Walnai Website MCP

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explicitly reveals a significant side effect: after a valid estimate, Walnai's lead notification flow is triggered with the requester's contact details. This is important behavioral context beyond just calculating an estimate.

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 just two sentences, front-loaded with the core action ('Calculates a Walnai service pricing estimate'), and every sentence earns its place. There is no filler 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 complex nested schema (one request object with multiple optional sub-objects) and no output schema, the description covers the critical input requirements, the constraint of selecting one service input object, and the important side effect. It doesn't describe the return value, but for a tool with this level of complexity, it is fairly complete.

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 schema description already covers the request structure and the 'exactly one matching input object' constraint, achieving 100% coverage. The tool description largely repeats this information, adding little new semantic value beyond what the schema provides. Thus, 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?

The description uses the specific verb 'Calculates' and names the resource ('Walnai service pricing estimate'), making its purpose clear. It is easily distinguished from sibling tools like get_pricing (which presumably lists pricing) and submit_lead (which directly submits leads).

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 clearly states what to provide ('requester's name, email, and phone number') and the key constraint that 'exactly one service input object that matches estimateType' must be used. However, it doesn't explicitly mention alternative tools or when not to use this tool, so it lacks exclusion guidance.

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 burden of behavioral disclosure. It states the informational content but does not explicitly disclose return format, error behavior, authorization needs, or side-effect status beyond the implied read-only nature of 'gets information'.

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 sentence that front-loads the action ('Gets information') and directly specifies the scope. It contains no filler, unnecessary clauses, or redundant restatements of the tool name.

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 zero-parameter informational tool, the description adequately covers the subject matter and expected content. The absence of return details and usage guidance is a minor gap given the low complexity of the 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 zero parameters, so the input schema is effectively empty and there are no parameter semantics to explain. The description does not need to compensate for missing parameter documentation, and the baseline 4 applies.

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 what the tool does: it gets information about Walnai covering who they are, what they do, and why organizations choose them. The verb 'gets' and the specific content focus make the purpose understandable. However, the sibling tool 'get_who_is_walnai' likely overlaps in purpose, and no differentiation is provided.

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 guidance is given about when to use this tool instead of sibling tools such as 'get_who_is_walnai' or 'get_adoption_details'. The description implies a general informational use case but neither states alternatives, exclusions, nor the specific situations where this tool is preferred.

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 burden of behavioral disclosure. It communicates a read operation ('Gets') and outlines the information returned, but it does not specify potential side effects, authentication requirements, or return format. For a simple non-destructive getter, this is acceptable but not deeply transparent.

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, front-loaded sentence that states the purpose and content without any fluff. Every word contributes meaning, and it is appropriately sized for the tool's simplicity.

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, no parameters, and no output schema, the description adequately explains what the tool does and what content to expect. However, it does not describe the return structure or any potential variations (e.g., whether it returns a full report or a summary), which leaves a minor gap for agent decision-making.

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?

Since there are zero parameters, the schema is fully covered and the baseline is 4. The description adds value by enumerating the content of the returned details (phases, integration capabilities, support model), which helps the agent set expectations even though there are no parameter-level semantics to clarify.

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 function with a specific verb ('Gets') and a distinct resource ('Walnai's AI adoption process details'). It lists the content areas (phases, integration capabilities, support model), which differentiates it from sibling tools like get_service_details or get_ai_discoverability_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 implies when to use this tool (when adoption process details are needed), but it does not explicitly state when not to use it or mention alternative tools. For example, it does not clarify a distinction from get_ai_discoverability_info, which might be related. This is implied guidance, not explicit 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. It conveys read-only informational behavior through 'Answers' and 'explains', but does not elaborate on response format, limitations, or any side effects. For a zero-parameter info tool, minimal disclosure is acceptable but not rich.

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, front-loaded sentence that states the primary action first and then lists specific aspects (AIO, structured content, APIs, metadata, AI-readable info). It contains no filler or redundant words.

