Owlvin MCP Server

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation4/5
Most tools have distinct purposes: scanning, optimizing, checking credits, buying credits, listing services, calling services, and checking pricing. However, owlvin_services and owlvin_pricing could be slightly confused as both relate to service discovery and costs, though their focuses differ (services vs. credit packages).
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with the 'owlvin_' prefix, and they use clear verbs like scan, optimize, buy, call, check, and list. This uniformity makes the set predictable and easy to navigate.
Tool Count5/5
With 7 tools, the server is well-scoped for its purpose of managing and optimizing MCP server discoverability and platform usage. Each tool serves a specific function without redundancy, covering scanning, optimization, billing, service discovery, and API calls.
Completeness4/5
The toolset covers core workflows: scanning and optimizing AEO, managing credits, discovering and calling services, and checking pricing. A minor gap is the lack of a tool for managing account settings or viewing usage history beyond credits, but agents can work around this with the existing tools.
Average 4.2/5 across 7 of 7 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
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{
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  "maintainers": [
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  ]
}
```
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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

Behavior3/5
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 states that the tool 'returns available credit packages with prices, bonuses, and per-call costs for each service,' which implies a read-only operation that fetches data. However, it doesn't mention potential behaviors like rate limits, authentication needs, or error handling. The description adds some value but lacks comprehensive behavioral context.
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 appropriately sized and front-loaded, with the first sentence stating the core purpose and the second elaborating on the return values. Both sentences earn their place by adding useful information without redundancy. It's efficient, though not perfectly structured for maximum clarity.
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 complexity (simple read operation with 0 parameters), the description is complete enough. It explains what the tool does and what it returns, and since an output schema exists, it doesn't need to detail return values further. However, it could improve by addressing behavioral aspects like authentication or rate limits, given the lack of annotations.
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 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't need to compensate for missing parameter info, as there are none to explain. It provides a baseline level of clarity about what the tool does without parameter details, which is sufficient for a tool with no inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Show credit packages and pricing for the Owlvin platform.' It specifies the verb 'show' and the resource 'credit packages and pricing,' making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'owlvin_credits' or 'owlvin_buy_credits,' which might have overlapping or related functionality, so it doesn't reach a perfect score.
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. It doesn't mention any context, prerequisites, or exclusions, nor does it reference sibling tools like 'owlvin_credits' or 'owlvin_buy_credits' that might be related. This lack of usage instructions leaves the agent without clear direction on tool selection.
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 effectively describes key traits: billing, rate limiting, and auth are handled automatically; cost is deducted from credit balance; and it returns the service response. This covers operational, financial, and output aspects well for a gateway 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 appropriately sized and front-loaded, starting with the core purpose. Each sentence adds value: the first defines the tool, the second covers behavioral aspects, the third gives usage guidance, and the rest detail parameters and returns. There's no wasted text, and it's well-structured with clear sections.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a universal API gateway with 3 parameters, 0% schema coverage, no annotations, but an output schema, the description is complete enough. It explains the tool's role, behavioral traits, usage flow, parameter meanings, and return behavior. The presence of an output schema means the description doesn't need to detail return values, and it adequately covers other aspects.
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 0%, so the description must compensate. It adds meaning by explaining each parameter: service as 'Service slug (e.g., "drumsplit")', operation as 'Operation name (e.g., "full", "drumbus")', and input_data as 'JSON string with operation-specific parameters.' This clarifies usage beyond the bare schema, but doesn't provide exhaustive details like format constraints or examples for all possible values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Call any service on the Owlvin platform' and identifies it as 'the universal API gateway,' which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like owlvin_aeo_optimize or owlvin_aeo_scan, which appear to be more specialized operations.
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 for usage: 'Use owlvin_services first to see available services and operations.' This gives explicit guidance on prerequisites. It doesn't specify when not to use this tool or name alternatives beyond owlvin_services, but the context is well-defined.
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. It discloses key behaviors: scanning the server, generating files, and requiring copying to the server. However, it doesn't mention potential side effects (e.g., whether it modifies the live server), authentication needs, rate limits, or what happens if the URL is invalid. The description adds useful context but lacks comprehensive behavioral disclosure for a tool that interacts with external servers.
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 efficiently structured with a clear purpose statement, action steps, parameter explanation, and return value description. Every sentence adds value: the first states the goal, the second explains the process, the third specifies what to do with outputs, and the final sections document parameters and returns. No wasted words.
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 complexity (server scanning and file generation), no annotations, but with an output schema present, the description is reasonably complete. It explains what the tool does, the parameter, and the return value ('generated metadata files and projected score improvement'). However, it could better address behavioral aspects like error conditions or security considerations for a tool that accesses external URLs.
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 description adds meaningful context for the single parameter 'url' by providing an example ('e.g., "https://api.example.com"') and explaining it's 'The MCP server's base URL to optimize'. With 0% schema description coverage and only 1 parameter, this compensates well. However, it doesn't specify URL format requirements or validation rules beyond the example.
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 with specific verbs ('generate optimized metadata files', 'scans the server', 'copy these files') and resources ('server-card.json, agents.json, and improved tool descriptions'). It distinguishes from siblings by focusing on optimization rather than scanning (owlvin_aeo_scan) or other unrelated functions like buying credits or checking pricing.
