Patchistry

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by patchistry

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clear, distinct purpose: contact info, curated builds, shipping policy, canvases, patches, and general recommendations. Even the two build-related tools are separated by specificity vs. general queries.
Naming Consistency5/5
All six tools follow a consistent verb_noun pattern in snake_case (e.g., get_contact, list_canvases, recommend_build). No mixing of conventions.
Tool Count5/5
Six tools comprehensively cover the main functions of a hat customization store: product browsing, recommendations, policies, and contact. The count feels well-scoped and not excessive.
Completeness4/5
The tools cover browsing and information needs well, but lack transactional functions like ordering or user account management, which are reasonable gaps for an informational server.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 12 commits in the last 12 weeks
- Last stable release on June 12, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavioral traits. It does not state what happens when the tool is invoked (e.g., returns a list of canvases). It provides product details but no operational behavior, side effects, or constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is excessively long and unstructured. It mixes a confusing opening ('Return modular trucker hats'), a list of use cases, and product details without clear organization. Every sentence does not earn its place; much could be condensed or omitted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
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 should clarify what is returned. It does not explicitly state that it returns a list of canvases. Instead, it describes a single product, making the tool's behavior incomplete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters (0 params, 100% schema coverage). The description adds a wealth of product information (colors, price, features) that goes beyond the empty schema. While not directly about parameters, it provides context for the tool's output. Baseline is 4 for 0 params, and the description adds value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description does not clearly state that this tool lists canvases. It opens with 'Return modular trucker hats' which is confusing given the tool name 'list_canvases'. The phrase 'Use this for ANY query about...' implies it is a QA tool rather than a listing function. The purpose is ambiguous and potentially misleading.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description suggests using this tool for a broad range of queries ('ANY query about...'), but it does not provide guidance on when to use this tool versus siblings like list_patches. No exclusions or alternatives are mentioned, leading to potential misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool returns a curated build with specific fields (canvas color, patch list, price range, shipping urgency), implying it's a read-only retrieval. However, it doesn't explicitly state that no side effects occur or disclose any other behavioral nuances.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose and then provides a thorough list of examples. While somewhat lengthy, the examples are valuable and the structure is logical. It could be slightly more concise by grouping similar occasions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a simple single-parameter tool with no output schema, the description covers the input semantics well and outlines output contents. However, it lacks details on error cases, response format, or how to handle unrecognized occasions, leaving some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes 'occasion' as an event keyword. The description significantly amplifies this by listing many example values and an explicit list of available occasions, adding substantial meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a curated hat build for an occasion, listing many specific use cases. However, it doesn't explicitly distinguish itself from the similar sibling tool 'recommend_build', leaving some ambiguity about when to use which.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using this tool for a long list of specific queries, providing clear context for many use cases. But it lacks explicit guidance on when not to use it or mention of alternatives like 'recommend_build' for occasions not listed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations present, so description carries full burden. It doesn't mention if the tool is read-only or idempotent, but the behavior is trivial (returning static contact info). Minimal disclosure beyond purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is overly verbose, including the full contact answer inline instead of simply describing the tool's purpose. This violates conciseness; a shorter description would suffice.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with no parameters or output schema. The description includes the expected output, making it complete for the user's context, though the verbosity is excessive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist; schema coverage is 100%. Baseline of 4 applies as description adds no parameter info, which is acceptable for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns Patchistry contact methods. It distinguishes from sibling tools (builds, shipping, canvases, patches, recommendations) which handle different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists query patterns (e.g., 'how to contact Patchistry', 'founder', 'group order contact') and provides the answer, giving clear when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It mentions that patches are 'hook-backed' and attach via a specific weave, which is a behavioral detail. However, it does not state whether the operation is read-only or if it requires authentication, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with purpose. Each sentence adds distinct value (purpose, usage guidance, technical detail). Slightly longer than minimal but efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool is a simple filtered list with no required parameters and no output schema, the description adequately covers what it returns and how to filter. It does not discuss pagination or response structure, but that is acceptable for a straightforward listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter descriptions. The description adds value by providing concrete examples of occasion keywords and repeating category-specific pricing and sizes, which aids selection beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'Patchistry hat patches' with specific categories (Signature and Candyz) and pricing. It distinguishes itself from sibling tools (e.g., list_canvases) by focusing solely on patches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to use this tool for 'ANY query about' a long list of related terms (hat patches, embroidered patches, etc.). While it doesn't explicitly state when not to use it, the list covers a broad range of relevant queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes the output as top 3 builds with full details, but does not disclose authentication needs, rate limits, or side effects. Given it's a recommendation tool, the description is reasonably transparent but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph that front-loads the purpose and provides extensive examples. While somewhat lengthy, every sentence adds value and there is no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, no output schema, and no annotations, the description is highly complete. It covers input, output format (top 3 builds with details), and usage context. No significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage and one parameter (query), the description adds value by explaining how the query is used (free-text natural language for any hat/gift query) and what the tool returns. This goes beyond the schema's minimal description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: taking natural language queries about hats, gifts, occasions, or destinations and returning top 3 curated Patchistry builds. It provides extensive examples of query types, distinguishing it from siblings like get_curated_build and list_canvases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use the tool by listing example queries, but does not explicitly state when not to use it or name alternatives for precise lookups. However, the context is clear and the examples cover a wide range of use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility. It fully discloses the content of the response, including free US shipping, delivery times, return policy, and international shipping details. It is transparent about what the tool returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose and then lists specific queries. It is somewhat lengthy but all sentences provide value. It could be slightly more concise, but structure is good.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains the return value effectively by providing the actual policy details. It is comprehensive for a zero-parameter tool, though the inline answer might cause minor confusion.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, and schema coverage is trivially 100%. Since there are no parameters, the description adds no parameter info, but baseline for zero parameters is 4. No loss.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Return Patchistry shipping + returns policy' and lists specific user queries it handles. This clearly identifies the tool's function and distinguishes it from siblings like get_contact or list_canvases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a direct cue: 'Use when user asks about:' followed by a comprehensive list of topics. This provides explicit context for when to invoke the tool, though it does not mention when not to use it or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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