Components Build MCP

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between get_rules and list_rules, as both deal with rules documentation, which could cause confusion. However, descriptions clarify that get_rules provides detailed documentation with filtering, while list_rules offers a summary list, helping to mitigate ambiguity.
Naming Consistency5/5
Tool names follow a consistent verb_noun pattern throughout, such as check_compliance, generate_component, get_quick_reference, and grade_component. This predictability makes it easy for agents to understand and select tools without confusion.
Tool Count5/5
With 11 tools, the server is well-scoped for its purpose of component development and compliance. Each tool serves a specific function, from generating and grading components to accessing documentation, ensuring a comprehensive toolset without being overwhelming.
Completeness4/5
The tool surface covers key aspects like component generation, compliance checking, grading, and extensive documentation access. A minor gap is the lack of tools for updating or deleting components, but agents can likely work around this given the server's focus on development and compliance rather than full lifecycle management.
Average 3/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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 Apache 2.0.
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.
Add a glama.json file to provide metadata about your server.
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.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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, so the description carries the full burden of behavioral disclosure. It states the tool gets a template 'for reference', implying a read-only operation, but doesn't specify if it's safe, if authentication is needed, what the output format is, or any error conditions. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly. There's no wasted verbiage, and it earns its place by conveying the essential function.
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 lack of annotations and output schema, the description is incomplete for a tool that retrieves data. It doesn't explain what 'for reference' entails, what the return value looks like, or any limitations. With no structured fields to compensate, the description should provide more context about the tool's behavior and output, but it falls short.
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 description doesn't add any parameter details beyond what the input schema provides. With 100% schema description coverage, the schema fully documents the single parameter 'template' with its enum values. The description doesn't explain what a 'component template' is or how the parameter relates to it, so it meets the baseline of 3 without adding extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as 'Get a component template for reference', which includes a verb ('Get') and resource ('component template'), making it clear what it does. However, it doesn't differentiate from siblings like 'get_quick_reference' or 'get_specification', leaving ambiguity about when to use this specific tool versus others that also retrieve reference materials.
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 provides no guidance on when to use this tool versus alternatives. With siblings like 'get_quick_reference' and 'get_specification', there's no indication of whether this is for templates only, what context it's suited for, or any prerequisites. It relies entirely on the agent to infer usage from the name and description.
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 full burden of behavioral disclosure. It mentions a 'quick check' and a compliance threshold ('score >= 80'), but lacks details on what 'compliant' means, how the score is calculated, error handling, or response format. This is inadequate for a tool with no annotation coverage, leaving key behaviors unspecified.
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 a single, efficient sentence: 'Quick check if component code is compliant (score >= 80)'. It is front-loaded with the core purpose and includes essential context (the threshold). There is no wasted verbiage, making it highly concise and well-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 no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., scoring method, error cases), output format, and usage context relative to siblings. For a compliance-checking tool with one parameter, this minimal description leaves too many gaps for effective agent use.
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 input schema has 100% description coverage, with one parameter 'code' documented as 'The component source code to check'. The description adds no additional parameter semantics beyond this, such as code format or length constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles the heavy lifting.
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's purpose: 'Quick check if component code is compliant (score >= 80)'. It specifies the verb ('check'), resource ('component code'), and success criterion ('score >= 80'). However, it doesn't explicitly differentiate from sibling tools like 'grade_component', which might perform a similar function with different scoring or output.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'grade_component' or 'get_rule', nor does it specify prerequisites, such as needing code input or when a quick check is appropriate versus a detailed analysis. Usage is implied but not explicitly stated.
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 full burden of behavioral disclosure. It states the tool generates a component following rules, but doesn't explain what 'follows all rules' entails, whether it creates files or modifies existing ones, what permissions are needed, or what the output looks like. For a creation tool with zero annotation coverage, this is a significant gap in transparency.
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, efficient sentence that front-loads the core purpose ('generate a new component') and adds a brief instruction. There's no wasted verbiage, but it could be slightly more structured by separating the rule-following aspect from the template choice for clarity.
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 complexity of a 6-parameter creation tool with no annotations and no output schema, the description is insufficient. It lacks details on behavioral traits, output format, error handling, or how 'follows all rules' is enforced. The high schema coverage helps, but for a generative tool, more context is needed to understand its full operation and implications.
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%, so the schema already documents all 6 parameters thoroughly. The description adds minimal value beyond the schema, only implying that parameters relate to template selection or customization. It doesn't provide additional context like parameter interactions or examples, but the high schema coverage justifies a baseline score of 3.
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 action ('generate a new component') and the resource ('component'), specifying it must follow all rules. It distinguishes from siblings like 'grade_component' or 'get_template' by focusing on creation rather than evaluation or retrieval. However, it doesn't explicitly differentiate from all siblings, such as 'check_compliance' which might involve rule-following.
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 provides minimal guidance: it mentions choosing a template type or customizing, but offers no explicit context on when to use this tool versus alternatives like 'get_template' or 'grade_component'. There's no mention of prerequisites, dependencies, or scenarios where this tool is preferred over others, leaving usage unclear.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states a read operation ('Get details'), which implies it's likely safe and non-destructive, but doesn't address potential errors (e.g., invalid rule IDs), authentication needs, rate limits, or return format. This leaves significant gaps for a tool with no annotation support.
