Polar Cloud MCP

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by Polar3D

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Server Quality Checklist

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Latest release: v0.2.0
Disambiguation5/5
Every tool has a clearly distinct purpose. Personal and school contexts are cleanly separated, and within each context, operations like listing, uploading, slicing, and approving are unambiguous.
Naming Consistency4/5
All tool names start with a verb and use snake_case, which is consistent. Minor deviations like 'preview_plate' (verb+noun) and 'slice_and_queue' (compound verb) are still readable and follow the overall pattern.
Tool Count5/5
11 tools is well-scoped for a 3D printing cloud server covering both personal and educational workflows. Each tool addresses a specific need without redundancy.
Completeness2/5
The tool set covers model generation/upload, slicing, and queuing, but lacks critical lifecycle operations: there is no way to list queued jobs, cancel a job, or start a print. Agents cannot fully execute a print workflow, leaving dead ends.
Average 3.7/5 across 11 of 11 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
- Last stable release on August 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.
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.
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose all behavioral traits. It mentions inclusion of thumbnail URLs and default pending status, but does not state whether the tool is read-only, whether it requires specific permissions, or what the response structure looks like. The lack of output schema and annotation burden makes this description insufficient.
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 two sentences, front-loading the main purpose. It is concise with no filler. Could potentially add one more sentence about usage without becoming verbose, so 4 rather than 5.
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 4 parameters and no output schema, the description should provide more context about return structure and behavior. It does not explain the default pagination (if any), how thumbnail URLs are formatted, or how to interpret the different queue_statuses beyond what's in the schema. The tool is relatively simple but still lacks completeness for an agent to use effectively without additional information.
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%: 'group_id' and 'queue_status' have descriptions in the schema, while 'skip' and 'limit' do not. The description adds context that 'group_id' comes from available_queues and explains 'queue_status' values, but does not address pagination parameters. Baseline 3 is appropriate since it partially compensates for the schema 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 lists student print requests for schools/classrooms managed by the teacher/admin, and mentions thumbnail URLs and default status. While it distinguishes it from siblings like 'list_school_printers' which lists printers, it could be more explicit about the differentiation, hence 4 rather than 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 explicit guidance on when to use this tool versus alternatives like 'approve_and_queue' or 'reject_queue_item'. The description implies it's for viewing pending requests but does not clarify when to use filtering via 'queue_status' or suggest using 'get_job' for individual job details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the student is notified, which is a useful side effect, but it does not disclose whether the operation is destructive, reversible, requires specific permissions, or what state the queue item enters after rejection. For a mutation tool, this is insufficient transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two short sentences with no wasted words. The core action is front-loaded, and every sentence contributes a distinct piece of information.
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 (2 params, no output schema, no annotations), the description is incomplete. It does not explain the lifecycle of the queue item after rejection, whether the action is reversible, or any permission requirements. The missing details could lead an agent to misuse the tool or misunderstand its effects.
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 50% description coverage (only 'comments' is described). The description adds that comments are optional, but this is already implied by the schema's lack of 'required' for comments. The description does not clarify the meaning of 'queue_item_id' (e.g., format, source, uniqueness) beyond the schema. With low coverage, the description should compensate, but it does not.
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 ('Reject') and the resource ('student print request'), and adds the behavioral detail that the student is notified. It distinguishes itself from sibling tools like 'approve_and_queue' by being the complementary action, and the purpose is immediately understandable without ambiguity.
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., 'approve_and_queue'). There is no mention of prerequisites, conditions for rejection, or when not to use it. The description simply states what it does, 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?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the basic action of uploading, without mentioning whether the operation is destructive, requires authentication, has size limits, or what the response looks like. This is insufficient for safe invocation.
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 with 18 words, which is concise. However, it could be more structured by explicitly stating that only one of file_path or file_url should be provided, and by separating the two input modes.
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, no annotations, and three parameters (one undocumented), the description is too minimal. It does not explain return values, error handling, or how to decide between file_path and file_url. It also does not leverage sibling context to differentiate usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 67% coverage; the description adds value by clarifying that file_path and file_url correspond to local and URL-based inputs, and that the accepted formats are STL, 3MF, OBJ. However, the 'name' parameter is not described in the schema nor in the description, leaving its purpose ambiguous.
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 (upload), target system (Polar Cloud), and supported file formats (STL, 3MF, OBJ). It also mentions the alternative input source (public file_url), distinguishing it from sibling tools like generate_model which create models rather than upload existing ones.
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 does not provide any guidance on when to use this tool versus alternatives such as generate_model or slice_and_queue. It lacks context on prerequisites, whether the model must be preprocessed, or when a URL is preferred over a local file path.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It only states the action ('get') but does not disclose whether the operation is read-only, idempotent, requires authentication, or what happens if both IDs are provided or if an invalid ID is given.
