or typical use cases, so agents must infer the intended usage from the name alone.
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, and the description adds no behavioral context beyond the tool's name. It does not mention idempotency, permission requirements, side effects, or any validation details.
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 only three words, with no waste or redundancies. It is concise but does not use the available space to convey additional useful details.
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 mutation tool with no output schema and no annotations, this description is thin. It lacks any indication of typical use, pricing constraints, or side effects, leaving the agent to infer too much from the schema.
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 both product_id and price clearly described. The description itself adds no extra meaning beyond the schema, so a 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 'Update product price' clearly states the action (update) and resource (product price). It is specific enough to differentiate from list/order tools, though it doesn't explicitly contrast with the sibling tool 'update_product_availability'.
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 like update_product_availability or other product modification tools. The description implies its use but offers no context about prerequisites or scenarios.
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?
There are no annotations, so the description carries the full burden. It only states 'Accept an incoming order' without disclosing side effects, requirements, or the effect on order status. This adds nothing beyond the tool's 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, clear sentence with no filler. However, it is extremely brief, potentially under-specified, but not wasteful.
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 the tool lacks an output schema and annotations, the description should explain the acceptance process and preparation_time's role, but it does not. The description is too minimal for a mutation 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?
The schema provides complete descriptions for both parameters (order_id and preparation_time), so the baseline is 3. The description itself does not add additional semantic context for parameters.
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 specific verb 'accept' with the resource 'order', clearly distinguishing it from the sibling tool 'reject_order'. It conveys the core action concisely.
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 like 'reject_order' or 'get_order'. The context of 'incoming order' is implied but not explicit.
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 must disclose behavioral traits such as side effects, reversibility, permissions, or state changes. 'Reject an incoming order' only restates the purpose and does not explain what happens after rejection, system impacts, or any required authorization.
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, focused clause with no unnecessary words. It is front-loaded and immediately understandable, which is ideal for a simple tool.
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?
While the tool is simple, the description lacks critical context: no usage scenarios, no side-effect disclosure, and no information about return values or outcomes. With no output schema and no annotations, the description alone is insufficient for an agent to know the full consequences of invoking this 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?
The input schema fully describes both parameters (order_id and reason) with clear descriptions, so the schema carries the explanatory burden. The description adds no additional semantic information beyond what the schema already provides, landing at the baseline for 100% 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 'Reject an incoming order' clearly states a specific action (reject) on a specific resource (order). It is immediately distinguishable from sibling tools like accept_order, get_order, and list_orders.
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 given on when to use this tool versus alternatives such as accept_order or get_order. It does not mention prerequisites, context, or situations where rejection is appropriate. The instruction is purely the action, leaving the 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.
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 only states what the tool does but not the effects, reversibility, existence checks, or error handling. This is insufficient for a mutation tool.
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 wasted words. It is front-loaded and effectively states the tool's action.
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?
Despite full schema coverage, the description lacks context for a mutation tool with no annotations or output schema. It doesn't explain the meaning of 'enabled/disabled', potential side effects, or error conditions, leaving gaps for an AI 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% with clear parameter descriptions ('Product ID', 'Set product active/inactive'). The description does not add any meaning beyond the schema, so baseline 3 is appropriate.
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 'Enable or disable a product' clearly states the tool's function with a specific verb and resource. It distinguishes from siblings like update_product_price and list_products, making its purpose unambiguous.
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. It doesn't mention prerequisites, exclusions, or scenarios where this tool is preferred, such as toggling product availability versus updating price.
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 the full burden of behavioral disclosure. It only states 'List products' without explaining return format, whether results are paginated, how category_id affects results, or any required permissions. This is a significant gap for a simple list 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 a single, focused sentence with no wasted words. It is concise and immediately understandable, though it could benefit from a bit more detail without becoming verbose.
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 the tool's simplicity, the description is adequate for basic understanding, but with no output schema and no annotations, it leaves out critical context such as filtering behavior, response shape, and potential pagination. It is minimal to the point of being under-specified.
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 only parameter (category_id) is fully described in the schema ('Filter by category ID'), providing 100% coverage. The description adds no extra meaning beyond what the schema already provides, which is acceptable but not additive.
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 states a clear action ('List') and resource ('products') with a specific scope ('in your Getir store'). It inherently distinguishes from sibling tools like list_orders, which target a 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?
The usage context is implied by the resource type, but the description provides no explicit guidance on when to use this tool vs alternatives, nor does it mention the optional filtering capability. It doesn't give exclusions or alternative tool references.
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 burden. It indicates a read operation but does not disclose return format, error behavior, or whether the response includes nested details. This is minimal disclosure for an API call.
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 entire description is one clear, concise sentence with no filler. It is appropriately front-loaded and earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (1 param, no nested objects), but the absence of an output schema and behavioral details leaves the agent without knowledge of the response shape or potential side effects. Adequate but with notable gaps.
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% and the parameter description 'Order ID' is present in the schema. The tool description adds no additional meaning beyond the schema, so baseline 3 is appropriate.
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 'Get a specific order by ID' clearly states the action (get) and resource (specific order), and distinguishes itself from sibling tools like list_orders by emphasizing specificity rather than 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?
Usage context is implied: use when you need a single order by ID, as opposed to list_orders for all orders. However, no explicit exclusions or alternative recommendations are 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 burden of behavioral disclosure. It reveals the return values (open/busy) and implies a read-only operation via 'Get', but does not detail error behavior, freshness, or other characteristics. It is minimally transparent but not misleading.
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 one sentence, front-loaded with the action and resource, and includes the essential output hint. Every word earns its place; it is appropriately sized for the tool's simplicity.
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 parameterless tool with no output schema and no annotations, the description sufficiently covers the return values (open/busy). Given the low complexity, no additional information is necessary to select or invoke the tool correctly.
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 tool has zero parameters, so the schema is empty. Per rubric, 0 params receives a baseline of 4. The description does not need to explain nonexistent parameters, and it adds value by clarifying the output 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?
The description 'Get current store status (open/busy)' uses a specific verb ('Get') and clearly identifies the resource ('store status') with the possible values parenthetically. This distinguishes it from sibling tools, which are all order/product related.
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: when you need to know if the store is open or busy. However, it does not explicitly state when to use it versus alternatives, and there are no exclusions or scenarios provided. This is sufficient for a simple getter but lacks explicit guidance.
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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