mcp-cha-cha

by danny-sbr

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct action: menu browsing, recommendations, order creation, order query, pickup, and cancellation. There is no meaningful overlap; get_menu and recommend_drinks serve different selection purposes.
Naming Consistency4/5
Most tool names follow a clear lower_snake_case verb_noun pattern (create_order, get_menu, cancel_order). pickup_order is a slight deviation since 'pickup' is used as a verb-like noun, but it remains readable and consistent in style.
Tool Count5/5
Six tools is well-scoped for a focused beverage-ordering server. Each tool covers a necessary part of the workflow without redundancy.
Completeness4/5
The core ordering lifecycle is covered: menu lookup, recommendation, create, query, cancel, and pickup. A minor gap is the lack of an order update/modify tool for changing items before pickup.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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
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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

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 explaining behavior. It states the core state transition but does not disclose side effects, whether the operation is idempotent, what happens if the order is already picked up, or any authorization requirements.
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. The verb and the object are front-loaded, making the purpose immediately clear.
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 state-changing tool with no annotations and no output schema, the description is too thin. It provides no information about required preconditions, error cases, or the response after a successful pickup, leaving important operational context to speculation.
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 only parameter, order_id, is documented as '訂單 ID'. The description adds no additional meaning 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description, '標記訂單為已領取' ('Mark order as picked up'), states a specific verb (mark) and resource (order) with a clear resulting state. It is naturally distinct from siblings like create_order, cancel_order, and get_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?
The description gives no guidance about when to use this tool versus create_order, cancel_order, or get_orders. Prerequisites such as the order needing to exist or be in a certain status are not mentioned, so the agent has to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden. '查詢' implies a read-only operation, and the mention of automatically showing an interactive menu browsing interface on supported MCP Apps hosts is a useful behavioral trait. However, it does not explicitly state side-effect-freeness, response shape, or behavior on non-supporting hosts.
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 short sentences with no filler. The first sentence front-loads the core purpose and resource, and the second adds a relevant behavioral detail about the interactive UI. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with one optional parameter and no output schema, the description covers the menu contents and the interactive UI behavior sufficiently. It lacks explicit usage routing and a clear statement of return format, but these are minor given the schema and tool simplicity.
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 describes the single optional category parameter with 100% coverage, so the description does not need to repeat it. The tool description does not add parameter-level meaning beyond the schema, matching the baseline for 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 clearly states the verb '查詢' (query) and the resource '侘茶菜單' (Wacha tea menu), and enumerates the menu contents: categories, drinks, toppings, and sweetness/ice options. This is specific enough to distinguish it from order-related siblings like create_order and get_orders, though it does not explicitly name an alternative tool.
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 explicit guidance on when to use this tool versus alternatives such as recommend_drinks or get_orders. It only explains the menu query purpose and the interactive behavior on MCP Apps hosts, which is context rather than routing guidance.
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 reveals the precondition about unpicked orders but does not state that cancellation is likely destructive/irreversible, what state the order enters, or any permission/authorization requirements. For a mutating tool, this is a notable gap.
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 compact sentence with the key constraint placed in parentheses. It contains no filler and every word adds value.
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 and the schema fully documents the only parameter, but with no annotations and no output schema the description does not explain the mutation's effects or possible error conditions. It is sufficient for tool selection, but leaves some behavioral context for the agent to infer.
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, order_id, is already fully documented in the schema with 100% coverage. The description adds no additional meaning about the parameter, 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action '取消' (cancel) and the target '訂單' (order), and adds a scope constraint ('僅限尚未領取的訂單'). This distinguishes it well from sibling tools like create_order, pickup_order, and the read-only listing tools.
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 explicitly says the tool only applies to orders that have not yet been picked up, which is a clear usage condition. It does not name an alternative for already-picked-up orders, so it stops just short of full routing guidance.
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?
