Roboflow (Official)

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully covers behavior: multi-step conversation, draft-only saving, never auto-publishes, clarifying questions, incorrect URL warning, and response shape.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Long but well-structured with sections, bullet points, and bold text; every sentence is justified. Slightly verbose but all information is relevant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all important aspects: conversation flow, publishing, running workflows, response shape, and URL discrepancies. Despite complexity, no gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and description adds meaningful context to each parameter (mode options, message purpose, conversation_id usage), enhancing beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool is for chatting with the Roboflow AI agent, lists specific use cases (Q&A, complex workflows, planning), and distinguishes from sibling workflow_* tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly describes when to use this tool (e.g., advanced workflows, planning) and when not to (straightforward workflows using direct tools). Also provides flow details, critical publishing notes, and URL handling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It states it returns metadata and ordered messages, but does not explicitly confirm it's read-only or mention any side effects. For a get operation, this is acceptable but lacks full transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose, no redundant words. Every sentence contributes meaning without being verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter) and the presence of an output schema, the description covers purpose, usage, and parameter meaning adequately. Minor lack of error condition details, but overall sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds value by explaining the usage of conversation_id in relation to agent_chat, beyond the schema's description. This helps the agent understand the workflow.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Get a single agent conversation with its full message history', specifying verb and resource. It distinguishes from siblings like agent_chat and agent_conversations_list by indicating it returns the full history, not a list or continuation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs to pass the conversation_id back to agent_chat to continue the conversation, providing clear context on when to use this tool (retrieve history) and how it relates to another tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden for behavioral disclosure. It states it returns metadata but does not mention pagination, limits, sorting, or side effects, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each earning its place: purpose, return type, and sibling guidance. Front-loaded and no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple listing tool with two optional filters, the description is adequate. It explains what is returned and references siblings. Lacks pagination or rate limit details but still sufficient for basic usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds no parameter-specific information beyond what the schema already provides (source and workflow_url filters).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists agent conversations in the workspace and specifies the returned metadata fields (id, name, source, timestamps, linked workflow ids). It also distinguishes itself from siblings by referencing agent_conversation_get for full history and agent_chat for resuming conversations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly guides usage by directing to siblings for detailed history or resuming conversations. However, it lacks explicit when-to-use versus alternatives statements or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It explains the promotion behavior, error condition (400 if no draft), and the return object. It could mention that publishing is a mutation but it's implied by 'goes live'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a clear front-loaded purpose, followed by context and error conditions. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema provided, the description fully specifies the expected return ({ workflowId, workflowUrl, versionId, status }) and mentions the error case. It is complete for this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the parameter description is already clear ('URL slug...'). The description adds context by linking the parameter to agent_chat output, enhancing usability.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Publish the latest agent-edited draft of a workflow' with specific verb 'publish' and resource 'draft'. It distinguishes from siblings by explaining that the agent never publishes on its own.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use (when there is a draft to promote) and when not to use (errors if no draft). It also clarifies the outcome: 'goes live for callers using the workflow by id'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide comprehensive behavioral hints (read-only, open-world, idempotent, non-destructive). The description adds value by specifying what details are returned (image count and status), which goes beyond annotations. It doesn't contradict annotations (which correctly indicate a safe read operation) and provides useful context about the return content.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with just two sentences. The first sentence states the core purpose, and the second specifies what details are returned. Every word earns its place with zero redundancy or unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 required parameters), comprehensive annotations, and existence of an output schema, the description is reasonably complete. It specifies what details are returned (image count and status), which is helpful since the output schema isn't visible here. However, it could mention that this is for annotation batches specifically (implied by tool name but not stated).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both parameters clearly documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides (batch_id and project_id). However, it doesn't need to compensate since schema coverage is complete, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get details about a specific batch' with specific resources (batch details including image count and status). It distinguishes from sibling 'annotation_batches_list' by focusing on a single batch rather than listing multiple. However, it doesn't explicitly contrast with other batch-related tools that might exist.