docs-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It fails to mention any behavioral traits such as authentication needs, rate limits, error handling, or response format. The only extra detail is the flexible input format, which is insufficient for a complete behavioral picture.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the core purpose, and includes a clarifying example. No superfluous information. Every word contributes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description covers the basic purpose and parameter flexibility. However, it omits details about the return format, possible errors, and how it relates to other documentation tools. It's adequate but leaves gaps for an agent to infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is high at 100%, so the schema already documents both parameters thoroughly. The description adds the insight that component_name can be in any format (e.g., 'button' vs 'vaadin-button'), which is a minor but useful addition. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it returns Java API documentation for a specific Vaadin component. The title and description align, and the inclusion of 'Java API' distinguishes it from sibling tools like get_component_react_api or get_component_web_component_api. The component name flexibility is also noted.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving Java API docs, but provides no explicit guidance on when to use this tool over siblings, nor any conditions or prerequisites. The name alone differentiates, but without explicit direction, it's only moderately helpful.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description only mentions input flexibility. It does not disclose any behavioral traits such as data volume, side effects, authentication needs, or return format. With no annotations, the description carries the full burden but provides minimal transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with only two sentences, front-loaded with the core purpose, and contains no unnecessary information. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema and the presence of many sibling tools, the description is somewhat incomplete. It does not explain the return format (e.g., JSON structure, HTML) or provide context on how it relates to other tools. More information would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds value by explicitly stating that the component name can be in any format (e.g., 'Button', 'button', 'vaadin-button'), which is not fully captured in the schema's examples. This contributes meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns React API documentation for a Vaadin component and notes component name flexibility. However, it does not differentiate from sibling tools like get_component_java_api or get_component_web_component_api, so the purpose is clear but not distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The name implies React API usage, but no when-to-use or when-not-to-use advice is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 returns a 'comprehensive list' but doesn't specify if it's paginated, rate-limited, requires authentication, or what happens on errors. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior beyond the basic read operation implied by 'Returns.'

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose and details the scope of returned information. Every part earns its place without redundancy or unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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 parameter, no output schema, no annotations), the description is adequate but not complete. It covers the purpose and scope well, but lacks details on behavioral aspects like error handling or output format, which are important for a tool with no annotations or output schema. It meets minimum viability with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the 'version' parameter well-documented in the schema itself. The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or constraints. 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.

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 ('Returns') and resource ('components available in a specific Vaadin version'), and distinguishes it from siblings by specifying the scope of information returned (names, React component names, Java class names, npm packages). It doesn't just restate the name/title.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by specifying 'in a specific Vaadin version,' but doesn't explicitly state when to use this tool versus alternatives like 'get_component_java_api' or 'search_vaadin_docs.' It provides basic context but lacks explicit guidance on exclusions or comparisons with sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, and description only states what is returned. Lacks disclosure of side effects, error handling, required permissions, or output format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with main action, no wasted words. Efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for a simple retrieval tool, but could provide more context on output format or prerequisites. No output schema to compensate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds value beyond schema by indicating component name can be in any format (e.g., 'Button', 'button', 'vaadin-button'), which is not in the schema description. Schema already covers parameter descriptions well.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns styling/theming documentation for a specific Vaadin component, mentions both Java and React styling documentation, and distinguishes from sibling tools like get_component_java_api.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies use for styling needs but does not explicitly state when to use this tool versus alternatives like get_component_java_api or get_component_react_api. No exclusions provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It mentions fetching from external docs, hinting at network dependency, but does not disclose behavior like possible failures, rate limits, or return structure. The transparency is adequate 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the main action, no extraneous information. Every sentence is purposeful and concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description does not explain what the returned documentation looks like (string, JSON, etc.). The tool has low complexity and both parameters are well-documented in schema, but the lack of output details makes it incomplete for an agent to fully understand the tool's behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline 3. The description adds value by stating that the component name can be in any format (e.g., 'Button', 'button', 'vaadin-button'), which is not in the schema. This clarifies input flexibility beyond the schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns Web Component/TypeScript API documentation by fetching external docs. It uses specific verb 'Returns' and resource 'Web Component/TypeScript API documentation', and distinguishes from sibling tools like get_component_java_api or get_component_react_api.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for web component API but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites. Usage is implied by the resource type but not clearly contrasted with sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 mentions the tool retrieves 'complete documentation pages' and handles 'one or more file paths,' but lacks details on permissions, rate limits, or response format. This is adequate for a read operation but misses richer 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded and concise with two sentences that earn their place: the first defines purpose and usage, the second specifies parameter constraints. There is zero waste, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity is low (single parameter, read operation) and no output schema exists, the description is mostly complete. It covers purpose, usage, and parameter constraints, but lacks details on response format or error handling, which would enhance completeness for a retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents the single parameter 'file_paths' as an array of strings. The description adds minimal value by restating 'Provide file_paths only (array),' which doesn't enhance semantics beyond the schema. 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.