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 about when to use this tool ('to boost an MCP server's agent discoverability', 'to improve your AEO score'), but doesn't explicitly state when NOT to use it or mention alternatives. It implies usage for optimization after scanning, but doesn't compare with the sibling scan tool or other optimization approaches.
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 and does well by disclosing key behaviors: it performs a scan across multiple sources (server-card.json, agents.json, etc.), returns a score with grade and recommendations, and specifies the output format. However, it lacks details on rate limits, authentication needs, or potential 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by details on what it checks and the return format. Every sentence adds value without redundancy, and it's appropriately sized for the tool's complexity.
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 moderate complexity, no annotations, and an output schema (which handles return values), the description is mostly complete. It covers purpose, parameters, and output at a high level but could benefit from more behavioral details like error handling or performance considerations.
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 0% description coverage, but the description compensates by explaining the 'url' parameter's purpose (the MCP server's base URL to scan) and providing an example. Since there's only one parameter, the baseline is high, and the added context is sufficient for understanding.
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 with specific verbs ('scan', 'score') and resources ('MCP server', 'agent discoverability (AEO)'). It distinguishes from siblings by focusing on scanning/scoring rather than optimization, buying credits, or other services listed.
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 context (e.g., when you need to assess an MCP server's discoverability) but does not explicitly state when to use this tool versus alternatives like 'owlvin_aeo_optimize' for improvements or other siblings for unrelated tasks. No exclusions or prerequisites are mentioned.
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 full burden. It discloses the tool's behavior by specifying the return data (credits remaining, account tier, API calls, total spent), which is helpful. However, it does not mention potential limitations like rate limits, authentication requirements, or error conditions, leaving some behavioral aspects unclear.
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 front-loaded with the core purpose in the first sentence, followed by specific return details. Both sentences earn their place by adding value—no wasted words or redundancy. It is appropriately sized for a simple, parameter-less tool.
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 (0 parameters, no annotations, but has an output schema), the description is mostly complete. It explains what the tool does and what it returns, which is sufficient for an agent to use it correctly. However, it could be more complete by addressing authentication or error handling, though the output schema may cover return values.
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 0 parameters, and schema description coverage is 100%, so no parameter information is needed. The description appropriately does not discuss parameters, focusing instead on the tool's purpose and output. A baseline of 4 is applied as it compensates adequately for the lack of 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 clearly states the specific action ('Check your Owlvin platform credit balance and usage') and resource ('Owlvin platform'), distinguishing it from sibling tools like owlvin_buy_credits (purchase) or owlvin_pricing (price inquiry). It goes beyond a tautology by specifying what information is retrieved.
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 implies usage context (checking credit status) but does not explicitly state when to use this tool versus alternatives like owlvin_pricing for pricing details or owlvin_buy_credits for purchasing credits. It provides clear context but lacks explicit exclusions or named alternatives.
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 full burden. It discloses key behaviors: it returns a checkout URL that must be opened in a browser, and credits are added automatically after payment. However, it lacks details on authentication needs, error handling, rate limits, or what happens if the payment fails, leaving gaps for a financial transaction 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 appropriately sized and front-loaded, starting with the core purpose, followed by parameter details and return instructions. Every sentence earns its place by providing essential information without redundancy, making it efficient and well-structured for quick understanding.
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 complexity (financial transaction), no annotations, and an output schema (likely for the URL), the description is mostly complete. It covers purpose, parameters, and key behaviors, but could improve by addressing authentication, error cases, or linking to sibling tools like owlvin_credits for balance checks, leaving minor gaps.
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 description coverage is 0%, so the description must fully compensate. It provides detailed semantics for the single parameter 'amount_cents', including valid options with monetary values and bonus credits, which adds significant meaning beyond the basic integer type in the schema. This effectively documents the parameter's purpose and constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get a Stripe Checkout URL') and resource ('to buy platform credits'), distinguishing it from siblings like owlvin_credits (likely checking balance) and owlvin_pricing (likely viewing prices). It precisely communicates the tool's function without being vague or tautological.
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 for when to use this tool (to purchase credits via Stripe), but does not explicitly mention when not to use it or name alternatives. For example, it doesn't specify if owlvin_credits should be used first to check balance, leaving some guidance implicit rather than explicit.
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 effectively describes the tool's behavior: it returns a list of services with operations and per-call costs, and serves as a discovery mechanism. However, it doesn't mention potential limitations like rate limits, authentication requirements, or data freshness, which would be helpful for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise and well-structured: three sentences that each earn their place. The first states the purpose, the second details the return value, and the third provides usage guidance. It's front-loaded with the core function and wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations, but has an output schema), the description is complete. It explains what the tool does, what it returns, and when to use it. The output schema will handle return value details, so the description doesn't need to elaborate further. This is adequate for a discovery tool with no complex inputs or behavioral risks.
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 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description doesn't need to compensate for any parameter gaps, and it appropriately focuses on the tool's purpose and output rather than inputs. A baseline of 4 is appropriate for a zero-parameter tool where the description adds value beyond the 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 with specific verbs ('List all available services') and resources ('services on the Owlvin platform with pricing'). It distinguishes itself from siblings like owlvin_pricing by focusing on service discovery rather than pricing details alone, and from owlvin_call by being informational rather than operational.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use this to discover what tools are available before calling them.' This provides clear guidance to use it for discovery purposes, distinguishing it from operational siblings like owlvin_call or owlvin_aeo_optimize, and suggests it as a prerequisite for informed tool selection.
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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