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by specifying 'a specific batch' and mentioning what details are returned. It differentiates from 'annotation_batches_list' by focusing on individual batch retrieval rather than listing. However, it lacks explicit guidance on when to use this versus alternatives like 'projects_get' or 'versions_get' for related metadata, or prerequisites for accessing batch details.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare this as read-only, non-destructive, idempotent, and open-world, so the agent knows it's a safe, repeatable query. The description adds useful context about what information is returned (id, name, image count, upload info), which isn't covered by annotations. However, it doesn't mention pagination, rate limits, or authentication needs beyond what annotations imply.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two sentences: the first states the action and scope, the second specifies the return data. Every word serves a purpose, and key information is front-loaded. There's no redundancy or unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, read-only operation), rich annotations, and the presence of an output schema (which handles return value documentation), the description is reasonably complete. It covers the core purpose and output structure. The main gap is lack of usage guidance relative to sibling tools, but overall it provides sufficient context for basic use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema fully documents the single required parameter 'project_id'. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't clarify format variations or constraints). This meets the baseline of 3 when the schema handles parameter documentation effectively.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('upload batches in a project'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'projects_list' or 'workflows_list', which also list resources within projects, leaving some ambiguity about when this specific listing tool is appropriate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'annotation_batches_get' (which might retrieve a single batch) or explain why one would list batches instead of using other listing tools. There's no context about prerequisites, timing, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate this is a non-readOnly, non-destructive, non-idempotent, open-world operation. The description adds value by specifying it's for batch image assignment and returns job details, but doesn't disclose additional behavioral traits like rate limits, auth needs, or side effects beyond what annotations cover.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence and adds return details in the second, with zero wasted words. It's appropriately sized and structured for clarity without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 required parameters), rich annotations, and presence of an output schema, the description is mostly complete. It covers creation purpose and return values, but could improve by addressing usage context or behavioral nuances, though the output schema reduces need for return value details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so parameters are fully documented in the schema. The description doesn't add meaning beyond the schema, such as explaining relationships between parameters (e.g., batch_id must exist). Baseline 3 is appropriate as the schema handles parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Create an annotation job') and resource ('batch of images to a labeler'), distinguishing it from sibling tools like annotation_batches_get or models_train. It precisely defines the tool's function without being vague or tautological.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as workflows_create or projects_create, nor does it mention prerequisites like needing an existing batch or project. It lacks explicit context or exclusions for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering key behavioral traits. The description adds minimal context beyond this, stating it saves annotations for existing images but not detailing effects like overwriting behavior, authentication needs, or rate limits. It doesn't contradict annotations, so a baseline score is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (5 parameters, 4 required), rich annotations, and the presence of an output schema, the description is reasonably complete. It covers the core action but lacks details on error conditions or integration with sibling tools. The output schema reduces the need to explain return values, keeping the description adequate though not exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with all parameters well-documented in the input schema. The description doesn't add any meaningful semantic information beyond what the schema provides, such as explaining relationships between parameters or usage nuances. With high schema coverage, the baseline score of 3 is justified.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Save') and resource ('annotation for an existing image'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'annotation_batches_get' or 'annotation_jobs_create', which also involve annotations but serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing image), exclusions, or comparisons to sibling tools like 'annotation_batches_get' or 'annotation_jobs_create', leaving the agent with no contextual usage information.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (readOnly, idempotent), the description adds polling frequency, start delay, and return shape including statuses. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences that front-load purpose and provide necessary behavioral details without redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Output schema is present, and the description already outlines the return shape. Combined with annotations and parameter description, the tool definition is fully complete for a polling operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers task_id with description; the description adds context that the ID comes from an async-enqueueing tool (e.g., projects_fork), enhancing understanding beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Poll an async task by id,' providing a specific verb and resource. It includes polling details (every 5 seconds, up to 30 seconds to start) that distinguish it from sibling tools focusing on other entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear usage guidance on polling interval and expected processing time. It does not explicitly exclude alternatives, but the context of sibling tools indicates this is the dedicated polling tool for async tasks.