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Retrieves complete documentation pages') and resource ('for one or more file paths'), distinguishing it from siblings like search_vaadin_docs by emphasizing full context beyond search results. It explicitly mentions the verb 'retrieves' and the target 'documentation pages,' making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool ('when you need full context beyond what search results provide'), distinguishing it from search_vaadin_docs. It also specifies constraints ('Provide file_paths only'), offering clear context for usage without exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 return format ('simple JSON object' with fields 'version, released'), which is helpful. However, it does not mention potential limitations like rate limits, error conditions, or whether the data is cached/real-time. For a read-only tool with zero annotation coverage, this leaves some behavioral aspects unspecified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise and front-loaded: the first sentence states the core purpose, the second provides usage context, and the third specifies the return format. Every sentence earns its place with no redundant or vague language, making it highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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 output schema, no annotations), the description is largely complete: it explains what the tool does, when to use it, and the return format. However, without annotations or output schema, it could benefit from mentioning any behavioral constraints (e.g., update frequency, error handling) to achieve full completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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% (though empty). The description does not need to explain parameters, so it appropriately focuses on the tool's purpose and output. A baseline of 4 is applied since no parameters exist, and the description adds value by clarifying the return format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Returns the latest stable version of Vaadin') and resource ('Vaadin'), distinguishing it from sibling tools like 'get_supported_vaadin_versions' (which returns multiple versions) or 'get_components_by_version' (which focuses on components). It uses precise language that leaves no ambiguity about what the tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 ('setting up new projects, checking for updates, or when helping with dependency management'), which helps differentiate it from sibling tools that retrieve component APIs, documentation, or multiple versions. However, it does not explicitly state when NOT to use it or name specific alternatives, which prevents a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 data for multiple major versions, includes specific fields (version number, release date, license requirement), and is read-only (implied by 'Returns'). It doesn't mention error handling, rate limits, or authentication needs, but for a simple query tool with zero parameters, this is reasonably comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core functionality in the first sentence, followed by a concise usage context. Every sentence adds value: the first defines the output, and the second explains its utility. There is no wasted text or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is largely complete. It clearly states what the tool does, its output fields, and use cases. However, it doesn't specify the exact format of the return data (e.g., JSON structure), which could be helpful for an agent, but this is a minor gap for such a simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 baseline is 4. The description adds no parameter information, which is appropriate since none are needed, and it doesn't detract from the schema's completeness.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Returns the latest stable release for each supported Vaadin major version') and resource ('Vaadin major versions'), distinguishing it from siblings like 'get_latest_vaadin_version' (singular) and 'get_components_by_version' (component-focused). It specifies the exact versions covered (25, 24, 23, 14, 8, 7) and output fields (version number, release date, license requirement).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 ('Useful for migration planning and understanding which versions are available'), which implicitly suggests alternatives like 'get_latest_vaadin_version' for a single version or 'get_components_by_version' for component details. However, it doesn't explicitly state when not to use it or name specific alternatives, keeping it at a 4.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It declares it returns documentation, which implies a read-only operation. However, it does not disclose any required permissions, network dependencies, or potential side effects. For a simple lookup, this is adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero waste. The first sentence defines the tool's action and scope; the second provides a practical tip. Information is front-loaded and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 enum parameters, no output schema), the description is sufficiently complete. It explains what is returned and for which themes. However, it does not detail the return format (e.g., JSON structure), which could be inferred but is not explicitly stated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the baseline is 3. The description adds extra context beyond the schema: it states the purpose (looking up correct CSS variables) and provides a usage hint about Base styles being available in all themes. This adds meaningful value over the enum descriptions in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns CSS custom properties documentation for a specific Vaadin theme (Aura, Lumo, or Base). It uses specific verbs ('Returns... documentation') and resource ('CSS custom properties for a theme'), and distinguishes from sibling tools which focus on component APIs and version info.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs when to use it: 'Use this to look up the correct CSS variables for the theme the application is using.' It also notes that Base style properties are available in all themes. However, it does not specify when not to use it or mention alternatives among sibling tools, though the context of siblings makes the tool's unique purpose clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description carries the full burden. It explains that the tool returns a primer addressing AI misconceptions and provides version-specific guidance. It does not detail auth needs or rate limits, but as a read-only lookup, the behavioral context is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with an important imperative and provides necessary context in a few sentences. While it could be slightly more concise, it effectively communicates key information without excess.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter invocation with no output schema, the description is comprehensive: it states what it does, when to use, and version handling. It fully equips an AI agent to decide and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds value beyond the schema by explaining that legacy versions return guidance on version-specific resources. This is not apparent from the parameter description alone, providing semantic enrichment.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns a comprehensive primer for modern Vaadin development and positions it as the first tool to use. It distinguishes itself from sibling tools like get_component_java_api by being a foundational resource.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs to use this tool first before working with Vaadin, and clarifies handling of legacy versions. However, it does not explicitly mention when not to use it or list alternatives, though the sibling context fills some gaps.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses hybrid semantic+keyword search and suggests deducing the development model. However, it doesn't specify the exact format of results (e.g., snippets vs. full text) or limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately long but front-loaded with purpose and usage. It could be slightly more concise, but every sentence adds value. The structure is logical.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complex search behavior and lack of output schema, the description covers main points but does not explain return format or pagination. It relies on sibling tool get_full_document for completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining hybrid search, how to deduce ui_language, and emphasizing that vaadin_version is required. This goes beyond the schema's property descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches Vaadin documentation and lists many specific topics (components, testing, etc.). It distinguishes itself from siblings like get_full_document by noting when to use that tool for full context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'USE THIS TOOL for questions about:' and lists a wide range of topics. It also instructs when to use get_full_document and how to deduce the development model for ui_language.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.