Base Mini App Builder MCP Server

by Mr-Web3

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. The tools are well-separated into categories like generation (generate_*), guides (get_*_guide), and validation, each targeting specific aspects of mini app development such as metadata, code, manifests, guides, and requirements checking. There is no overlap in functionality.
Naming Consistency5/5
The tool names follow a highly consistent verb_noun pattern throughout. All tools use either 'generate_' or 'get_' prefixes followed by descriptive nouns, with consistent snake_case formatting. This predictability makes it easy for agents to understand and select the right tool.
Tool Count5/5
With 10 tools, the count is well-scoped for the server's purpose of Base mini app building. Each tool earns its place by covering essential aspects like code generation, guides, validation, and metadata, providing a comprehensive toolkit without being overwhelming or insufficient.
Completeness4/5
The tool surface is nearly complete for the domain of Base mini app development, covering generation, guides, and validation. Minor gaps exist, such as the lack of tools for updating or deleting resources, but agents can work around this as the server focuses on creation and guidance rather than full lifecycle management.
Average 3/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 provided, the description carries the full burden of behavioral disclosure. It only states what the tool does ('get' information) without detailing aspects like whether it's a read-only operation, potential rate limits, authentication needs, or what the output format might be. This leaves significant gaps in understanding the tool's behavior.
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 that efficiently conveys the core function. It's appropriately sized and front-loaded, with no unnecessary words, making it easy to parse quickly.
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 moderate complexity (checking compatibility with one parameter) and the absence of annotations and output schema, the description is insufficient. It doesn't cover behavioral traits, return values, or usage context, leaving the agent with incomplete information to effectively invoke the 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 has 100% description coverage, clearly documenting the 'feature' parameter with an enum. The description adds no additional meaning beyond this, as it doesn't explain parameter usage or context. According to the rules, with high schema coverage (>80%), the baseline score is 3 even without param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool retrieves 'Base App compatibility information and feature support status,' which provides a general purpose (get information about compatibility). However, it lacks specificity about what 'Base App' refers to and doesn't clearly differentiate from siblings like 'get_base_account_guide' or 'get_base_deployment_guide,' making it somewhat vague.
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 explicit guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context for checking compatibility, or how it differs from sibling tools like 'validate_mini_app_requirements,' leaving the agent with no usage direction.
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 states the tool 'generates' something, implying a creation or output operation, but doesn't specify what the output entails (e.g., format, structure), whether it's idempotent, or any side effects like rate limits or authentication needs. This leaves significant gaps in understanding the tool's behavior.
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, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is front-loaded and appropriately sized, making it easy for an agent to parse quickly.
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 complexity (generating metadata for social sharing), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the generated metadata includes (e.g., Open Graph tags, Twitter cards), how it's formatted, or any behavioral traits, leaving the agent with insufficient context to use the tool effectively.
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 has 100% description coverage, clearly documenting all four parameters with details like 'max 32 chars' and '3:2 aspect ratio'. The description adds no additional meaning beyond the schema, such as explaining how parameters interact or their impact on the output. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('generate embed metadata') and the purpose ('for social sharing of your mini app'), which is specific and understandable. However, it doesn't explicitly differentiate this tool from its siblings like 'generate_mini_app_code' or 'generate_mini_app_manifest', which might also relate to mini app components, so it doesn't reach the highest 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, such as when to generate embed metadata compared to other mini app tools. It lacks explicit context, prerequisites, or exclusions, leaving the agent to infer usage based on the purpose alone.
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 states the tool generates starter code but doesn't specify what that entails (e.g., file structure, dependencies, whether it overwrites existing files, or if it's a one-time setup). For a code generation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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, efficient sentence that front-loads the core purpose without unnecessary details. Every word earns its place, making it easy to parse quickly.
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 complexity (generating code with multiple parameters) and lack of annotations and output schema, the description is insufficient. It doesn't explain what 'starter code' includes, how MiniKit integration manifests, or what the output looks like (e.g., a zip file, directory structure). For a generative tool with no structured output, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all three parameters (app_name, app_type, features) with descriptions and enums. The description adds no additional meaning beyond what's in the schema, such as explaining how features interact or what 'MiniKit integration' implies for parameter choices. Baseline 3 is appropriate when the schema does the heavy lifting.
