ultralytics-mcp

Server Quality Checklist

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Latest release: v0.1.10
Disambiguation5/5
Each tool targets a distinct resource and action, with clear descriptions that prevent confusion. Even similar upload tools are differentiated by input type (file, folder, video), and dataset vs project vs model operations are clearly separate.
Naming Consistency4/5
Tools use a consistent verb_noun pattern in snake_case, but there is a minor inconsistency between singular and plural prefixes (e.g., 'dataset_ingest' vs 'datasets_list'). The overall pattern is predictable and readable.
Tool Count4/5
27 tools is above the typical range, but given the breadth of Ultralytics functionalities (dataset management, project management, model training, inference, exports, GPU monitoring), each tool serves a necessary purpose and the count is justified.
Completeness4/5
The tool surface covers core CRUD and lifecycle operations for datasets, projects, models, exports, and training. Minor gaps exist: no tool to directly create a model (only via training), no dataset update, and no model deletion, but agents can still accomplish primary workflows.
Average 3.4/5 across 27 of 27 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 68 commits in the last 12 weeks
- Last stable release on July 18, 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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read operation. The description adds no further behavioral context such as pagination behavior, rate limits, or what happens if the dataset is empty or missing. It merely restates the purpose.
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 extremely concise at one sentence, which is not overly verbose. However, it is under-specified given the complexity of the tool (8 parameters). The sentence could earn its place by providing more high-level context, but it currently lacks substance. A 3 reflects 'adequate but minimal'.
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 number of parameters (8) and the absence of an output schema, the description is insufficient. It does not explain return values, filtering behavior, or any constraints. The low schema coverage and missing output details make the tool feel incomplete for an agent to invoke confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 8 parameters and only 13% schema description coverage (only 'dataset' has a description), the burden falls heavily on the tool description. However, the description does not describe any parameter semantics, leaving parameters like limit, split, offset, search, classIds, hasLabel, and includeImageUrls entirely unexplained. This severely limits an agent's ability to use the tool correctly.
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 'List images in a dataset with optional filtering,' which is a specific verb-resource combination. It distinguishes from sibling tools like dataset_export or dataset_ingest, making its purpose clear. However, it does not explicitly differentiate from other list tools like datasets_list or explore_datasets, so not a perfect 5.
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. Sibling tools include many dataset-related operations (export, ingest, delete, list), but the description does not clarify when this specific list tool is appropriate. There is no mention of prerequisites 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?
Annotations provide no safety hints (readOnlyHint, destructiveHint, idempotentHint all false). The description does not disclose potential side effects, such as whether existing data in the dataset is replaced or appended, or any file format restrictions. No authorization requirements or rate limits are mentioned.
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, front-loaded sentence that wastes no words. Every part directly adds to understanding the tool's function. There is no redundancy or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (3 parameters, no output schema), the description lacks important context: file format expectations, whether the ingest is synchronous or asynchronous, error handling, and any return value or confirmation. It is underspecified for reliable agent use.
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 67% (2 of 3 parameters have descriptions). The tool description adds no additional meaning for the parameters; e.g., 'targetSplit' remains unexplained beyond its name and type. The description does not compensate for the missing parameter documentation.
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 (upload a local archive file) and the secondary action (start ingest), and specifies it's for an existing dataset. It implicitly distinguishes from siblings like upload_folder by mentioning 'archive file', but lacks explicit 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 guidance on when to use this tool versus alternatives like dataset_upload_folder or dataset_upload_video. No prerequisites, such as the dataset must exist, are stated explicitly. No exclusions or when-not-to-use instructions.
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 indicate readOnlyHint=false (mutation), which aligns with the description's 'Create'. The description explicitly mentions creating a 'frozen snapshot', implying immutability but not elaborating on effects. No contradictions, but lacks additional behavioral context like permission requirements or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, efficient but not structured. It conveys the core purpose without fluff, but could benefit from additional context or bullet points. The conciseness is adequate, but the lack of structure limits usefulness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no output schema and 2 parameters (1 required), the description is incomplete. It does not explain what 'frozen' means, the result of the operation, or how versions relate. More context is needed for an agent to use this tool correctly.
