aceternity-mcp

by devinoldenburg

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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Latest release: v2.4.1
Disambiguation3/5
Several tools serve similar recommendation purposes (recommend_components, recommend_combination, match_components_to_project, generate_page_layout), which could confuse agents. However, descriptions clarify their distinct scopes (single recommendation, per-section, whole project, full layout). Other tools like filter_by_scores, search_components, and list_components are more distinct.
Naming Consistency5/5
All tools consistently use verb_noun naming with underscores (e.g., filter_by_scores, list_components, recommend_components). The pattern is predictable, even for longer names like match_components_to_project.
Tool Count5/5
12 tools is well-scoped for a UI component library server. It covers browsing, searching, filtering, recommending, installation, and monitoring without being excessive or too sparse.
Completeness5/5
The tool surface covers the full lifecycle of component discovery: listing, filtering, searching, retrieving details, installation instructions, and cache stats. No obvious gaps for the stated purpose of helping users find and use Aceternity components.
Average 4/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 11 commits in the last 12 weeks
- Last stable release on April 4, 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.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It implies a read-only operation by stating 'Get...entry,' but does not explicitly confirm non-destructiveness, idempotency, or any side effects. Adequate for a simple retrieval, but lacks depth.
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 concise, with the core purpose in the first sentence, followed by a bullet-like list of return contents. The Args line is efficient. No redundant or unnecessary text.
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's simplicity (one required parameter) and the presence of an output schema, the description adequately covers the input semantics and return scope. It could mention error conditions but is still sufficient for correct usage.
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 description adds concrete examples for the slug parameter (e.g., 'spotlight'), which is not present in the schema. With 0% schema description coverage, this compensates well and clarifies usage beyond the raw type definition.
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 retrieves full details for a single component using a slug. It distinguishes from sibling tools that list or search components, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance on when to use this tool vs. alternatives like list_components or search_components. It implies use for a single component but lacks explicit context or exclusions.
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 the analysis process but does not disclose behavioral traits such as idempotency, resource consumption, or error handling. The description lacks transparency on behavior beyond the basic operation.
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 well-structured: purpose, mechanism, examples, then parameter docs. It front-loads the key information. Minor redundancy in explaining 'analyses' could be trimmed, but overall efficient.
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 presence of an output schema, the description handles inputs well but omits contextual details like edge cases, no-match handling, or differentiation from similar tools. It is adequate but not fully complete for complex usage.
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?
With 0% schema coverage, the description fully documents all three parameters: description with illustrative examples, max_results with default and max, and include_pro with its toggle meaning. This adds substantial value 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 it recommends components for a use case or design goal by analyzing descriptions. It differentiates from simple search tools but not explicitly from similar tools like 'match_components_to_project'. Thus, it's clear but lacks full sibling differentiation.
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 provides examples that imply usage for natural language queries, but it does not explicitly state when to use this tool versus alternatives like 'search_components' or 'filter_by_scores'. Usage guidance is implicit and not robust.
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 must disclose behavioral traits. It states the tool lists data but does not mention that it is read-only, safe, or any side effects. The agent cannot infer safety from the description alone.
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, concise sentence that front-loads the action ('List all...'). Every word adds value, with no redundancy or filler.
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?
The description covers the key output fields (descriptions and component counts). Since an output schema exists (has_output_schema: true), detailed return format is unnecessary. The description is complete for this simple, parameterless 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?
The tool has zero parameters, so the schema coverage is 100% by default. According to guidelines, 0 parameters warrant a baseline score of 4. The description adds no parameter details, but none are needed.
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 lists all component categories and includes descriptions and component counts. It distinguishes itself from siblings like 'get_category' (single category) and 'list_components' (different resource).
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 filter_by_scores or generate_page_layout. There are no explicit usage contexts or 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?
Annotations are absent, so description carries transparency burden. It explains return structure (category info + component summaries) but does not mention side effects, permissions, or rate limits.
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 sentences with no fluff; the main purpose is front-loaded. Efficient and clear.
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?
Output schema exists, so return values are covered. Description adds sufficient context about output contents. Could mention error conditions (e.g., invalid slug) but fine overall.