No annotations are provided, so the description carries the behavioral disclosure burden. It does convey the core behavior: recommendations are based on user preferences. Still, it does not mention whether this is a read-only tool, whether it respects all constraints, or what happens when no matching drinks exist, which are relevant gaps.
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 front-loaded sentence with no filler or redundancy. Every word contributes to identifying the tool's purpose and preference-based behavior, making it appropriately concise.
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?
For a read-only recommendation tool with fully documented optional parameters, the description is minimally sufficient for invocation. However, it lacks explicit guidance on when to prefer it over get_menu, does not describe the output format, and has no annotation support, so it is not fully complete.
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 parameters like mood, count, max_price, and caffeine_free are already well documented with defaults and meaning. The description only adds the general 'based on preferences' idea and does not deepen parameter understanding, so the baseline of 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 uses a specific verb and resource: '根據偏好推薦侘茶飲品' clearly means 'recommend wabi-cha drinks based on preferences.' It is distinct from the sibling tools such as get_menu, create_order, or cancel_order, because it is explicitly about preference-driven recommendations rather than menu retrieval or order management.
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 context is implied: the tool is for recommending drinks when the user expresses a preference like '想喝清爽的' or '不要咖啡因'. However, the description does not explicitly state when not to use it or name alternatives like get_menu for browsing the full menu, so the routing decision is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. The verb '查詢' indicates a read-only operation, and it explicitly discloses that no parameters returns all orders and that order_id overrides other filters. It does not mention auth, rate limits, or output format, but these are less critical for a simple query 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 two short, information-dense sentences. It front-loads the action and resource, then lists usage modes without any filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, fully described parameters, and clear sibling differentiation, the description covers all invocation modes: exact ID, filters, and no-parameter listing. The absence of return-value details or pagination behavior is a minor gap but does not hinder correct selection or invocation.
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 all four parameters are already documented in the input schema. The description only summarizes the filter options and does not add meaningful per-parameter semantics beyond the schema, yielding the baseline score of 3.
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 opens with the specific verb-resource pair '查詢訂單' (query orders) and clearly distinguishes exact-ID lookup from filtered listing. This is clearly distinct from the sibling mutation tools (create_order, pickup_order, cancel_order) and other resources (get_menu, recommend_drinks).
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 gives clear usage contexts: exact order ID, filtering by purchaser/pickup status/date, and a no-parameter fallback that returns all orders. It does not explicitly state 'use X instead for mutations,' but the sibling tool names make those exclusions obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It goes beyond a simple 'create order' statement by revealing that item_name must exactly match the menu, that colloquial terms are not automatically resolved, and that drink and topping components must be separated. It could mention side effects or error behavior, but the key operational caveats are clearly surfaced.
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 compact and front-loaded: the first clause states the purpose, and the second sentence delivers the most important usage warning. Every sentence earns its place, with no filler or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a create operation with 7 parameters, 100% schema coverage, and no output schema, the description is nearly complete. It covers the critical domain-specific pitfall (colloquial abbreviations), the prerequisite get_menu call, and the expected field separation. It does not describe validation behavior or return values, but those are not essential for an agent to invoke the tool correctly in the given context.
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?
Schema description coverage is 100%, so the baseline is 3. The description adds meaningful value by giving a concrete example: '波霸奶綠' should be split into item_name='茉香奶綠' with toppings=['波霸']. This clarifies the relationship between item_name and toppings beyond the schema's individual 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 states a specific verb and resource: it establishes a Wucha beverage order. It also clearly distinguishes itself from sibling tools by focusing on order creation, while pickup_order, cancel_order, get_menu, and get_orders cover other actions. The domain-specific note about colloquial abbreviations reinforces its purpose without ambiguity.
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 clear workflow guidance: when customers use spoken abbreviations, call get_menu first to confirm the exact item name, then fill in drink and toppings separately. It does not explicitly state when not to use this tool or compare it directly with pickup/cancel, but the context is strong enough for an agent to choose it appropriately.
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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