devuniverse-mcp

by joeshwoa

Overview Schema Related Servers Score Discussions

TypeScript

Local

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation4/5
Tools are mostly distinct, but audit_compliance and generate_compliance_docs both relate to compliance, which could cause confusion. Similarly, verify_assets and watch_assets are related, though the latter extends the former.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case, e.g., audit_compliance, generate_media_asset, provision_omnicinema. No mixing of conventions.
Tool Count5/5
With 13 tools covering project init, compliance, asset generation, media integration, Figma sync, and more, the count is well-scoped for the domain of a development universe assistant.
Completeness4/5
The tool surface covers core workflows (init, compliance, asset generation, verification), but lacks tools for updating or deleting projects and generated artifacts, which are minor gaps.
Average 3.9/5 across 13 of 13 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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.
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses file saving, provenance manifest creation, and theme re-adaptation. However, with no annotations, it does not cover error handling, overwrite behavior, or side effects for non-image types.
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 but somewhat convoluted with 'CASE A' and lacks clear separation of steps. It is moderately concise but could be better 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?
For a tool with 5 parameters and no output schema, the description is incomplete. It omits details like file name auto-generation, project directory purpose, and whether the tool is idempotent, leaving significant gaps for an AI 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 coverage is low (40%), and the description adds meaning to 'kind' and 'adapt' (e.g., adapt only for images). However, 'file_name' and 'project_dir' remain unexplained, and the description does not fully compensate for the gaps.
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 requests media assets (image/texture/audio/video) from an API, saves them to a specific directory with provenance, and re-adapts the theme. However, the 'CASE A' prefix is ambiguous and suggests incomplete context.
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 on when to use this tool versus alternatives like generate_spatial_module or omnicinema_status. The description lacks context for appropriate usage scenarios 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?
With no annotations provided, the description must fully disclose behavioral traits. It mentions optional mesh renderers and Rive HUD, but lacks details on file overwriting, required permissions, error handling, or side effects. The description gives a sense of what is generated but not the behavior during generation.
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 of about 45 words, listing many features. While informative, it could be more concise or structured (e.g., bullet points) for easier scanning. The information density is high but the presentation is dense.
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 5 parameters, no output schema, and no annotations, the description should provide more comprehensive coverage. It does not explain the depth_layers parameter, the return value or success indicators, nor does it address prerequisites (e.g., Flutter setup). The description leaves significant gaps for an agent to invoke the tool 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 description coverage is 60% (3 of 5 parameters have descriptions). The tool description adds meaning for mesh_renderer and rive by referring to them as optional, and for project_dir by distinguishing existing project vs. standalone module. However, depth_layers and project_name are not explained beyond their schema defaults, missing an opportunity to clarify their role in the generated output.
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 function: generating an immersive 3D deep-scroll Flutter module. It lists specific features (scroll-driven Z-axis camera, Matrix4 parallax, CustomPainter starfield, etc.) and distinguishes the tool from siblings like audit_compliance or generate_compliance_docs by focusing on code generation for spatial canvas.
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 notes that the tool can target an existing project or a fresh module directory, providing some usage context. However, it does not explicitly state when not to use the tool or mention alternatives. Given the sibling tools are quite different, the guidance is clear enough for typical use.
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?
With no annotations, the description thoroughly discloses behavioral traits: parsing, debating, voting, generating scaffold, cross-examining, compiling wireframes, running compliance audit, and starting asset watcher. It also states the output location (projects/<slug>/) and that a meeting transcript is stored. However, it does not mention potential side effects or persistence behavior.
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 long sentence (over 100 words) summarizing many actions. While it front-loads the core purpose, it is not concise and could benefit from structured bullets or clearer separation of key points. Every sentence earns its place but readability suffers.
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?
No output schema is provided and the description does not explain the return value of the tool (e.g., JSON response, slug, status). It only mentions that artifacts land on disk. Given the tool's high complexity and multiple generated outputs, this omission leaves the agent without essential information about what the tool returns.
