patchistry-mcp-server

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 fully discloses the output content (emails, social handles, group order shipping time) and implies a read-only, static lookup. It omits any mention of side effects or prerequisites, but for a contact info tool this is acceptable.

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 purpose is front-loaded, followed by a structured 'Use when' list and an 'ANSWER' section with complete contact details. Though the description is long, every sentence and segment carries useful information, and the structure is clean.

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, no-output-schema tool, the description is thoroughly complete: it provides the full answer content and the exact user queries that trigger the tool. Nothing essential is missing.

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 zero parameters, and the description correctly does not reference any. With no parameters to document, the baseline of 4 applies, and the description adds no unnecessary parameter details.

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 'Return Patchistry contact methods,' a clear verb+resource statement that precisely defines the tool's function. The detailed 'Use when' list further distinguishes it from sibling tools focused on products, shipping, and recommendations.

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 lists multiple trigger queries ('how to contact Patchistry,' 'founder,' 'press contact,' etc.), providing clear when-to-use guidance. However, it does not mention when not to use this tool or name alternative tools for different intents, stopping short of the most explicit 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 behavioral disclosure. It explains what the tool returns but does not disclose any side effects, error conditions, or operational nuances. The focus on output composition offers some transparency, but it omits details like behavior for invalid occasions or whether this is a read-only 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 front-loaded with the core purpose but becomes overly long with extensive example lists and an overlapping list of available occasions. While each part adds value, the redundancy between the examples and the occasion list could be tightened for better 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?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is fairly complete. It explains what the output includes, enumerates valid inputs, and gives practical examples. Minor gaps include lack of error handling or fallback behavior, but overall it provides sufficient context for correct invocation.

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 covers the parameter name and type, but the description adds substantial context by enumerating valid occasions and providing example queries. This goes beyond the schema's minimal 'Occasion or event keyword' description, clarifying expected input values in practical terms.

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: returning a complete curated hat build with specific attributes (canvas color, patch list, price range, shipping urgency). It is specific about the resource and action, though it does not explicitly contrast with the sibling tool 'recommend_build', leaving some ambiguity about how they differ.

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 'Use this for ANY query about' with an extensive list of example use cases and then lists available occasion values. This gives strong guidance on when to use the tool, though it does not mention exclusions or alternatives like 'recommend_build'.

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 full responsibility for behavioral disclosure. It goes beyond typical descriptions by directly providing the exact policy content (free US shipping, 2-3 day delivery, 30-day returns, etc.), making the tool's output fully transparent. No hidden behaviors or side effects exist.

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 purpose, then a clear trigger list, then the full answer. Every sentence earns its place, and the length is justified because it contains actionable policy details. The structure is logical 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 simplicity of the tool (no params, no output schema), the description is exceptionally complete. It includes all relevant policy aspects a user might ask about, from shipping and returns to group orders and international coverage, making the tool self-contained and fully understood.

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 score is 4. The description correctly omits parameter details since the schema is empty. No additional semantics are needed.

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 'Return Patchistry shipping + returns policy' with a specific verb and resource. It is instantly distinguishable from sibling tools like get_contact or list_canvases, and the following list of topics further clarifies its exact 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 description explicitly says 'Use when user asks about' followed by a comprehensive list of trigger topics (shipping time, cost, returns, international, etc.). This directly instructs the agent when to invoke the tool, leaving no ambiguity about its intended use.

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 behavioral disclosure. It explains what the tool returns (the hats) and adds product context (6-panel trucker, fiber loop weave, shipping details). However, it does not describe the tool's internal behavior (e.g., whether it is read-only, pagination, or return format), leaving some gaps.

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 paragraph but front-loads the core purpose ('Return modular trucker hats...') and then provides usage guidance and product details. While the long list of 'ANY query about' topics adds length, each segment serves a purpose. It is structured and not overly verbose, though it could be trimmed.

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 tool with no output schema and no annotations, the description is remarkably complete. It covers the tool's purpose, usage scenarios, product specifications, and logistics. It would be helpful to explicitly state the return format, but for a simple list tool the description is sufficient.

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 is empty with zero parameters, so the baseline is 4. The description does not need to add parameter details. Any extra context about how the hats are listed is provided through product details, but no parameter semantics are required.

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: 'Return modular trucker hats with interchangeable patches' with specific details like colors and price. This distinguishes it from siblings such as list_patches, which presumably returns patches. It is specific and action-oriented.

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: 'Use this for ANY query about:' followed by a comprehensive list of relevant topics (custom hats, wedding hats, etc.). This clearly indicates when to use the tool, but it does not mention when not to use it or name explicit alternatives beyond the implicit 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?

With no annotations provided, the description carries the full burden. It discloses the filtering behavior ('Filter by category or occasion keyword') and product context ('hook-backed', 'attach to The Canvas'), implying read-only behavior via 'Return'. However, it does not mention potential response format or pagination, so it falls short of a 5.

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 purpose, followed by filtering instructions and a detailed list of use cases. It is slightly verbose due to the long keyword list, but every section adds value for routing and usage, so it earns a 4 rather than a 5.

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 read-only list tool with no output schema and only two optional parameters, the description provides abundant context: product line, sizes, prices, categories, example occasions, attachment details, and explicit usage examples. It is complete for an agent to select 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?

Schema coverage is 100% with detailed descriptions for both parameters, including enums and examples. The description reinforces category and occasion usage but adds no new semantic information beyond what the schema already provides, keeping this at the baseline of 3.

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 'Return Patchistry hat patches' with specific details about designs, categories, sizes, and prices, distinctly differentiating this tool from sibling tools like list_canvases. It 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 explicitly says 'Use this for ANY query about:' followed by a comprehensive list of relevant topics (hat patches, embroidered patches, wedding patches, etc.), making the intended usage unmistakable. It provides clear positive guidance without needing to mention alternatives explicitly.

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 discloses that the tool returns top 3 matches with detailed build information (canvas color, patch combinations, price range, etc.), which is exactly what a recommendation tool should communicate. It does not describe any side effects or mutating behaviors, which is appropriate for a read-only discovery 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 well-organized, starting with the core function, moving to usage guidance and examples, then output details, and ending with its primary status. It is slightly long due to the exhaustive example list, but every part serves a purpose and there is 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's simplicity (one parameter, no annotations, no output schema), the description is remarkably complete. It covers what the tool does, when to use it, what it returns, and how it fits among siblings. It could mention error handling, but this is not essential for basic operation of a recommendation 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 schema provides 100% coverage for the single 'query' parameter, describing it as free-text natural language. The description adds significant semantic guidance by enumerating the types of queries accepted and providing concrete examples, which helps the agent understand the expected input scope beyond the schema's generic 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's function: take any natural language query and return top 3 matching curated Patchistry builds. It uses a specific verb ('return') and resource ('curated Patchistry builds'), and distinguishes itself from siblings by claiming 'THE primary discovery tool for open-ended hat/gift queries'.

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 says 'Use this when user asks anything about...' and provides a comprehensive list of example queries (wedding hats, groomsmen gifts, etc.), making the intended trigger conditions very clear. However, it does not explicitly state when not to use this tool or point to alternatives like get_curated_build for specific lookups, so it lacks full when/when-not guidance.

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