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 a single, clear sentence with no wasted words. It's front-loaded with the core purpose and efficiently conveys the essential information without unnecessary elaboration.
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?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'details' are returned (e.g., rule metadata, compliance criteria), error handling, or how it differs from sibling tools. This leaves the agent with incomplete context for effective use.
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 schema description coverage is 100%, with the single parameter 'ruleId' well-documented in the schema. The description adds no additional meaning beyond what the schema provides, such as example usage or edge cases. Given high schema coverage, the baseline score of 3 is appropriate.
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 action ('Get details') and resource ('specific rule by ID'), making the purpose understandable. However, it doesn't distinguish this from sibling tools like 'get_rules' or 'list_rules', which likely retrieve multiple rules rather than a single one by ID.
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 provides no guidance on when to use this tool versus alternatives such as 'get_rules' or 'list_rules'. It mentions 'by ID' but doesn't clarify prerequisites or contexts where this is preferred over other retrieval methods.
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 full burden of behavioral disclosure. It mentions 'Get' which implies a read operation, but doesn't specify if it's safe, requires authentication, has rate limits, or what the output format might be. This leaves significant gaps in understanding how the tool behaves.
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 a single, clear sentence that efficiently conveys the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the cheat sheet contains, how it's formatted, or any behavioral traits like safety or performance. For a tool with no structured data to rely on, more context is needed to fully understand its use.
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 tool has zero parameters, and the schema description coverage is 100%, so there's no need for the description to add parameter details. The baseline score for zero parameters is 4, as the description doesn't need to compensate for any missing schema information.
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 action ('Get') and resource ('quick reference cheat sheet for component development'), making the purpose understandable. However, it doesn't differentiate this tool from sibling tools like 'get_specification' or 'get_template', which might also provide reference materials, so it doesn't reach the highest score.
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 provides no guidance on when to use this tool versus alternatives like 'get_specification' or 'get_template', nor does it mention any prerequisites or context for usage. It only states what the tool does, without indicating appropriate scenarios.
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. It discloses the return values (score, grade, violations, suggestions) but lacks details on behavioral traits such as performance implications, error handling, or whether grading is idempotent. For a tool with no annotations, this leaves significant gaps in understanding how it behaves beyond basic output.
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 a single, efficient sentence that front-loads the core action and output, with zero wasted words. It directly communicates the tool's purpose and results without unnecessary elaboration, making it easy to parse quickly.
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 annotations, no output schema, and 2 parameters with full schema coverage, the description is minimally adequate. It covers the basic purpose and output but lacks depth on usage context, behavioral details, or integration with sibling tools. For a grading tool with potential complexity, more completeness would be beneficial.
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%, so the schema already documents both parameters ('code' and 'verbose'). The description adds no additional meaning beyond what the schema provides, such as explaining the grading process or how 'verbose' affects output. Baseline 3 is appropriate when the schema handles parameter documentation adequately.
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 action ('Grade') and resource ('a component against all rules'), specifying what the tool does. It distinguishes from siblings like 'check_compliance' or 'get_rule' by focusing on grading with comprehensive rule evaluation. However, it doesn't explicitly differentiate from 'check_compliance' which might have overlapping functionality.
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 provides no guidance on when to use this tool versus alternatives like 'check_compliance' or 'list_rules'. It mentions grading against 'all rules' but doesn't specify prerequisites, context, or exclusions, leaving the agent to infer usage scenarios without explicit direction.
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 full burden of behavioral disclosure. It states the tool 'Returns matching sections and context,' which hints at output behavior, but lacks details on permissions, rate limits, error handling, or whether it's read-only. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its operational traits.
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 highly concise and front-loaded, consisting of two clear sentences: one stating the purpose and one stating the return behavior. Every word earns its place, with no redundant or vague language, making it easy for an agent to parse quickly.
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 the tool's low complexity (single parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and return hint, but lacks output details (e.g., format of 'matching sections and context'), usage context, or behavioral constraints, which could hinder an agent's ability to use it effectively without trial and error.
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%, with the input schema fully documenting the single 'query' parameter. The description adds minimal value beyond the schema by implying the query is for 'a specific term or concept,' but doesn't provide additional syntax, format, or usage examples. This meets the baseline score of 3 when schema coverage is high.
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's purpose: 'Search the components.build specification for a specific term or concept.' It specifies the verb ('search'), resource ('components.build specification'), and action ('for a specific term or concept'). However, it doesn't explicitly differentiate from sibling tools like 'get_specification' or 'list_specification_sections', which might also retrieve specification content.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_specification' (which might fetch the entire specification) or 'list_specification_sections' (which might list sections without searching), leaving the agent to infer usage based on the 'search' keyword alone.