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 baseline is 3. The description adds context beyond the schema by explaining that keywords in 'description' steer the debate and that 'project_type' values like 'flutter_spatial_app' enable specific features. However, it does not systematically enhance each parameter's meaning.
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 uses the specific verb 'Convene the 27-senior virtual agency for a new project' and clearly states the tool initializes a DevUniverse project. It distinguishes itself from sibling tools like audit_compliance or generate_spatial_module by describing an all-encompassing initialization process.
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 starting new projects but does not explicitly state when to use this tool versus alternatives. There is no guidance on prerequisites, exclusions, or comparison with sibling tools like get_agency_roster or sync_figma. The purpose is implied but not formally clarified.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that documents are draft-level with a 'review-by-counsel banner' and explicitly states 'structured drafts, not legal advice,' which clarifies limitations. This adds valuable behavioral context beyond a simple 'generates docs'.
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, front-loaded with clear outputs and a critical disclaimer. No unnecessary words. Every sentence adds value.
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?
The tool generates complex legal documents but has no output schema and missing parameter descriptions. The description does not explain the response format, success criteria, or how inputs map to outputs. For a tool with no output schema, more detail is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 25% (only 'governing_law' has a description). The description does not explain key parameters like 'project_dir', 'company_name', or 'contact_email'. It vaguely says 'from the project's actual data categories' but does not connect to parameters.
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 generates a Terms of Service, Privacy Policy (GDPR/CCPA), and a compliance checklist from project data categories. The verb 'generate' and specific outputs distinguish it clearly from sibling tools like 'audit_compliance' or 'verify_assets'.
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 used when legal documents are needed based on project data, but it does not explicitly state when to use it or when to use alternatives like 'audit_compliance'. 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses a write operation (compliance/AUDIT_REPORT.md) and the nature of findings (severity-ranked, with remediations). It also states it's a 'static scan,' implying no network or destructive 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 sentences with no wasted words. The first sentence lists what is scanned, the second states the output. Information is front-loaded and each sentence earns its place.
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 tool with one parameter and no output schema, the description explains the scan scope and output file. Missing details about prerequisites, report overwrite behavior, or network requirements, but overall adequate for the simple tool.
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?
Only one parameter (project_dir) with 0% schema description coverage. The description mentions 'project' but does not explicitly define the parameter format or semantics (e.g., absolute/relative path). It adds some implicit meaning but not enough for full clarity.
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 performs a static scan for security liabilities and writes a report. It lists specific issues checked (committed credentials, plaintext endpoints, etc.), differentiating it from sibling tools like generate_compliance_docs or scan_extension_marketplace.
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 versus alternatives. Among siblings, generate_compliance_docs might be related but no distinction provided. The description implies it's for scanning a project, but lacks exclusions or contextual usage advice.
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 carries the full burden. It discloses that the REST API cannot create designs and that the plugin is needed for editable drafts. However, it omits important behavioral details such as auth requirements (FIGMA_TOKEN is mentioned but not clarified as required), side effects (e.g., does it modify the Figma file?), and output location (local path or URL?).
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, efficiently front-loaded with the purpose and then detailing outputs and constraints. Every sentence adds value, with no redundancy or fluff.
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 lack of an output schema, the description should explain return values. It names the artifacts but does not specify the tool's response format (e.g., paths, URLs). It also lacks details on error handling or token requirements for handoff. Despite these gaps, it covers the core outputs and constraints reasonably.
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 low (33%), so the description must compensate. It adds meaning by linking mode enum values to output types (wireframes, plugin, handoff) and mentioning the token context. But it does not describe project_dir or provide constraints for figma_file_key beyond the schema's description. The compensation is partial.
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 compiles a screen plan into three specific design artifacts: SVG wireframes, a Figma plugin, and an optional REST handoff comment. The verb 'compile' and resource 'design artifacts' are specific, and the outputs are distinct from sibling tools, which focus on compliance, media, and spatial generation.
