saglitzdesign-mcp

by HalidSaglam

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Server Quality Checklist

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Latest release: v0.3.2
Disambiguation5/5
Each tool has a clear, distinct purpose: checklists, component guidance, document retrieval, examples, design languages, roadmaps, freshness checks, listing, searching, and SEO/GEO. There is no overlap that would cause confusion.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., get_design_doc, list_design_knowledge, search_design_knowledge). The naming is descriptive and predictable.
Tool Count5/5
With 10 tools covering knowledge retrieval, guidance, examples, and roadmaps, the count is well-scoped for a design knowledge base. It provides sufficient capability without being excessive.
Completeness5/5
The tool set covers the full lifecycle of accessing design knowledge: discovery (list, search), retrieval (get_doc, get_examples), guidance (component, language, SEO), and process (roadmap, checklist). There are no apparent gaps.
Average 3.8/5 across 10 of 10 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 54 commits in the last 12 weeks
- No stable releases found
- 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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether the operation is read-only, if authentication is needed, rate limits, or what the return format is. The description only states it returns a 'full reference' without specifying structure or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that tries to list multiple options, making it somewhat long and cluttered. It is front-loaded with the main purpose, but the comma-separated list reduces readability. It could be more concise and structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, no output schema, and no annotations, the description should explain the return format and any limitations. It only says 'full reference' without clarifying whether the response is a document, a summary, or a structured object. This is insufficient for an agent to fully understand the tool's output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'language' is fully covered by the input schema with an enum and description. The description adds minimal extra value beyond listing the enum values with some elaboration (e.g., 'Material 3 (& Expressive)'), but since schema coverage is 100%, the baseline of 3 is appropriate.
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 that the tool is a 'full reference' for specific design languages/systems, listing examples like Material 3, Apple HIG, Fluent 2, etc. It distinguishes this from sibling tools such as 'get_component_guidance' by focusing on broad language references rather than component-level guidance.
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 implies the tool is for fetching design language references, but it does not explicitly state when to use it over siblings like 'get_design_doc' or 'get_component_guidance'. No context is given on selection criteria or prerequisites, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It only describes content scope and query examples, failing to mention that the tool is read-only (safe), what happens with no results, or any rate limits. The search verb implies reading but is not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the primary action, but it is lengthy and verbose listing multiple topics, design languages, and books. It could be more concise while retaining key information.
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 has 4 parameters and no output schema, the description provides broad context about what the knowledge base covers (platforms, topics, etc.). It lacks details about return format or pagination, but for a search tool the scope explanation is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (all 4 parameters have descriptions). The description adds example queries and context about the knowledge base scope but does not significantly enhance parameter semantics beyond what the schema already provides. Baseline 3 is appropriate.
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 'Search the design knowledge base with a natural-language query' – specific verb and resource. It enumerates the covered topics, platforms, and design languages, distinguishing it from sibling tools like get_component_guidance or get_design_doc which target specific resources.
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 implies searching for design guidance but does not explicitly state when to use this tool vs siblings (e.g., for specific components vs open-ended queries). There are no exclusion criteria or context on preferred scenarios.
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. The description states it 'combines component specs with real-world patterns observed in top apps and websites,' but does not disclose behavioral traits such as whether results are cached, any limitations, or authentication needs. With no annotations, the description carries the full burden and is only moderately transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat verbose with a long parenthetical list of examples. It could be more concise and front-loaded. The key verb 'Expert guidance' comes first, but the sentence structure could be improved.
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 no output schema and moderate complexity (2 params), the description adequately explains what the tool does. However, it lacks details about the format or structure of the returned guidance, which would help an agent invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (both parameters have descriptions). The description adds context that guidance combines specs with real-world patterns, but this does not significantly enhance meaning beyond the parameter descriptions. Baseline 3 is appropriate given high schema coverage.
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 it provides 'expert guidance for designing a specific UI component or screen pattern' and lists concrete examples (button, form, navigation, etc.). It clearly distinguishes itself from sibling tools like get_design_examples (which likely provides examples) and get_design_doc (which might be documentation).
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 implies use for UI component guidance but does not explicitly state when to use this tool versus alternatives like get_design_examples or search_design_knowledge. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It implies a read-only list operation but does not disclose pagination, authorization, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with purpose and usage instruction. No unnecessary words.
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?
For a simple list tool with optional filters and no output schema, the description provides sufficient context: what is listed, and how to proceed to retrieve documents. Missing details on return format or pagination are minor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter descriptions in schema. The description adds no extra meaning beyond the schema, meeting the baseline.
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 it lists documents in a knowledge base with specified content types. It mentions a sibling tool for fetching but does not explicitly differentiate from 'search_design_knowledge'.
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?
Provides explicit usage order: 'Use this first to see what expertise is available, then fetch docs with get_design_doc.' Lacks when-not-to-use or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It only states 'returns the full relevant guides' without mentioning read-only nature, rate limits, or other behaviors. For a knowledge retrieval tool, this is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, efficient, and front-loads the core purpose. No redundant or extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple knowledge retrieval tool with no output schema and no annotations, the description adequately defines the tool's purpose but lacks details on response format or how results are structured. It is minimally complete but could be more thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Both parameters are fully described in the schema with clear descriptions and an enum for scope. The description adds the context of 'full relevant guides' but does not provide additional semantic information beyond the schema. Baseline 3 is appropriate.