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. It states this is a 'Get' operation (implying read-only) and mentions filtering/output format, but doesn't disclose behavioral aspects like whether it returns all rules by default, pagination behavior, error conditions, or authentication requirements. The description provides basic intent but lacks 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise (two sentences) and front-loaded with the core purpose. Every sentence adds value: first states the action and goal, second adds filtering capability. No wasted words, though it could be slightly more structured.
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 read operation with 2 optional parameters and 100% schema coverage but no output schema, the description provides adequate context about what the tool does and filtering options. However, it doesn't describe the return format or structure (beyond the format parameter), which would be helpful since there's no output schema. The description is minimally complete but could better address output expectations.
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%, so the schema already fully documents both parameters with descriptions and enums. The description mentions filtering by category and implies format selection ('Can filter by category'), but adds no additional semantic context beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.
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's purpose: 'Get component rules documentation' (verb+resource). It specifies the scope ('understand what rules exist and how to follow them') and mentions filtering capability, but doesn't explicitly distinguish it from siblings like 'get_rule' or 'list_rules'.
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 usage context ('understand what rules exist and how to follow them') and mentions filtering, but doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_rule' (singular) or 'list_rules'. No when-not-to-use or prerequisite information is included.
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 full burden. It mentions retrieving 'FULL' documentation and that it's 'complete', which hints at comprehensiveness, but doesn't disclose behavioral traits such as response format, pagination, error handling, or performance considerations. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.
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 concise and front-loaded, with two sentences that efficiently convey the tool's purpose and intended use. Every sentence adds value, though it could be slightly more structured by explicitly separating purpose from usage context.
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 the tool has no annotations, no output schema, and a simple input schema, the description is adequate but incomplete. It covers the purpose and high-level context but lacks details on behavior, output format, or error handling, which are important for a documentation retrieval tool. It's minimally viable but has clear gaps.
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 input schema has 100% description coverage, with the 'section' parameter well-documented including optional status and available values. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without compensating with additional semantics.
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's purpose: 'Get the FULL components.build specification documentation' and identifies it as 'the complete guide to building modern UI components'. It specifies the resource (specification documentation) and verb (get), though it doesn't explicitly differentiate from sibling tools like 'get_quick_reference' or 'list_specification_sections'.
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 usage context by stating this is for 'understanding the complete philosophy, patterns, and best practices', suggesting it's comprehensive. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_quick_reference' or 'list_specification_sections', nor does it provide any exclusions or prerequisites.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists rules with specific attributes, but lacks details on permissions required, pagination behavior, rate limits, or whether the operation is safe (read-only). For a list operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic function.
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 a single, efficient sentence that front-loads the core action ('List all available rules') and specifies the returned attributes. There is no wasted verbiage, repetition, or unnecessary elaboration, making it highly concise and well-structured for quick understanding.
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 the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate for a basic list operation. It covers the purpose and output attributes, but lacks behavioral context like safety or performance traits. Without annotations or output schema, the description does the minimum viable job but could be more complete by addressing missing behavioral aspects.
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 0 parameters with 100% coverage, meaning there are no parameters to document. The description correctly implies no filtering parameters by stating 'all available rules', adding value by clarifying the tool's scope. This aligns with the schema's empty properties, so the description compensates appropriately for the lack of parameters.
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 verb ('List') and resource ('all available rules') along with specific attributes returned (IDs, names, categories). It distinguishes from siblings like 'get_rule' (singular) and 'get_rules' (potentially filtered) by emphasizing 'all available' without filtering parameters. However, it doesn't explicitly differentiate from 'search_specification' which might overlap in scope.
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?
No guidance is provided on when to use this tool versus alternatives like 'get_rules' or 'search_specification'. The description implies a comprehensive listing, but there's no explicit mention of prerequisites, when-not-to-use scenarios, or comparison to sibling tools that might offer filtered or specific rule retrieval.
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 full burden of behavioral disclosure. It states the tool lists sections but doesn't describe how it behaves—e.g., whether it returns a simple list, includes metadata, has pagination, or requires authentication. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 the tool's simplicity (0 parameters, no output schema), the description is minimally adequate but lacks completeness. It doesn't cover behavioral aspects like return format or error handling, which are important even for simple tools. With no annotations and no output schema, more context would be helpful for reliable use.
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 tool has 0 parameters, and schema description coverage is 100% (though empty). With no parameters to document, the description doesn't need to add parameter semantics beyond what the schema provides. A baseline of 4 is appropriate as the description doesn't mislead about parameters.
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 action ('List all available sections') and the target resource ('in the components.build specification'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_specification' or 'search_specification' that might also relate to specification content, which prevents a perfect score.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or exclusions, nor does it reference sibling tools like 'get_specification' that might overlap in functionality. This leaves the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![components-build-mcp MCP server](https://glama.ai/mcp/servers/getlokiui/components-build-mcp/badges/card.svg)](https://glama.ai/mcp/servers/getlokiui/components-build-mcp)

Score Badge

Copy to your README.md:

[![components-build-mcp MCP server](https://glama.ai/mcp/servers/getlokiui/components-build-mcp/badges/score.svg)](https://glama.ai/mcp/servers/getlokiui/components-build-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/getlokiui/components-build-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server