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 50%, but the description provides no parameter details. The 'dataset' parameter is described in the schema, but 'description' lacks explanation. The tool description does not add meaning beyond the schema, leaving ambiguity about the 'description' field's role.
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 creates a frozen dataset version snapshot, specifying the verb 'Create' and the resource 'frozen dataset version snapshot'. This distinguishes it from sibling tools like datasets_create (creates a dataset) and dataset_export (exports data). However, the term 'frozen' could be elaborated.
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 usage guidelines are provided. There is no indication of when to use this tool versus alternatives like dataset_export or datasets_create. Missing context such as prerequisites (e.g., dataset must exist) or typical workflows.
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?
The annotations indicate this is a non-readOnly, non-idempotent operation with side effects (openWorldHint=true). However, the description does not elaborate on the asynchronous nature of the job, how to monitor progress, or potential failure modes. It adds minimal behavioral context beyond what annotations already provide.
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, concise sentence that front-loads the core purpose. However, it sacrifices necessary detail, which slightly reduces its effectiveness.
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 initiates an asynchronous ingest job, yet the description fails to explain expected outcomes, status checking, or error handling. Given the lack of output schema and the complexity of the operation, more contextual information is needed for effective use.
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?
With only 33% schema description coverage, the description does not compensate by explaining parameters. It adds no meaning beyond the schema: 'sourceUrl' and 'targetSplit' remain undocumented. The schema's description for 'dataset' is already present, but the description adds no further 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 specific action ('Start a remote URL ingest job') and the resource ('an existing dataset'). It distinguishes the tool from sibling tools like dataset_upload_file or dataset_upload_folder by specifying 'remote URL ingest'.
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 (e.g., dataset_upload_file for local files). There are no prerequisites or conditions mentioned, leaving the agent to infer usage context.
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?
Description adds no behavioral context beyond annotations. Annotations already indicate non-read, non-idempotent, non-destructive, but description does not elaborate on side effects, auth needs, or any constraints.
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, front-loaded with verb and resource. No wasted words.
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?
With 6 parameters, low schema coverage, no output schema, and no details on required fields or format (e.g., slug, visibility), the description is insufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 17% (only 'task' has description). Description provides no additional meaning for any parameter, leaving 5 of 6 parameters unexplained.
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 action (Create), resource (dataset), and context (in your Ultralytics workspace). It distinguishes from sibling tools like datasets_list and datasets_delete.
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 vs alternatives (e.g., datasets_ingest, dataset_upload_file). No prerequisites or exclusions 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?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's mention of 'List' adds no new safety info. The 'in your Ultralytics workspace' adds minimal context beyond annotations.
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, no fluff. Every word earns its place.
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, so description should hint at return format or pagination. It doesn't. Given the parameter and sibling tools, more context needed for full understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one optional parameter 'username' with no description (0% coverage). The tool description does not mention this parameter at all, failing to add 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 the tool lists datasets in the user's Ultralytics workspace, with a specific verb and resource. It distinguishes from sibling tools like datasets_get and datasets_create through the 'list' action and workspace 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 on when to use this tool vs alternatives like search or filter capabilities. No mention of prerequisites or when not to use it.
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?
The description lacks detail on side effects, authentication requirements, or behavior during overwrites. Additionally, there is a contradiction: the description describes a read-like operation ('download'), but annotations set readOnlyHint=false, implying possible server-side modifications. The description does not clarify this inconsistency.
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 concise sentence with no redundancy. It could benefit from slightly more detail without becoming wordy, but it is well-structured for its length.
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 (2 required) and no output schema, the description omits crucial details such as return value (file path, success status), blocking vs async behavior, and error handling. This leaves the agent underinformed 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 description adds context to 'model' (id or slug) and 'output_path' (local destination). However, with schema coverage at 40%, it fails to explain 'project', 'filename', or 'overwrite' behavior. Baseline 3 is appropriate as partial compensation is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('download'), the resource ('trained model weight file'), and the target destination ('explicit local path'), effectively distinguishing it from sibling tools like dataset_upload_file or model_predict.