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 conveys the necessary information without any wasted words or redundancy.
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 no output schema and no annotations, the description fails to inform the agent about what the response contains (e.g., job details, status, metadata). This leaves a significant gap for an agent to correctly interpret the tool's result.
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 100% but the description adds value by associating 'job_id' with Polar Cloud print jobs and 'ai_job_id' with Tripo generations, clarifying which parameter applies to which context beyond the schema alone.
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 a Polar Cloud print job or a Tripo generation using the respective IDs. It uses a specific verb ('get') and identifies the resource ('job') along with the two distinct contexts.
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. It does not mention scenarios like 'use this when you have a job ID; for listing jobs use a different tool' – and there is no listing tool among siblings to contrast with.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the key actions (generate, save, return object_id) but omits details such as potential asynchronicity, failure modes, limits, or cost implications. The behavior is outlined but not fully transparent.
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, method, inputs, and output. Every word adds value; there is no fluff or redundancy.
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 output schema and annotations, the description is minimal. It covers the core flow (generate, save, return ID) but does not explain how the object_id is used, error handling, or prerequisites. For a tool with 4 parameters and no output schema, more detail would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so each parameter has a description in the schema. The tool description does not add new semantic information beyond what the schema already provides, merely restating the input sources. 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 verb 'generate', the resource '3D model', the method 'Tripo 3D', the input sources ('text prompt or an image'), and the outcome ('save it to Polar Cloud, and return object_id'). This is specific and distinguishes it from siblings like 'upload_model', which handles existing 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?
The description implies usage when a text prompt or image is available for model generation, but it does not explicitly contrast with alternatives like 'upload_model' (for pre-existing models) or provide when-not-to-use guidance. The context is helpful but not directive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It clarifies that the tool is non-destructive ('Does not create a job'), which is good. However, it does not disclose whether reading queue items or printer status has any side effects, nor any rate limits or permissions required.
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 short and to the point at 20 words. The first sentence clearly states the action, and the second adds the important behavioral caveat. It could be considered slightly terse, but every 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?
Given the complexity (4 params, no output schema, and 11 sibling tools), the description provides enough to distinguish this tool from others like 'slice_and_queue', 'approve_and_queue', and 'reject_queue_item'. It lacks an explicit return description, but given the absence of an output schema, describing the return values would help even more.
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%, so the description adds value for the 'queue_item_ids' (IDs from list_school_queues) and 'serial_number' (needed when multiple printers). The description mentions the purpose of queue items and the printer but does not explain 'brim' or 'gap_mm' beyond what the schema provides (boolean and number). It provides some additional context but does not fully compensate for the 50% coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it measures STLs and packs them onto a build plate, returning fit count, layout, and overflow. It is distinguished from siblings like 'generate_model', 'upload_model', 'slice_and_queue', and 'approve_and_queue' which are clearly different actions. The verb 'Measure... and pack' plus the specific resources (student queue STLs, school printer build plate) provide high precision.
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 explains the tool's purpose (previewing fit/layout before committing) and explicitly states 'Does not create a job,' which helps an agent decide to use this before 'approve_and_queue' or 'slice_and_queue'. However, it does not explicitly say when NOT to use it or name alternative tools for related tasks.
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 carries the full burden. It discloses that slicing uses Polar Cloud defaults (0.4 / 0.20mm / PLA), that prints are queued but not started, and nesting behavior. It does not mention side effects (e.g., whether items are removed from a queue after approval) or error scenarios, so transparency is adequate but incomplete.
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 three sentences, each carrying distinct information: purpose, key exclusion (does not start print), and special behavior for nest. No wasted words, front-loaded with core action.
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 9 parameters and no output schema, the description covers the main workflow, defaults, and a key parameter behavior. It lacks detail on return value or error handling but is reasonably complete for a tool that returns no output schema and has sibling context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 44% (4 of 9 parameters have descriptions), so the description must compensate. It adds meaning for the nest parameter (default when multiple ids, combines STLs) and suggests serial_number is conditionally required. However, it does not clarify brim, infill, support, layer_height, or tree_support beyond what the schema provides, but the defaults help narrow their purpose.
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 (approve, slice, queue) and identifies the resource (student print requests, Polar Cloud defaults, school printer). It distinguishes itself from siblings like reject_queue_item and slice_and_queue by explicitly mentioning approval and queueing with defaults, and also notes it does not start the print.
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 clearly states the tool does not start the print and mentions when to set nest=true. However, it does not explicitly say when to use this tool versus alternatives like slice_and_queue (which might handle slicing without approval) or reject_queue_item. Context for selection is implied but not fully explicit.