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 provides guidance on when to use each mode (wireframes, plugin, handoff) and explicitly notes that the REST API cannot create designs, so the plugin is the editable-draft path. It also implies that figma_file_key is needed for handoff mode. However, it does not explicitly state when to use this tool versus alternatives, though no close siblings exist.
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 responsibility. It discloses key behaviors: it writes a report at 'docs/ADAPTATION_REPORT.md' and includes WCAG contrast verification. However, it does not disclose whether the tool modifies project files beyond the report, what permissions are needed, or any side effects of re-deriving design tokens. The transparency is adequate but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the main action ('Analyze art... and re-derive the design tokens') and then detailing the output. Every sentence adds meaningful information without repetition or fluff. It is appropriately sized for a moderate-complexity tool.
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, the description should explain what the tool returns or how the agent should use the results. It mentions writing a report but does not clarify if the report content is returned or if the tool just signals completion. It also assumes knowledge of 'asset drop zones' without explanation. While the description covers the main actions, it lacks completeness for an agent to fully understand the tool's integration into a workflow.
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 one parameter with 100% coverage, providing a clear description: 'A generated devuniverse project directory.' The description does not add new information about the parameter itself; it only contextualizes it as 'project's asset drop zones.' With full schema coverage, the baseline is 3, and the description adds minimal extra semantic value.
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: analyzing art assets in drop zones (format, dimensions, dominant palette) and re-deriving design tokens (colors, brightness, padding grid, radii) to adapt the theme. It also mentions writing a report. The verb 'analyze' and 're-derive' specify the action and resource. This distinguishes it from siblings like 'watch_assets' which likely only monitors changes.
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 the tool should be used after assets are uploaded to 'asset drop zones' to adapt the theme. It provides context for when to use it but does not explicitly state when not to use it or mention alternatives. With sibling tools, an agent might need guidance on when to choose this over 'audit_compliance' or 'sync_figma', but the description is clear enough for the primary use case.
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 exist, so the description alone must disclose behavior. It fully describes what is returned (names, titles, focus areas, operating principles). It implies a read-only list operation with no side effects. Could mention if authentication is needed or if the roster is static, but the disclosure is good for a simple tool.
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?
Single sentence that is concise, front-loaded, and descriptive without any 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?
For a simple list tool with no parameters and no output schema, the description adequately explains the content. It could mention if it returns the current or all personas, but overall it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters with 100% coverage, so the baseline is 4. No additional parameter information is needed; the description adds value by explaining what the output contains.
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 lists the 27 senior personas with details like names, titles, focus areas, and operating principles. It uses a specific verb (list, implied) and resource (senior bench). No sibling tool has a similar function, so no need for differentiation.
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 versus alternatives. The sibling tools include various other functions, but the description does not provide context on when to fetch the roster vs. other operations.
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 the description carries the full burden. It discloses the output location, pending human review status, risk flags, and that nothing is auto-installed. Could add details on rate limits or error handling.
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, front-loaded with core action and outcome. No redundant words; every sentence adds value.
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?
Covers purpose, behavior, and outcome but omits parameter details and potential errors. With no output schema, the description could better explain return format. Adequate for a simple tool but with gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description mentions the two sources (GitHub, Hugging Face) but does not explain the 'limit' parameter or provide any additional meaning beyond the schema. The parameter semantics are weak.
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 (crawl and log), the target sources (GitHub, Hugging Face), the output (MCP servers/dev packages to a specific file with risk flags), and distinguishes from sibling tools like audit_compliance.
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?
States that nothing is auto-installed and human decisions survive re-scans, implying a review workflow. Lacks explicit when-not-to-use or alternatives, but the context is clear enough for a single-purpose scanning tool.
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?
With no annotations, the description carries the full burden. It discloses that the tool only analyzes patterns and never copies protected artwork/trade dress, and mentions ffmpeg keyframe analysis when present. However, it does not describe side effects like file modifications or output format.