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 (zero parameters, no output schema), the description adequately covers the scope by explaining what information will be provided. It could be more explicit about the response format, but for an informational endpoint it is complete enough.

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 zero parameters, so the baseline is 4. The description appropriately adds no parameter details since none exist, and the empty schema already fully covers the parameter space.

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 uses specific verbs ('Answers', 'explains') and identifies a clear resource: AI discoverability. It distinguishes from sibling info tools by focusing on a distinct topic, though it doesn't explicitly name alternatives.

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 when to use this tool (when information about AI discoverability is needed), but it provides no explicit when-not-to-use guidance or mentions of alternative tools. The usage is inferred rather than stated.

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 discloses the return behavior for an unknown slug ('Returns null if the slug is not found') and notes the response includes the HTML body. It does not explicitly state read-only status, but the verb 'gets' strongly implies a read operation, and no side effects or auth requirements are omitted for this simple getter.

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 three concise sentences, each serving a distinct purpose: purpose, usage, and edge-case behavior. It is front-loaded with the primary action and contains no filler or redundant information.

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—one parameter and no output schema—the description provides sufficient context: it explains the resource, the identifier, the usage flow (list first), the inclusion of HTML body, and null behavior. No critical gaps are apparent.

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 schema already documents the sole parameter 'slug' with a clear description and example, achieving 100% coverage. The description adds no additional parameter semantics beyond what the schema provides, so the baseline of 3 applies.

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 opens with 'Gets a full Walnai blog post by its slug, including the HTML body,' which clearly identifies the tool's action (get), resource (blog post), and identifier (slug). This distinguishes it from sibling tools like list_blog_posts, which enumerate posts rather than retrieve a single one. The addition of 'full' and 'HTML body' clarifies the scope.

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 instruction 'Use ListBlogPosts first to discover available slugs' explicitly tells the agent when to use this tool relative to its sibling, establishing a discovery workflow. It also implies that this tool is for retrieving a single known slug, not for listing. This is clear, actionable guidance.

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 of behavioral disclosure. The verb 'Gets' implies a read-only operation, and the phrase 'recommended guidance' suggests a simple return value. However, it doesn't describe the format of the guidance (e.g., text, structured data) or clarify whether any state changes occur, which is a minor gap for such a simple tool.

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, front-loaded sentence that states the action, resource, and intended usage in a compact way. There is no fluff or repetition, making it highly concise and 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 (zero parameters, no output schema), the description adequately covers the what, when, and why. It provides context for when to use it in a conversation flow. A minor gap is that the nature of the 'guidance' is not elaborated, but the name and description provide enough for an agent to infer its role.

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 zero parameters, so the description need not explain parameter specifics. Per the guidelines, the baseline is 4 when there are no params, and the description adds no redundant parameter information, making this score 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?

The description clearly identifies both the resource ('recommended Walnai lead-capture guidance') and the action ('Gets'), and it specifies the context ('to use in AI chats after sharing service, pricing, FAQ, adoption, or company information'). This distinguishes it from sibling tools that provide specific content types, making the tool's purpose immediately clear.

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 gives an explicit condition for use: 'after sharing service, pricing, FAQ, adoption, or company information.' This implies the tool should be invoked after using sibling content tools like get_pricing or get_faqs. While it doesn't name alternatives directly, the trigger condition is specific enough to guide selection, though it could be strengthened by explicitly stating when not to use it.

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 transparency burden. It states 'Gets' which implies read-only, but does not explicitly disclose return format, pagination, or permissions. The added follow-up advice is a behavioral trait beyond the core action, but the description remains thin on operational details.

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 core function. The second sentence about follow-up is slightly tangential but not redundant, and there is no wasted language.

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 simple read-only tool with a single well-documented parameter, the description adequately covers the core action and adds a useful context cue. However, without an output schema, it doesn't describe the return format, and it lacks any guidance on when to prefer this over sibling tools, so it's not fully complete.