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 has 0% description coverage for the 'slug' parameter, but the description adds examples and context (e.g., 'hero-sections', 'backgrounds'), significantly aiding the agent.
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 retrieves all components in a category, with specific return information (category info + full summaries). It differentiates from siblings like 'list_categories' and 'get_component'.
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 explicit guidance on when to use this tool over alternatives like 'search_components' or 'filter_by_scores'. The description provides example slugs but lacks context for decision-making.
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 provided. The description discloses the return format but does not mention pagination, rate limits, or side effects. It correctly implies a read-only operation but lacks detailed behavioral context.
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 with five lines, front-loading the main purpose and then detailing filtering and return format. Each sentence adds value, though the Args section could be integrated more smoothly.
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 annotations and a low-complexity tool, the description covers purpose, filtering, and return format. It lacks error handling or behavior when no results, but output schema exists. Adequate for the task.
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 description coverage is 0%, so the description carries the burden. It explains the 'category' parameter with an example slug and notes that 'include_pro' filters pro components. This adds meaning beyond the schema's type and default.
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 'List all available Aceternity UI components' with optional filtering by category slug. It specifies the return format (compact summary with slug, name, category, tags, summary). This distinguishes it from sibling tools like search_components and filter_by_scores.
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 for listing components with optional category filter. However, it does not explicitly state when to use this tool over siblings (e.g., search_components for text search, filter_by_scores for scoring). No exclusions or alternatives are provided.
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?
With no annotations, the description must cover behavioral traits. It explains the AND filtering logic and scoring dimensions, but does not disclose whether the tool is read-only, what the return format is, or any side effects. The output schema exists but is not referenced, 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 well-structured with a clear lead sentence, bullet points for dimensions, and an args list. It is informative but somewhat lengthy; a slightly more compact version could be equally clear.
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 complexity of 9 parameters and scoring thresholds, the description covers the core functionality, parameter meanings, and logical AND combination. It omits details about the output schema and any limitations, but is largely complete for a filtering tool.
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?
Schema description coverage is 0%, so the description fully compensates. It lists all 9 parameters, explains each scoring dimension with a scale (1-10), and notes the default for include_pro. This adds complete meaning beyond the schema titles.
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 filters components by numeric scoring thresholds on a 1-10 scale. The verb 'filter' and resource 'components' are specific, and the distinct focus on scoring thresholds differentiates it from sibling tools like search_components or recommend_components.
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 notes that all parameters are optional and only components passing every threshold are returned, implying usage for narrowing down by scores. However, it does not explicitly guide when to use this tool versus alternatives like search_components or recommend_components, nor does it mention excluded cases.
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 provided, so description carries full burden. It discloses returns (ordered layout, install commands, dependency summary) and auto-detection behavior, but does not mention side effects, auth needs, or rate limits. Adequate but not comprehensive.
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?
Description is well-structured with a summary, supported types list, examples, and parameter details. Some redundancy in examples could be trimmed, but overall efficient and front-loaded.
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's complexity and output schema presence, the description covers inputs, auto-detection, and return value. Minor gap: no mention of error handling for vague descriptions. Near complete.
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?
Schema has 0% description coverage, but the description adds full meaning to all four parameters: what they are, defaults, constraints (e.g., max for components_per_section), and auto-detection behavior for page_type. Fully compensates for schema gap.
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 generates a complete page layout with component recommendations, listing supported page types and giving examples. It is distinct from sibling tools which focus on individual components or cache stats.
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 when to use (to generate a layout), but does not explicitly mention when not to use or suggest alternatives (e.g., recommend_components for component-only tasks). Usage is inferred but not fully guided.
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 full burden. It lists the returned fields (install command, dependencies, etc.) but does not disclose potential side effects, access permissions, rate limits, or any additional behavioral details beyond the return content.
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 concise: a two-sentence main description followed by a clear Args listing. The first sentence front-loads the purpose. No unnecessary words or repetition.
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 only one required parameter and an output schema exists (not shown but indicated), the description sufficiently covers what the tool does, what it returns, and how to use it. The examples for slug provide additional clarity.
