Scoped Support Tools Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Each tool targets a distinct resource/action: customer lookup, listing, order ID retrieval, refund processing, product info, and a generic answer fallback. The only potential overlap is answer_user_query, but its purpose as a canned-response fallback is clear.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., process_refund, get_order_id, list_customers). No mixed conventions or unclear verbs.
Tool Count5/5
With 6 tools, the server is well-scoped for a support-focused toolkit. The number is reasonable and each tool has a clear role without unnecessary redundancy.
Completeness4/5
Core support workflows are covered: customer lookup, product info, order ID retrieval, refund processing, and generic canned answers. Minor gaps exist (e.g., no order detail retrieval beyond ID), but these can be worked around with the existing tools.
Average 3.5/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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?
With no annotations, the description carries full burden for disclosing behavior. It only says 'canned answer' but doesn't state whether the answer varies, what happens for unknown queries, or whether it performs any side effects. This is minimal.
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 sentence with no fluff, perfectly sized for a simple tool. Every word earns its place and it is immediately clear.
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 tool with one parameter and an output schema, the description is adequate in length but lacks usage context and behavioral details. It is minimal but not dangerously incomplete, given the simple 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?
The description implies 'query' is the user's question, but does not elaborate on format, length, or examples. Since schema description coverage is 0%, the description should compensate, but it only provides a loose association between the parameter and its meaning.
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 uses a specific verb 'Return' and resource 'canned answer', clearly indicating this tool provides a static response to a user's query. However, it does not differentiate from sibling tools that also handle queries in specific domains, so it's not fully distinguished.
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 the sibling tools (e.g., process_refund, get_product_info). The description only states what it does, not under what circumstances or when to prefer alternatives.
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 carry the full burden of behavioral disclosure. It only states the action without mentioning irreversibility, financial impact, required authorization, or any side effects—a significant gap for a mutating 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 one short, clear sentence with no redundancy. It is appropriately front-loaded and easy to parse.
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 simple tool with one parameter and an output schema, the description covers the basics but omits important behavioral context such as refund processing results, reversibility, or error states. The mutation nature warrants more detail.
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 0%, but the description identifies 'order ID' as the parameter, adding meaning to the order_id field. No format or validation details are given, but with a single parameter this is minimally adequate.
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 verb 'process' and the resource 'refund', with the order ID as the target. It is distinct from sibling tools that handle queries, lookups, and customer information.
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, nor any prerequisites or conditions. The sibling list does not clarify usage, leaving the context implied at best.
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 carry the full burden. It only states the basic action and does not disclose what happens for non-existent customer IDs, error behavior, or any side effects. The behavioral transparency is minimal.
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?
A single concise sentence that is front-loaded with the action and resource, containing no redundant or irrelevant information.
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 has an output schema, so return values need not be described. However, the description omits any guidance on error handling or non-existent IDs. For a simple lookup tool, it is minimally complete but lacks edge-case context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description only says 'given a customer ID', which merely reiterates the schema's parameter name. No format, constraints, or examples are added, so the description fails to compensate for the low 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?
Description states 'Return customer info for a given customer ID' with a specific verb 'return' and resource 'customer info'. It clearly distinguishes from siblings like list_customers (which likely lists all customers) by focusing on a single customer ID.
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 use when a specific customer ID is known, but provides no explicit guidance on when to use this tool vs alternatives such as list_customers or answer_user_query. It is clear but not explicitly differentiated.
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 full burden. It implies a read-only operation via 'Return a list,' but does not disclose details like pagination, ordering, or authorization requirements. This is a minimal but acceptable level 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no unnecessary words, directly stating the purpose.
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 simplicity (zero parameters) and the presence of an output schema, the description covers the core behavior. However, the lack of usage context prevents a perfect score.
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 input schema has zero parameters, so the description cannot add parameter-level details. Per the rubric, 0 params earns a baseline of 4.
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 'Return a list of all customers' uses a specific verb and resource, clearly indicating a list operation. It does not explicitly contrast with sibling tools like lookup_customer_info, so it misses the differentiation criterion.
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 about when to use this tool versus alternatives such as lookup_customer_info for individual customer details. The description simply states what it does without usage context.
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 full burden. 'Look up' clearly indicates a read-only operation, but it does not disclose behaviors such as handling multiple orders, error cases for invalid/missing customer IDs, or whether it returns the most recent order. No contradiction with annotations since none are provided.
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 that states the verb and resource immediately. It is concise with zero unnecessary words or repetition.
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?
The tool is simple with one parameter, and an output schema exists to handle return value details. The description adequately conveys the core purpose and input. Minor gaps include lack of guidance on edge cases and alternatives, but these do not severely impede usability for a straightforward lookup.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning. It merely restates the parameter by saying 'given customer ID' without explaining its format, constraints, or possible values. The schema already provides type and required status, so the description adds no extra semantic value.
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 'look up' and resource 'order ID' with an input 'customer ID', clearly distinguishing it from sibling tools like lookup_customer_info or list_customers. The purpose is unambiguous and directly tied to the tool name.
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 an order ID for a customer ID) but does not explicitly state when to use this tool over alternatives or provide exclusions for edge cases like multiple orders. No sibling tool mention is present, so guidance is only implicit.
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 must convey behavioral traits. It states the tool returns product info and accepts an ID or name, but doesn't disclose behavior for invalid or non-existent products, error handling, or any side effects. This is adequate for a simple read-only tool but lacks depth.
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 concise sentence that front-loads the action and resource, then provides input details and examples. Every word contributes value, with no redundancy or filler.
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 one-parameter tool with an output schema, the description covers the core purpose and input semantics. It doesn't explain what happens when no product is found or if multiple matches exist, but these are minor gaps given the tool's simplicity and existing output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only a parameter name 'product' with no description. The description compensates fully by explaining it accepts a product ID or name and giving examples ('macbook', 'iPhone'). This adds critical meaning beyond the structured schema.
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: 'Return product info (name, price) for a product ID or name'. It uses a specific verb ('Return') and identifies the resource ('product info'), distinguishing it from sibling tools like get_order_id or lookup_customer_info which operate on different entities.
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 context on when to use the tool: when product information is needed, specifying the input format (product ID or name). It doesn't explicitly mention when not to use it or name alternatives, but the implied usage is straightforward for a simple query tool.
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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