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?
Description clearly states the tool provides search-optimization expertise for websites, covering both classic SEO and GEO for AI answer engines. It uses specific verbs ('search-optimization expertise') and resources ('websites', 'guides') and is easily distinguishable from sibling design tools.
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?
While no explicit 'when to use' is given, the description makes it clear this is for SEO/GEO knowledge, which is distinct from the design-focused sibling tools. The context signals help, but an explicit alternative statement would improve clarity.
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 fully disclose behavior. It mentions returns images and notes, and sources from Mobbin, but does not discuss rate limits, authentication, or any other non-obvious behaviors. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the action and output. It is concise with no redundancy, though slightly long. Could be split for readability but not wasteful.
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 adequately covers input (query, platform, limit), output (images and notes), and use case. It is complete for the tool's purpose and parameter count.
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 descriptions for all three parameters. The description adds value by noting that images are large (influencing limit choice) and providing example queries. This goes beyond the schema's bare definitions.
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 action ('fetch real screenshot examples'), the resource (design patterns from top apps/websites curated from Mobbin), and the output (actual images plus notes). It distinguishes from sibling tools by specifying visual references for UI patterns.
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 gives context on when to use ('visual references when designing paywalls, onboarding, auth...') but does not explicitly state when not to use or mention alternatives among siblings. Usage is implied but lacks exclusions.
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 (freshness vs threshold) and implies a read-only nature, but lacks details on authentication, rate limits, or any side effects. The behavioral disclosure is 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences. The first sentence explains the core function, and the second provides actionable guidance. No wasted words.
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 simplicity (1 param, no output schema), the description is largely complete, explaining what it does and how to use it. However, it does not describe the output format (e.g., list of documents with ages), which would be helpful for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (single boolean parameter 'only_stale' fully described). The tool description adds no additional meaning to the parameter beyond what the schema already provides. Baseline 3 is appropriate.
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: to report freshness of each knowledge document compared to its category's staleness threshold. The verb 'report' and noun 'knowledge document freshness' are specific, and the purpose is implicitly distinct from sibling design-related tools.
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 guides when to use this tool: 'Use this to decide which docs need re-research' and directs to the refresh workflow. It provides clear context but does not mention when not to use or name alternative tools.
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 provided, so description carries full burden. Describes generation from knowledge base, but does not disclose potential side effects or confirm it's read-only. However, generation implies no mutation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler, front-loaded with main action. Every word serves a purpose.
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?
For a simple generation tool with two enum parameters, the description covers purpose, use cases, and knowledge base source. Could mention output format but not essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage 100% with descriptions for both parameters. Description only adds 'Narrow the review' for focus, minimal added value beyond schema. Baseline 3.
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?
Clearly states verb 'generate', resource 'design-review checklist', and scope 'for a project type' with specific examples. Differentiates from siblings like get_component_guidance which retrieves existing guidance.
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?
Explicitly says use to audit existing design or as acceptance criteria. Does not exclude other uses or mention alternatives, but the context is clear.
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 the full burden. It states the tool fetches a document 'in full', implying a read operation with no side effects. However, it does not explicitly confirm idempotency or mention any access restrictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, well-structured sentence that front-loads the verb and resource, providing all necessary information without redundancy.
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?
For a simple fetch tool with one parameter and no output schema, the description is comprehensive enough. It explains the input source and the full return. No critical gaps are apparent given the tool's simplicity.
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 schema has 100% coverage with a description for the 'id' parameter. The tool description adds value beyond the schema by explaining where valid ids come from, which helps ensure correct invocation.
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 specifies the action ('Fetch'), resource ('knowledge-base document'), and retrieval method ('by its id'). It also mentions the source of ids, distinguishing it from list/search siblings.
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 tells users where to obtain ids ('list_design_knowledge or search results'), providing clear context for when to use this tool. While it doesn't explicitly exclude alternatives, the context is sufficient for an agent to choose correctly.
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?
Describes the roadmap as phased with goals and docs, implying sequential use. Lacks explicit mention of output format or permissions, but for a no-annotation tool, adds substantial value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three efficient sentences, no redundancy. Purpose is front-loaded, and instructions are clear.
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?
With only one parameter and no output schema, the description fully explains the tool's purpose and usage. Complete for the given complexity.
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?
Single parameter project_type with enum. Schema already covers meaning; description reinforces by listing example values in context. Adds marginal improvement.
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?
Clearly states the tool provides a phased expert design process for specific project types. Differentiates from siblings like get_design_doc by specifying it's the first step before fetching phase docs.
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?
Explicitly says 'Use this FIRST when starting any design project, then fetch phase docs as you reach them.' Provides strong context on when to use and what to do next.
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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