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: 'Generate starter code for a Base mini app with MiniKit integration.' It specifies the verb ('generate'), resource ('starter code'), and context ('Base mini app with MiniKit integration'). However, it doesn't differentiate from siblings like 'generate_mini_app_manifest' or 'generate_embed_metadata', which might also generate code-related artifacts.
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 prerequisites, when not to use it, or how it relates to sibling tools like 'generate_mini_app_manifest' or 'validate_mini_app_requirements'. The agent must infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates a manifest but lacks details on output format, whether it's a read-only operation, potential side effects, or any constraints like rate limits or authentication needs. This leaves significant gaps for an agent to understand the tool's behavior.
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, efficient sentence that front-loads the core action ('Generate a complete Base mini app manifest') and specifies the scope ('with all required fields'). There is no wasted text, making it highly concise and well-structured.
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 complexity of generating a manifest with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the output looks like, how to handle the generated manifest, or any behavioral traits, leaving the agent with incomplete context for proper tool 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?
The input schema has 100% description coverage, clearly documenting all 5 parameters with details like max lengths and enum values. The description adds no additional parameter semantics beyond implying it generates a 'complete' manifest, which aligns with the schema but doesn't provide extra value. This meets 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 'Generate' and the resource 'complete Base mini app manifest with all required fields', which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'generate_mini_app_code' or 'validate_mini_app_requirements', which might also relate to mini app creation or validation.
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 prerequisites, context for generating manifests, or how it relates to sibling tools such as 'generate_mini_app_code' or 'validate_mini_app_requirements', leaving the agent to infer usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does without mentioning traits like whether it's read-only, if it requires authentication, rate limits, or what the output format might be. For a tool with no annotations, this is a significant gap in transparency.
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, efficient sentence that directly states the tool's purpose without any unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects, usage context, or output details, which are crucial for an agent to understand how to invoke and interpret results from this tool effectively.
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 has 100% description coverage, with clear parameter descriptions and an enum for 'platform.' The description adds no additional meaning beyond the schema, such as explaining how parameters affect the guide content. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.
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 'Get' and the resource 'step-by-step deployment guide for Base mini apps,' making the purpose specific and understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'get_base_account_guide' or 'get_debugging_guide,' which might also provide guides, so it lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, such as when to choose this over other guide-related tools in the sibling list, leaving the agent without usage direction.
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 states the tool retrieves a guide, implying a read-only operation, but doesn't address other behavioral aspects such as authentication requirements, rate limits, error handling, or the format of the returned guide. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does and provides examples, making it easy to understand quickly with zero waste.
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?
Given the tool's low complexity (1 optional parameter with full schema coverage) and no output schema, the description is adequate but not complete. It covers the purpose and hints at parameters but lacks details on behavioral traits, usage context, and return values, which are needed for full understanding despite 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 mentions 'Base Account features like sponsored gas and batch transactions,' which aligns with the 'feature' parameter's enum values in the schema. Since schema description coverage is 100%, the schema already documents the parameter well, so the description adds minimal value beyond reinforcing the parameter's purpose. This meets the baseline score of 3 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 tool's purpose with a specific verb ('Get guide') and resource ('Base Account features'), and it provides concrete examples of the features covered ('sponsored gas and batch transactions'). However, it doesn't explicitly distinguish this tool from its siblings like 'get_base_deployment_guide' or 'get_debugging_guide', which might also involve guides but for different topics.
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 prerequisites, context for usage, or exclusions, and with siblings like 'get_base_deployment_guide' and 'get_debugging_guide', there's no indication of how this tool differs in scope or application.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves a guide but doesn't cover critical aspects like whether it's a read-only operation, if it requires authentication, what format the guide returns in, or any rate limits. This is a significant gap for a tool with zero 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
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?
Given the tool's low complexity (one optional parameter) and high schema coverage, the description is minimally adequate. However, with no annotations and no output schema, it fails to address behavioral aspects like return format or operational constraints, leaving gaps that reduce completeness.