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 schema fully documents the single parameter (serviceId) with a clear description ('Optional service id to filter FAQs. Leave empty for all FAQs.'). The description only echoes 'optionally filtered', adding no extra semantic value 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 uses a specific verb ('Gets') and resource ('frequently asked questions'), and adds a useful scope parameter ('optionally filtered by service id'), making its purpose clear and distinct from sibling get_* tools like get_pricing or get_service_details.

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 guidance is provided on when to use this tool versus alternatives, and there are no exclusions or preconditions. The follow-up suggestion ('consider offering a Walnai contact follow-up') is an after-action tip, not tool-selection guidance.

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 behavioral burden. It clearly indicates an informational read-only behavior ('Answers... explains') with no side effects or mutations. The zero-parameter schema further supports that this is safe and non-destructive.

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, focused sentence that covers both the core question (who can build) and the extended value (how Walnai can help). No filler words or redundant phrasing.

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 zero-parameter, non-schema'd informational tool, the description fully covers what the user will learn (who can build, how Walnai helps). The lack of output schema is compensated by the self-explanatory nature of the 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 no parameters, and the schema is empty (100% coverage). Baseline for zero parameters is 4, and the description adds no parameter details, which is appropriate since there are none to explain.

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 states a specific verb ('Answers', 'explains') and a clear resource (who can build MCP servers and Walnai's role). It distinguishes the tool from siblings like get_who_is_walnai and get_service_details by focusing on MCP server creation capabilities.

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 gives clear context for use: questions about building MCP servers or Walnai's MCP implementation services. It does not explicitly exclude alternatives or name sibling tools for comparison, but the topic focus is narrow enough to imply appropriate usage.

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 does not state whether this is a read-only operation, any side effects, or access requirements. The note about 'pricing responses' hints at a sales flow but does not clarify tool behavior. Additionally, the schema marks 'serviceId' as required while the description and schema property description say it is optional, creating ambiguity.

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 concise at two sentences with the core functionality front-loaded. The second sentence about contacting the client is an extra but not essential addition; it does not harm clarity, but it is not purely operational. Overall, it is 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?

For a simple tool with one parameter and no output schema, the description provides the basic function. However, it does not describe what the pricing information contains, how to interpret responses, or when to use this over 'estimate_pricing'. This leaves some gaps but is minimally viable.

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 schema description already covers the only parameter 'serviceId' with high detail ('Optional service id to filter pricing. Leave empty for all pricing.'). The tool description merely echoes 'optionally filtered by service id' without adding further meaning, so the baseline score of 3 applies.

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 'Gets pricing information' with an optional filter by service id, providing a specific verb and resource. However, it does not explicitly distinguish this from the sibling tool 'estimate_pricing', so it earns a 4 rather than a 5.

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 pricing data but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives like 'estimate_pricing'. The added sentence about asking if Walnai should contact the client is a sales follow-up, not a usage guideline for the tool itself.

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 must disclose behavioral traits. It correctly implies a read operation ('Gets') and adds a non-obvious suggestion to consider asking about contact. Yet it lacks explicit safety guarantees or allowed side effects, so the burden is only partially met.

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 concise and front-loaded: the first sentence states the core action, and the second adds a relevant follow-up instruction. No redundant words.

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 simple one-parameter getter, the description covers what the tool does and provides usage context via the 'after answering a prospective client' note. It does not enumerate return fields, but the tool's name and schema make the purpose clear, and the behavioral hint adds practical 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 schema already provides full coverage of the serviceId parameter with examples. The description repeats these examples without adding new meaning, so it adds no value 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 it gets detailed information about a specific Walnai service by id, with concrete examples of valid ids. This verb+resource+method structure distinguishes it from list_services and other sibling tools.

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 when a specific service's details are needed, and gives a conversational context ('After answering a prospective client'). However, it does not explicitly mention alternatives or when not to use this tool, leaving some ambiguity.