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 (e.g., model_predict for inference, or other download methods). There is no mention of prerequisites or scenarios where it should not be used.
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 neutral (readOnlyHint, destructiveHint, idempotentHint all false). The description adds value by disclosing the ffmpeg extraction and ingest initiation, but it omits important behavioral details like whether existing frames are replaced or appended, error handling, or resource implications (e.g., disk space usage).
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 of 16 words, efficiently conveying the core action. However, it sacrifices necessary detail for brevity. It earns a high score for conciseness but a deduction for incomplete structure (no param descriptions, no usage notes).
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 output schema and low schema coverage, the description is inadequate for a tool that performs a multi-step process (ffmpeg extraction then ingest). It does not explain return values, error conditions, required permissions, or how the ingest phase affects the dataset. Missing critical context for correct invocation.
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?
With only 40% schema coverage, the description must explain parameters, but it does not. Three parameters (fps, max_frames, targetSplit) lack descriptions in the schema, and the description offers no additional meaning beyond the schema's sparse info for video_path and dataset. The description repeats 'video' and 'dataset' concepts without elaborating on their format or constraints.
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 uploads a local video, extracts JPEG frames using ffmpeg, and starts dataset ingest for an existing dataset. This specific verb+resource combination distinguishes it from sibling tools like dataset_upload_file (single file upload) and dataset_upload_folder (folder upload).
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 vs alternatives, no prerequisites mentioned, and no exclusions. For example, it does not state that the dataset must already exist or that the video must be a supported format. The description lacks explicit context for selection.
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?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds no behavioral context beyond what's already known, such as pagination or response format.
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?
A single sentence is concise, but at the cost of missing important details; it could be more informative while remaining brief.
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 search tool with three parameters and no output schema, the description inadequately covers pagination (offset), sorting, and return structure, leaving agents underspecified.
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?
With 0% schema description coverage, the description should explain parameters but only mentions 'search', leaving q, sort, and offset largely unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (search), resource (public projects), and platform (Ultralytics Explore), effectively distinguishing it from sibling tools like projects_list.
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 searching public projects but does not provide explicit guidance on when to use this tool versus alternatives like explore_datasets or projects_list.
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 already indicate the tool is not read-only, not idempotent, and not destructive. The description adds that it is state-changing and may cost credits, which is useful context beyond annotations. However, it doesn't disclose other behavioral traits like return format or additional 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?
The description is a single sentence of 13 words with no fluff. Every word adds value: 'create', 'model export job', 'state-changing', 'may cost credits', 'Requires confirm_cost=true'. It is front-loaded with the core action.
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?
Despite having 8 parameters and no output schema, the description is very brief. It doesn't explain what happens after creating the export job, how to monitor progress (sibling tool export_status exists but not mentioned), or what the response contains. The tool is moderately complex, but the description omits essential context.
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 low (38%), and the description only mentions confirm_cost. It does not explain the meaning or constraints of other parameters like half, imgsz, dynamic, gpu_type, etc. The description fails to compensate for the low schema coverage, leaving many parameters underspecified.
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 'create' and resource 'model export job'. It distinguishes from sibling tools like 'dataset_export' by specifying 'model export', though it doesn't explicitly name the sibling. The purpose is clear but could be more explicit about the resource type.
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 the tool is state-changing, may cost credits, and requires confirm_cost=true. It implies when to use (when export is needed and cost is acceptable) but provides no explicit guidance on when not to use or alternatives (e.g., dataset_export for dataset exports). The requirement for confirm_cost is a helpful constraint.
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 already indicate this is a write operation (readOnlyHint=false) and not destructive (destructiveHint=false). The description adds 'Create a project', which is consistent but provides no additional behavioral context such as idempotency, return value, or error conditions (e.g., duplicate names).
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, front-loaded sentence that directly states the tool's purpose. However, it could benefit from additional context without becoming overly verbose.