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 has 100% description coverage, with the single parameter 'issue_type' fully documented via its enum values. The description doesn't add any meaning beyond this, such as explaining what 'all' entails or how issue types map to guide content, so it meets 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 tool's purpose with a specific verb ('Get') and resource ('comprehensive debugging guide for Base mini app development issues'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_search_discovery_guide' or 'get_base_deployment_guide' beyond the debugging focus, which prevents 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 prerequisites, context for selecting issue types, or how it differs from sibling tools like 'get_base_account_guide' or 'validate_mini_app_requirements', leaving usage decisions unclear.
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 full burden for behavioral disclosure. It states the tool retrieves guidelines but doesn't describe the return format, whether it's a read-only operation, potential rate limits, authentication needs, or error conditions. For a tool with no annotation coverage, this leaves significant behavioral 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, efficient sentence that front-loads the core purpose. Every word earns its place by specifying what is retrieved (design guidelines), for what (Base mini apps), and key content areas. There's no wasted verbiage or redundant 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?
Given the tool has no annotations, no output schema, and a simple single parameter with full schema coverage, the description is minimally adequate. It covers the basic purpose but lacks behavioral context and usage guidance that would be helpful for an agent. The simplicity of the tool means the description doesn't need extensive detail, but it could better address what the tool returns.
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 the single parameter 'category' fully documented in the schema including its enum values. The description mentions the same categories (colors, typography, spacing, navigation) but adds 'all' and 'components'/'app_icon' which aren't in the description text. This provides marginal value beyond the schema, meeting 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 tool's purpose with a specific verb ('Get') and resource ('comprehensive design guidelines for Base mini apps'), including key content areas (colors, typography, spacing, navigation). It distinguishes itself from siblings by focusing on design guidelines rather than code generation, validation, or other guides. However, it doesn't explicitly differentiate from similar 'guide' tools like get_base_account_guide or get_base_deployment_guide.
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 prerequisites, appropriate contexts, or exclusions. While the tool name suggests it's for design guidelines, there's no explicit comparison to other guide-related tools in the sibling list, leaving the agent to infer usage based on naming alone.
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 full burden. It states the tool 'gets' a guide, implying a read-only operation, but doesn't disclose behavioral traits like whether it requires authentication, returns structured vs. unstructured data, has rate limits, or what format the guide comes in. For a tool with zero annotation coverage, this is insufficient.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every word earning 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?
Given the tool's moderate complexity (one optional parameter with clear enum), no annotations, and no output schema, the description is minimally adequate. It states what the tool does but lacks details on output format, usage context, or behavioral constraints. It's complete enough to understand the basic purpose but leaves significant gaps for effective use.
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 the single parameter 'focus_area' fully documented in the schema (including enum values and description). The tool description adds no parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.
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 with a specific verb ('Get') and resource ('guide for optimizing Base mini app search and discovery'). It distinguishes from siblings by focusing on search/discovery optimization, unlike other guides for deployment, design, or validation. However, it doesn't explicitly differentiate from 'get_base_account_guide' which might also provide guidance.
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 prerequisites, when this guide is appropriate versus other sibling guides, or what scenarios warrant its use. The agent must 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.
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 states the tool performs a validation check but doesn't describe what happens during execution (e.g., whether it makes network calls, requires authentication, has rate limits, or what the output looks like). For a validation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance.
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?
Given the tool's moderate complexity (validation with 2 parameters) and lack of annotations or output schema, the description is minimally adequate. It covers the purpose but misses behavioral details and usage context. Without an output schema, it doesn't explain what the validation result looks like, leaving gaps in completeness for an agent to effectively use the 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 description coverage is 100%, with both parameters ('app_url' and 'manifest_url') clearly documented in the input schema. The description adds no additional parameter semantics beyond what the schema provides, such as explaining why these URLs are needed or how they relate to the validation. Baseline 3 is appropriate when the schema does the heavy lifting.
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: 'Check if your mini app meets Base featured placement requirements.' It specifies the action ('Check') and the target ('mini app'), but doesn't distinguish it from sibling tools like 'get_base_app_compatibility' or 'get_base_deployment_guide' which might have overlapping validation aspects.
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 prerequisites, when not to use it, or how it differs from sibling tools like 'get_base_app_compatibility' that might also check app requirements. The context 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.

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