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 0% description coverage (no description in JSON Schema for the slug parameter). However, the description's Args section adds meaning with examples (e.g., 'spotlight', 'sidebar', 'bento-grid') and explains that it is a component slug, which compensates for the schema gap.
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 'Get full installation instructions for a component.' It identifies a specific resource (installation instructions) and a clear action (get). This distinguishes it from siblings like 'get_component' (likely returns component details) and 'list_components'.
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 for retrieving installation instructions but does not provide explicit guidance on when to use this tool versus alternatives, nor does it state when not to use it. There is no mention of exclusions or context where another sibling tool would be more appropriate.
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 description carries full burden. It discloses that the tool returns more results and is biased, but does not explicitly state whether it is read-only or has side effects. For a matching tool, safety profile (e.g., no modification) is implied but not stated, leaving some ambiguity.
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?
Description is very concise: two sentences for purpose/context, then clean bullet-style args list. No wasted words, front-loaded with key differentiation.
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 that output schema exists (but not shown here), description does not need to explain return structure. It covers purpose, bias, and parameters well. Could be improved by noting that the tool is read-only or clarifying 'layout-critical,' but overall adequate.
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?
Schema coverage is 0% (no descriptions in schema), so description must add meaning. It does so clearly: gives example for project_description, specifies default and max for max_results, and default for include_pro. These details go beyond the schema's type/default information.
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 matches components to a project description. It explicitly distinguishes from the sibling 'recommend_components' by noting bias toward layout-critical components and returning more results for full project planning. Verb 'match' and resource are specific.
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?
Description provides clear context: use for full project planning with bias toward layout-critical components. It contrasts with recommend_components via 'similar to' but does not explicitly state when NOT to use. However, the context is sufficient for an agent to decide.
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 provided, so description carries full burden. It discloses ranking by relevance and default settings, but lacks details on case-sensitivity, matching behavior, or boundary conditions.
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 concise and well-structured: summary sentence, then detailed paragraph, then parameter list. Every sentence adds value 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?
Given an output schema exists, the description adequately covers search scope and fields. It lacks mention of pagination or whether search is across all components or user's, but is otherwise complete.
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?
Schema coverage is 0%, so description compensates fully. It explains each parameter with examples, defaults, and constraints (e.g., max_results max 100), adding significant meaning 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 it searches components by text query with optional filters, and lists the fields searched. It distinguishes from sibling tools like list_components (which lists all) and get_component (specific one).
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 implies usage for textual search with filters but does not explicitly state when not to use or alternatives. However, the context is clear and parameter details guide usage.
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?
Even without annotations, the description transparently explains that the tool returns performance metrics and implies a read-only, non-destructive behavior. It covers the main behavioral aspect well.
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 concise with a clear summary line followed by a bulleted list of returned metrics. Every sentence serves a purpose without 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 that the tool has no parameters and an output schema exists, the description fully explains what the tool does and why it should be used. It is complete for the monitoring context.
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, so the schema coverage is 100%. The description adds value by detailing the output structure beyond the schema, which is appropriate for this case.
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 retrieves search cache statistics for monitoring and debugging, with a specific verb and resource. It lists the exact metrics returned, making the purpose unambiguous.
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 says to use it for monitoring cache effectiveness and tuning cache parameters. While it doesn't provide when-not or alternatives, the context is sufficiently clear for appropriate usage.
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. It describes behavior as returning recommendations, implying no side effects. Missing explicit statement about read-only nature, but sufficient.
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?
Concise at 6 sentences, front-loaded with purpose, includes structured args list using docstring format, no fluff.
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 output schema present, description covers inputs and high-level output. Mentions all page sections returned, sufficient for agent to invoke correctly.
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?
Schema has 0% description coverage, but the description fully explains both parameters: 'description' with an example and 'include_pro' with default value, adding significant meaning 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 it recommends a full-page component combination, listing specific sections (navigation, hero, etc.), distinguishing it from sibling tools like 'recommend_components' which likely recommend individual components.
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 clear use case: 'Given a project or page description'. Includes example input. However, it does not explicitly exclude cases or compare to 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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