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 creation tool with 3 parameters and no output schema, the description is minimal. It does not clarify what a 'project' is in the Ultralytics workspace, expected outcomes, or error handling. Given the low schema coverage, more detail is needed for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 3 parameters (name, slug, description) but 0% schema description coverage. The description does not mention any parameter details, leaving the agent uninformed about the meaning or constraints of 'slug' and 'description'. The description adds no value beyond the schema's type declarations.
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 'Create a project', using a specific verb and resource. It distinguishes this tool from siblings that list, get, or delete projects, so the purpose is unambiguous.
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 you want to create a project, but provides no explicit guidance on when to use this tool versus alternatives (e.g., projects_list for finding existing projects, projects_get for details). No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no behavioral hints beyond non-read-only, non-idempotent, non-destructive. Description adds no further context about side effects (e.g., zip disposal, ingest failure handling, or permission requirements). The description carries full burden but is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with clear verb-object structure. No redundancy, front-loaded with key action and resource. Every part earns its place.
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 3 parameters, no output schema, and no nested objects, the description covers the core purpose but omits details about optional parameter targetSplit and the ingest process. It is minimally adequate but lacks completeness for full understanding.
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 67% (2 of 3 params have descriptions). The description does not add parameter-specific details beyond the schema; it only mentions 'local image folder' and 'existing dataset' but not the parameters directly. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'Upload' and resource 'local image folder', specifying the action of zipping and starting ingest for an existing dataset. This distinguishes it from siblings like dataset_upload_file and dataset_upload_video which handle single files or videos.
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 dataset_upload_file or dataset_upload_video. The description implies it's for folders but does not explicitly state exclusions or context for selection.
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 already declare readOnlyHint=true and destructiveHint=false, so the tool's safety is clear. The description adds no additional behavioral context (e.g., pagination, scope of projects listed), but does not contradict annotations.
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?
One concise sentence that conveys the core purpose. Slightly improved clarity if it mentioned 'authenticated user' or 'all', but still efficient and front-loaded.
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?
A simple list tool with one optional parameter and no output schema; the description is adequate but does not mention optional filtering or return format, leaving minor 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?
The schema has one optional parameter (username) with no description, and the tool description does not explain its purpose. With 0% schema description coverage, the description should compensate but fails to do so.
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 computer-vision projects in the user's Ultralytics workspace. It uses a specific verb ('list') and resource ('projects'), and distinguishes from siblings like projects_create or projects_delete.
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 such as explore_projects or datasets_list. The description does not mention when not to use it or provide context for selection.
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 already provide readOnlyHint=true and destructiveHint=false. Description adds no extra behavioral context (e.g., what happens if version not found, or link expiry).
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, no extraneous information, front-loaded with action and resource.
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?
Simple tool but description lacks return format (what is the link? URL? download link?). Without output schema, more detail would be helpful for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50% (dataset parameter described, version undocumented). Description hints at version semantics ('latest or one frozen') but doesn't fully compensate for missing schema description of version.
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?
Clear verb 'get' with specific resource 'export link' and qualifier 'latest or one frozen dataset version'. Distinguishes from sibling tools like dataset_images_list and export_create.
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 vs alternatives (e.g., export_create). No mention of prerequisites or context for choosing version vs latest.
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?
The description adds 'soft-delete' behavior beyond annotations, which already set destructiveHint=true. However, it does not elaborate on what 'soft' means (e.g., recoverability, visibility, cascade effects), leaving significant behavioral gaps for a destructive operation.
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 with no superfluous words. It is front-loaded with the verb and key detail about identifier types. Perfect structure for minimal length.
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 good schema coverage, annotations, and single parameter, the description covers basic purpose but omits return behavior, error states, and the practical implications of soft-delete. For a destructive tool, more completeness is expected.
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?
Parameter 'dataset' has 100% schema coverage with a description nearly identical to the tool description. No additional syntax, constraints, or examples are provided. Baseline 3 is appropriate as the description adds no value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('soft-delete') and the resource ('dataset'), covering multiple identifier types (id, slug, username/slug, ul:// URI). It effectively distinguishes this tool from sibling tools like datasets_create or datasets_list.
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, nor does it mention prerequisites, post-deletion effects, or related tools like restore. It lacks explicit 'when-to-use' or 'when-not-to-use' context.