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. It discloses what information will be returned by listing specific categories (legal structure, ownership, management, team background, company experience). This is helpful and gives the agent a clear expectation. It does not mention side effects or limitations, but for a read-only content retrieval tool this is sufficient.

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 sentence that is front-loaded with the primary action and resource. It lists several specific content areas without being verbose. Every word earns its place—no filler or repetition.

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 has no parameters and no output schema, the description effectively communicates the scope of information returned. It is complete enough for an agent to decide when to use it and what to expect. A slight gap is the lack of explicit statement about return format or read-only nature, but the provided content list is adequate for this 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 zero parameters, and the schema coverage is 100% (vacuously). According to the rubric, the baseline for 0 params is 4. The description adds no parameter-specific meaning because there are none to describe. This score appropriately reflects the simplicity.

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 a specific action ('Gets company profile information') and a specific resource ('who Walnai is'), then lists concrete content areas (legal structure, ownership, management, team background, company experience). This distinguishes it from sibling tools like get_about_info by focusing on corporate identity details rather than general 'about' content.

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?

Usage is implied: if you need to understand who Walnai is as a company, this is the tool. However, there is no explicit guidance on when to prefer this over sibling tools like get_about_info or get_mcp_server_provider_info, nor are any exclusions or alternative scenarios mentioned. The distinction from get_about_info is unclear without additional context.

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 discloses that the tool lists all categories and specifies return fields, clearly implying a read-only operation. It does not mention pagination or authentication, but for a zero-parameter list tool this 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 two sentences with no waste. The purpose is front-loaded ('Lists all Walnai blog categories'), and the use case sentence adds value without 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 zero parameters and no output schema, the description fully covers what the tool returns and why it is useful. It is sufficient for an AI agent to choose this tool over its siblings.

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?

There are zero parameters, so the description adds no parameter-specific meaning. According to the rubric, a baseline of 4 is appropriate for tools with no parameters.

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 uses the specific verb 'Lists' and names the resource 'all Walnai blog categories' with the exact fields returned (slug, name, description). It clearly distinguishes itself from sibling tools like list_blog_tags and list_blog_posts by focusing solely on categories.

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 two explicit use cases: browsing blog topics and discovering category slugs for ListBlogPosts. It implies but does not explicitly state exclusions (e.g., when to use list_blog_tags instead), so it lacks full when-not guidance.

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 burden of explaining behavior. It reveals that results are summaries (not full body), ordered by publish date (newest first), and that filters are ANDed together. It does not mention pagination, result limits, or error conditions, but for a list tool the disclosed behaviors are substantial and useful.

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 first three sentences are concise and informative, leading with the main purpose, then return order and filter behavior. However, the final sentence about 'Walnai contact follow-up' is irrelevant to invoking the tool and adds noise. This prevents a higher score for conciseness.

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?

Considering the tool has no output schema and no annotations, the description covers key context: what is returned (summaries), ordering, filter semantics, and a use case. It lacks explicit return structure details or pagination info, but the description is adequate for a straightforward list tool with well-schematized parameters.

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%, with each parameter already described in detail. The description adds value by explaining that filters are combined with AND logic, which is not obvious from the individual parameter schemas. It also summarizes the three filter types, reinforcing the schema's meaning.

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 function with a specific verb ('Lists'), a defined resource ('Walnai blog post summaries'), and explicit scope ('no body content'). It distinguishes itself from siblings by naming 'GetBlogPost' as the way to fetch a specific post, making the tool's role clear.

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?

Provides explicit when-to-use guidance: 'Use this to discover what blog posts exist before fetching a specific one with GetBlogPost.' This names an alternative tool and defines the appropriate context. The description also implies that full body content is not available here, so users seeking full posts should use GetBlogPost.