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 only three sentences, front-loaded with the core purpose. Every sentence adds value, including the caveat about protected works and the apply_theme behavior. No wasted words.
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?
The tool has 4 parameters and no output schema. The description explains the core function and some param semantics, but does not describe what the tool returns (e.g., a plan text, updated files) or error conditions. Given the lack of annotations, more detail on output would improve completeness.
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 50% (url and video_path have descriptions). The description adds meaning beyond schema: it explains that apply_theme blends the extracted palette into project tokens and clarifies the purpose of url and video_path as input references. project_dir lacks explanation but is required.
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 uses specific verbs ('reverse-engineer the patterns') and identifies concrete resources (palette, typography, framework/interaction fingerprint). It clearly distinguishes from siblings by focusing on pattern analysis, not compliance, media generation, or other tasks listed in sibling tools.
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 when a user provides a reference URL or video for studying patterns. It mentions ffmpeg analysis for videos and the apply_theme option, but does not explicitly state when to avoid this tool or suggest alternative tools from the sibling list.
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?
Without annotations, the description fully carries the burden. It transparently states the tool performs detection, health checking, and state reporting, which are read-only operations. It does not disclose potential side effects, but the operations described are inherently non-destructive.
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 sentence with no wasted words. It is front-loaded with the main action and provides all necessary context efficiently.
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 no parameters, no output schema, and is a straightforward status check, the description adequately covers the purpose, inputs, and outputs. It could be improved by specifying the output format, but it mentions 'CASE A/B/C state' which is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With no parameters (schema coverage 100%), the description adds meaning by detailing what is checked (well-known SSD paths, overrides, IPC health) and the output (CASE A/B/C state), which is not present in 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 uses specific verbs ('Detect', 'check', 'report') and clearly identifies the resource ('OmniCinema media generator'). It distinguishes itself from sibling tools like provision_omnicinema and generate_media_asset by focusing on detection and health checking.
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 checking status, but provides no explicit guidance on when to use versus alternatives (e.g., before provisioning) or when not to use. 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Describes toggling and automatic re-run behavior, but lacks details on side effects, permissions, or process implications.
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, front-loaded with main action, no wasted words. Efficiently conveys core 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?
For a simple toggle tool with no output schema and no annotations, description covers main purpose, parameter behavior, and automatic effects. Missing return value or error conditions, but adequately complete given low 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?
Schema has 0% description coverage, but description adds meaning: explains that removing project_dir changes behavior to listing. Adds value beyond schema structure.
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 starts/stops a watcher on a specific path, and lists active watchers when no project_dir. This distinguishes it from siblings like verify_assets or generate_media_asset.
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 usage context: start/stop watcher, and listing watchers without project_dir. However, no explicit when-not-to-use or alternatives among siblings.
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?
The description details all major actions (clone, install, build, start service, sync token) and notes that it refuses unpinned URLs. It does not cover prerequisites or potential side effects (e.g., overwriting), but given no annotations, it provides reasonable 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 sentence that efficiently conveys the trigger condition, actions, and constraint. It is slightly lengthy but each clause adds necessary detail. The structure is logical.
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 simple single-parameter schema and no output schema, the description covers the main behavior well. Missing details like prerequisites or error handling are acceptable for a tool with clear consent and a specific action.
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 a clear description for 'confirm'. The tool description adds value by explaining that confirm must be true to trigger the entire process, reinforcing the schema's requirement.
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 auto-provisions OmniCinema by cloning a pinned repo, installing dependencies, building, starting an IPC service, and syncing a bearer token. It distinguishes itself from sibling tools like 'omnicinema_status' by focusing on provisioning rather than querying status.
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 when one wants to set up OmniCinema with free assets, requiring explicit consent via confirm=true. It does not explicitly state when not to use it or compare to alternatives, but the context is clear given sibling tools.
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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