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 already indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the description's addition of 'works for private and public projects' adds minimal extra context. No contradictions or further behavioral details are provided.
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 (16 words) that front-loads the key information. Every word contributes to the purpose, with no wasted text.
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 description fails to address the tool's complexity: no explanation of return values (no output schema), no prerequisites (e.g., training must be active), and no guidance on the optional parameters that control history and metrics. It is incomplete for an agent to use effectively.
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?
With only 20% schema description coverage, the description does not compensate for the 4 undocumented parameters. It adds no meaning beyond the schema, leaving the agent without guidance on 'project', 'history_last_n', 'include_history', and 'include_metrics'.
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 a specific verb ('report') and resource ('model's training status and progress'), and distinguishes itself from siblings like 'training_start' and 'models_get'. The added note about private/public projects further clarifies its scope.
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 (works for private/public projects) but lacks explicit guidance on when to use this tool versus alternatives, such as 'models_get' or 'training_start'. No when-not-to-use or exclusions are provided.
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?
Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true. The description adds context that the tool searches public datasets, implying it accesses external data. It does not contradict annotations and provides reasonable behavioral context beyond annotations.
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 captures the tool's core function without extraneous words. It is front-loaded and efficient.
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 output schema, no param descriptions, and four parameters including ‘offset’ (implying pagination), the description fails to explain expected return values, pagination behavior, or how to effectively use parameters. An agent would likely need additional guidance to invoke correctly, making it incomplete.
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%, yet the description provides no explanation of the four parameters (q, sort, task, offset). While parameter names suggest 'q' is query and 'task' is task type, explicit semantic meaning is missing. For a search tool, describing 'q' as search term and 'task' as filter would be valuable. The description adds no 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?
The description clearly states the verb 'search' and resource 'public datasets' on 'Ultralytics Explore'. It distinguishes from siblings like 'datasets_list' (likely listing the user's own datasets) and 'explore_projects' (searching projects). The purpose is specific and unambiguous.
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?
No explicit guidance on when to use this tool versus alternatives like 'datasets_list' or 'dataset_ingest'. The description implies usage for searching public datasets, but lacks when-not-to-use or alternative references. This is a minimal viable level.
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 already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds no further behavioral context (e.g., pagination, rate limits). It does not contradict annotations.
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 succinct sentence with no extraneous words, earning its place with clarity and brevity.
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 description adequately states the tool's purpose for a simple list operation, but lacks details about return structure or pagination. The missing output schema adds to the need for more context, which is not provided.
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 50% (model parameter documented, project parameter not). The description does not elaborate on the project parameter or provide additional meaning beyond the schema. The model parameter's schema description is already clear.
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 'List export jobs for a model' clearly states the verb (list), resource (export jobs), and scope (for a model). It distinguishes from siblings like export_create and export_status, which are write and status operations respectively.
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?
Usage context is implied (use when you need to see export jobs for a model), but no explicit when-to-use or when-not-to-use guidance is given. No alternatives are mentioned, though the sibling tools provide natural contrasts.
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?
Annotations already indicate readOnlyHint=true and openWorldHint=true, so the tool is known to be a safe read operation. The description adds the behavioral detail that local file paths are not supported, which is valuable beyond annotations. It does not contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that includes the core action, the resource, and a crucial constraint. Every word is necessary and earns its place. No wasted text.
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 6 parameters, 2 required, and no output schema, the description is far too minimal. It does not explain what the tool returns, how to handle results, or what constitutes a successful inference. The agent would lack crucial context for integration.
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 33% (2 of 6 parameters have descriptions). The description does not explain the meaning or usage of iou, conf, imgsz, or project. It barely adds value beyond the schema's minimal descriptions. For a low-coverage schema, the description should compensate but fails to do so.
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 verb 'Run inference' and resource 'trained model on an image URL or base64 source'. It also explicitly excludes local file paths, distinguishing it from any possible file-based siblings. This verb+resource+scope combination is specific and unambiguous.