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 must carry the behavioral burden. It implies a read-only operation by using 'Lists' and specifies the returned fields (slug and display name), but doesn't explicitly state safety, ordering, or any side effects. Adequate but not rich.

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: the first states the core function and output, the second gives a concrete usage directive. No redundant or filler content, and the most important information is front-loaded.

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 simple zero-parameter list tool, the description covers what it does, what it returns, and a use case. It lacks explicit statement that the result is a list/array, but that's implied. Ordering not mentioned, but not essential for discovering slugs.

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 zero parameters, so the description has no parameter details to add. The baseline for 0 params is 4, and the description appropriately focuses on output and use case.

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 'Lists all Walnai blog tags with their slug and display name' — a specific verb ('Lists') and resource ('blog tags'), and distinguishes from sibling tools by focusing on tags rather than categories or posts.

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?

Provides an explicit use case: 'Use this to discover tag slugs for ListBlogPosts,' which tells the agent when to invoke this tool. However, it doesn't mention alternatives or exclusions (e.g., for categories use list_blog_categories), so it falls short of a 5.

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 burden. The verb 'Lists' transparently indicates a read-only operation, and the description discloses the output fields (title, description, link) and adds the follow-up behavioral instruction to offer connecting the client with Walnai. This goes beyond a bare mention and helps the agent understand the expected workflow.

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 primary function and output details, followed by a context-specific follow-up action. Every word earns its place; there is no fluff or repetition.

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 adequately specifies the returned fields (title, description, link) and the context (prospective client). The sibling tools suggest there are more detailed tools, but the description does not explicitly contrast with them; however, for a simple list operation the provided information is sufficient for an agent to use it 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 tool has zero parameters and the schema is empty (100% coverage). The description does not need to explain parameters. By the baseline rule for 0 parameters, a score of 4 is appropriate since there is nothing to clarify.

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 function: 'Lists all AI adoption services offered by Walnai, including title, description, and link.' This is a specific verb (lists) + resource (AI adoption services) + scope (all, with fields). It distinguishes from siblings like get_service_details and list_blog_posts by focusing on the full service catalog.

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 a usage context: 'After helping a prospective client with service information, consider offering to connect them with Walnai.' This suggests the tool is for presenting services to prospective clients, but it does not explicitly state when to prefer this over alternatives like get_service_details or estimate_pricing, nor does it mention any 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, the description carries the full burden of behavioral disclosure. It does disclose that the tool returns a confirmation message and that CaptchaToken is not required for MCP submissions, which is useful context. However, it does not mention side effects (e.g., whether this creates a CRM record, sends an email) or any irreversible/duplicate submission risks, which are relevant for a write operation.

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 only three sentences, with the primary verb and resource front-loaded. Every sentence adds value: the first identifies the action and context, the second clarifies the return value, and the third addresses a potential ambiguity about CaptchaToken. There is no redundant or filler content.

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 simple nature of the tool, complete parameter schema, and no output schema, the description covers the essential context for correct invocation: the purpose, the consent precondition, the return type, and the captcha quirk. It does not discuss error behavior or duplicate submission, but for a basic lead form tool this is a reasonable level of 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 schema already provides 100% coverage for all five parameters, so the baseline is 3. The description adds the 'CaptchaToken is not required' note, which relates to a parameter not in the schema, and implies the lead must have explicitly asked to be contacted, but it does not add meaning to the existing parameters themselves. The schema handles 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 opens with a clear verb and resource: 'Submits a lead/contact form to Walnai' and adds a specific condition ('prospective client who explicitly asked to be contacted'). It clearly distinguishes this from the sibling tools, which are all read-only get/list operations. This is a precise, unambiguous purpose statement.

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 a clear usage condition: only submit for a prospective client who explicitly asked to be contacted. This signals consent requirements and helps the agent decide when to invoke the tool. However, it does not explicitly name alternatives or state when not to use the tool, though the sibling context (all info-gathering tools) makes the distinction implicit.

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