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 indicates a read operation ('Get'), which aligns with the tool's behavior. No annotations are provided, so the description carries the burden. It does not disclose any additional behavioral traits (e.g., authentication needs, rate limits), but for a simple retrieval tool, the verb suffices.
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, 14-word sentence. It is highly concise and front-loaded with the verb and resource. Every word 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has zero parameters and no output schema, the description enumerates the key pieces of data returned (user, subscription, prints, credits). This is sufficient for a simple getter. It could be slightly more complete by stating that it returns the current authenticated user's account, but the context of 'this user' implies it.
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?
There are no parameters, and schema description coverage is 100% (vacuously). With zero parameters, the baseline is 4. The description adds no param-specific information because none is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'account' (Polar Cloud user). It lists specific data returned: subscription plan, remaining prints, and AI credits. This distinguishes it from sibling tools like list_printers or get_job, which deal with different entities.
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 by the description: use it to retrieve account information for the current user. However, there is no explicit guidance on when to use vs. alternatives or when not to use. Given the sibling context, the purpose is clear, but the description lacks direct instruction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-only operation by using 'list' and mentions fetching data from 'printer.json'. However, it does not disclose authentication requirements, rate limits, or how 'this teacher' is determined. The agent infers safety but lacks explicit confirmation.
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 that efficiently conveys the purpose, scope, data included, and a usage instruction. Every element is necessary and front-loaded, with 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?
Given the tool has no parameters and no output schema, the description partially compensates by mentioning 'build plate size' and implicitly 'serial_number'. However, it does not fully describe the expected output structure (e.g., all fields, type of list, pagination). An agent might need to infer the complete return format from context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the baseline is 4. The description adds no parameter details (none needed) but provides context about what the tool returns, which is valuable for an agent deciding to invoke it.
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 school/classroom printers that the teacher can queue to, including build plate size. It distinguishes from the sibling 'list_printers' by specifying the scope ('this teacher can queue to') and includes a specific data field ('build plate size from printer.json').
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 gives explicit guidance on what to do with multiple results: 'If more than one, ask which serial_number to use.' This directly informs the agent's behavior. However, it does not explicitly state when to use this tool versus alternatives (e.g., sibling 'list_printers' or 'list_school_queues'), though the scope is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It reveals that the tool may return multiple results requiring user input, but does not disclose other behavioral traits such as authentication requirements, rate limits, or whether the operation is read-only. For a simple list operation, this is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences, front-loaded with the purpose, and contains no filler. Every 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 no parameters and no output schema, the description is fairly complete: it states the purpose and provides a usage hint. It could briefly mention the return format or fields, but given the simplicity, this 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?
There are no parameters (input schema empty, schema description coverage 100%). With zero parameters, the baseline is 4. The description adds no parameter-specific meaning, but none is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'List' and the resource 'personal Polar Cloud printers', which is specific and distinguishes from the sibling 'list_school_printers' that handles school printers.
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 gives a clear usage hint: 'If more than one, ask the user which serial_number to use.' This implies the tool returns a list requiring disambiguation. It implicitly distinguishes from 'list_school_printers' but does not explicitly state when to use this tool over alternatives.
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 provided, the description bears full responsibility for behavioral disclosure. It clearly states the tool uses defaults (0.4 nozzle, 0.20mm, PLA), does not start the print, and requires serial_number for multi-printer accounts. It could be improved by noting if any parameters are required beyond object_id, but overall provides strong transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. It front-loads the core action and defaults, then adds the critical note about not starting the print, then the multi-printer guidance. Every 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?
Given 8 parameters (38% schema coverage, no output schema), the description is fairly complete. It explains the overall action, defaults, and key behavioral constraint. A small gap is that it does not mention what the tool returns (e.g., confirmation or queue item ID) since no output schema exists, but the agent might infer from context. Overall, it is sufficient for an agent to select and use 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 38%, so the description must compensate. The description itself does not explain individual parameters beyond noting serial_number is needed for multiple printers. However, it sets context for default slicing behavior, which helps an agent infer that infill, layer_height, etc. are optional overrides. The schema already has good descriptions for object_id, infill, support, and serial_number, so the baseline is 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 tool slices an object with Polar Cloud defaults and adds it to a personal printer queue. It specifies the default settings (0.4 nozzle, 0.20mm, PLA) and distinguishes from sibling tools like 'preview_plate' or 'approve_and_queue' by noting it does not start the print.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says the tool adds to a queue but does not start the print, and instructs the user to pass 'serial_number' when the user has multiple printers. This provides clear when-to-use and when-to-include guidance, distinguishing it from approval tools like 'approve_and_queue'.
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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