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 (when inference is needed) and provides a constraint (no local file paths), but does not explicitly state when not to use or suggest alternative tools. It gives clear context but lacks exclusions or comparisons.
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 already declare readOnlyHint=true. The description adds the dual-lookup behavior (id or slug+project), but no additional behavioral details like error handling or authentication.
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, clear sentence that immediately conveys the tool's purpose and key variants. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple getter with full schema coverage and annotations, the description is complete. Missing output schema but not critical for a single-model retrieval.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (both parameters described). The description reiterates the schema logic, adding no new semantics beyond the conditional relationship between model and project.
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 retrieves one model by id or slug+project, distinguishing it from sibling tool models_list which returns multiple models.
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?
Usage is implied but not explicit; no guidance on when to use this vs. alternative tools like models_download or models_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?
The description adds transparency beyond annotations by noting 'state-changing, may cost credits' and 'requires confirm_cost=true'. Annotations do not contradict this; readOnlyHint=false aligns with state-changing 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 concise, with two sentences that are front-loaded with the core purpose and key requirement. No unnecessary words.
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 10 parameters, a nested object (train_args), and no output schema, the description is too minimal. It lacks details on return values, error handling, or how to configure training via train_args, making it incomplete for a complex 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?
With 50% schema description coverage, the description compensates slightly by clarifying that 'model' can be an existing ref or base checkpoint, and that confirm_cost must be true. However, many parameters (batch, epochs, imgsz, name, train_args) remain undocumented, leaving gaps.
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 action: 'Start a cloud training job' with specific sources (existing model or YOLO base checkpoint). It distinguishes from sibling tools like training_monitor and model_predict.
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 mentions a precondition ('Requires confirm_cost=true') but does not provide explicit guidance on when to use versus alternatives or when not to use it. The context is implied but not comprehensive.
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 already indicate readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that the tool takes an ID and returns status, which is consistent but does not reveal additional behavioral details beyond the schema.
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 with no unnecessary words. It is front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple status-check tool with one required parameter and no output schema, the description covers the basic functionality. However, it could be enhanced by briefly mentioning what the status contains or expected format, but it is minimally adequate.
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%, and the parameter description in the schema ('24-character export job id.') is essentially identical to the tool description. No additional meaning is added beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get', the resource 'status for one export job', and the required identifier '24-character export id'. It effectively distinguishes this tool from siblings like 'export_create' and 'exports_list'.
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 a specific export job ID is known, distinguishing it from tools that list or create exports. However, it does not explicitly state when not to use it or suggest alternatives.
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 already declare readOnlyHint=true and destructiveHint=false, so the description's 'get details' is consistent. The description adds the identifier format variety but no further behavioral details (e.g., error handling, performance). With annotations, this is adequate.
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, concise, and front-loaded with the key action. No unnecessary 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 description covers input but lacks output details. Without an output schema, it would benefit from mentioning what fields are returned (e.g., 'returns name, description, owner'). As a simple get tool, it is minimally adequate but could be more informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (parameter 'project' already described). The tool description repeats the parameter description without adding new meaning. Baseline 3 applies as schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get details'), the resource ('one project'), and the multiple identifier types (id, slug, username/slug, URI). It distinguishes from siblings like projects_list and projects_create by specifying single-project retrieval.
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 implicitly indicates when to use this tool (when you need details of a specific project) but does not explicitly state when not to use it or mention alternative tools. The sibling context makes this clear, but the description itself lacks explicit usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds that the tool accepts multiple identifier formats and returns 'details', which is consistent. No additional behavioral traits (e.g., rate limits, auth needs) are disclosed, but the annotations suffice.
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 extraneous information. It front-loads the action ('Get details') and resource ('one dataset'), then specifies the identifier formats 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?
With no output schema, the description does not detail the return structure, but 'details' is generally understood. Given the tool's simplicity and the presence of sibling tools that might also fetch data (e.g., dataset_export), a brief note on what 'details' includes (e.g., metadata, fields) could improve completeness. However, it is adequate for a straightforward retrieval operation.
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?
With 100% schema description coverage, the parameter 'dataset' is already well-documented in the schema ('Dataset ref by id, slug, username/slug, or ul:// URI.'). The tool description repeats this format but adds no new meaning. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves details for a single dataset, specifying multiple identifier formats (id, slug, username/slug, ul:// URI). This distinguishes it from sibling tools like datasets_list (which lists all datasets) and datasets_create (creates datasets).
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 lists the identifier formats supported, guiding usage. It implies this tool is for fetching a specific dataset, not for other operations. While it doesn't explicitly state when to avoid using it or suggest alternatives, the context of sibling tools provides clarity. A more explicit note about not using it for listing or creation would elevate this.
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?
Annotations already declare `destructiveHint: true`, and the description explicitly mentions deletion. It adds the behavioral detail that the model can be identified either by ID or by slug+project, which helps the agent understand supported identification methods beyond what annotations provide.
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 unnecessary words or repetition. It is optimally concise for the tool's simplicity.
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?
Considering the simple nature of a delete operation and the presence of `destructiveHint` annotation, the description covers the essential identification methods. It does not explain return behavior or side effects, but for a destructive action with no output schema, it is adequate.
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 documentation coverage is 100%, and the description largely mirrors the schema's parameter descriptions. It concisely restates the two identification modes but does not add new semantic details beyond the schema, warranting a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Delete'), the resource ('a model'), and the two identification methods ('by id, or by slug plus project'). It distinctly differentiates from sibling tools like `models_get` and `models_list` by specifying the deletion action.
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?
No explicit guidance on when to use this tool versus alternatives, nor any mention of prerequisites or conditions. The usage is implied by the action 'Delete' but lacks context such as 'when you need to remove a model permanently' or 'use models_get to verify before deletion'.
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 already declare readOnlyHint=true and destructiveHint=false, so the description's role in behavioral transparency is reduced. The description adds context about input format (project identifiers) but does not disclose additional behavioral traits like pagination, ordering, or response structure.
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 purpose and includes all necessary information without redundancy. Every word adds value.
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 one parameter and no output schema, the description is complete. It covers the purpose and input specification. The lack of output format details is acceptable given the tool's simplicity and the presence of annotations for behavioral safety.
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% coverage with a clear description of the 'project' parameter. The tool description reiterates the parameter meaning but adds minimal new information beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (list) and resource (models) and specifies the scope (in a project). It also enumerates how to identify the project, distinguishing it from sibling tools like models_get (single model) or datasets_list (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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (to list models in a project) and specifies how to specify the project (by id, slug, username/slug, or ul:// URI). However, it does not explicitly exclude alternatives or mention when not to use it, though the simplicity of the tool makes this less critical.
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 explicitly says 'soft-delete,' which implies reversibility, adding context beyond the annotations (destructiveHint=true). However, it does not elaborate on the exact behavior (e.g., restore capability, cascading effects, response).
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 conveys all essential information without extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool (one param, no output schema), the description is mostly complete. However, it could include more context about post-action state or confirmation, though the soft-delete label provides some completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter. The description essentially repeats the schema's parameter description, adding no new information. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (soft-delete), the resource (project), and the supported identifiers (id, slug, username/slug, ul:// URI). It distinguishes itself from sibling tools like projects_create, projects_get, and projects_list.
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 does not provide guidance on when to use this tool vs. alternatives (e.g., permanent deletion). It lacks prerequisites, conditions, or examples of appropriate use 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?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds no additional behavioral context beyond confirming the tool's purpose. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, well-structured sentence conveys the full purpose without superfluous words.
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 no parameters, no output schema, and annotations cover the safety profile, the description provides all necessary context for a simple read query.
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 no parameters, so the description does not need to add parameter semantics. Baseline score of 4 is appropriate as there is nothing to document.
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 a specific verb 'Get' and clearly identifies the resource 'cloud-GPU stock status' with a qualifier 'by GPU type'. It distinguishes this tool from its siblings, which focus on datasets, models, and projects.
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?
There is no explicit guidance on when to use this tool versus alternatives. However, the narrow focus on GPU availability and the sibling tools being about datasets and models imply its appropriate use case.
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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