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds value by listing specific return fields (status, model/project type, image counts, subjob progress, ontology, confidence thresholds, linked annotation job id), which go 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasted words. The first sentence fronts the core purpose, the second enumerates return items. Perfectly sized for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists (covering return structure) and annotations cover safety, the description adequately explains the tool's output. It could mention error conditions or pagination for completeness, but for a simple get-by-id tool, it's largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers the sole parameter (autolabel_job_id) with 100% description coverage. The description adds no new parameter-specific meaning beyond implying it identifies the job, which is already clear from the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves per-subjob status and progress for an auto-label job. It specifies the action (get) and resource (per-subjob status/progress). This distinguishes it from sibling tools like autolabel_start, which initiates jobs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use after starting a job but lacks explicit guidance on when to use versus alternatives, such as checking the linked annotation job. No when-not-to-use or context for alternatives is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=false, destructiveHint=false, openWorldHint=true, and idempotentHint=false. The description adds that the tool returns jobId and annotationJobId, and that it starts a job (non-idempotent). No contradictions, but it doesn't elaborate on side effects or state changes beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first states the purpose, the second provides the return value and a pointer to the sibling tool for follow-up. No redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (11 parameters, nested objects, output schema), the description covers the core functionality and return value. It suggests polling with autolabel_job_get but does not explain when to use this over other annotation tools. Overall, it is fairly complete for a job-starting tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already explains each parameter thoroughly. The tool description adds no additional parameter semantics beyond what's in the schema, earning a baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Start'), the resource ('hosted auto-label job'), and the scope ('over a batch of images'). It also specifies the return format and a sibling tool for polling, helping distinguish from autolabel_job_get and other annotation tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells the agent to poll progress with autolabel_job_get, providing a clear next step. However, it does not explicitly state when not to use this tool or compare it with other job creation tools like annotation_jobs_create.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=true, but the description states it calls projects_create, which is a write operation, causing a contradiction. No additional behavioral traits disclosed beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that efficiently conveys the tool's purpose without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the tool has no parameters and no output schema, the description lacks details about what the interactive app does, how to interact, or what the user can expect. Minimal but adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters to describe; schema coverage is 100%. Baseline score of 4 for zero-parameter tools applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it opens an interactive create-project app that calls projects_create, distinguishing it from direct creation tools like projects_create. However, it does not explicitly differentiate from other interactive tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 projects_create directly. The description implies it is an interactive alternative but does not provide context or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (readOnlyHint false), description adds: required scope, return format with fields explanation (deviceId, installId, offlineProvisioningQrPayload), and special case for AI1 devices. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences: purpose, scope and return values, followed by a clarifying detail. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With annotations and rich schema, the description covers purpose, auth, return structure, and edge case. Lacks explicit mention of output schema but describes the returned fields adequately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% description coverage for all parameters. Description does not add significant new parameter-level info beyond the schema except explaining the return value conditions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description states 'Provision a new v2 device' with a clear verb+resource. Differentiates from sibling tools like 'devices_get' and 'devices_list' which are for retrieval or listing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for creating a new device, and the scope requirement is specified. No explicit alternatives or when-not-to-use, but the sibling tools are clearly different operations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that a 404 is returned if the device does not exist or belongs to a different workspace, which is useful but does not cover other behavioral aspects like permissions or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with two sentences, front-loading the core purpose and including only essential error information, with no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple retrieval nature, the description adequately covers purpose, return type, and an error case. However, with many sibling tools for device data, a brief note distinguishing base device object from config/telemetry would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema fully describes the only parameter ('device_id' with a clear description), and the description does not add additional semantic value beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get a single device by id' with a specific verb and resource, and it distinguishes itself from siblings like 'devices_list' and 'devices_create' through the singular retrieval focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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, such as 'devices_get_config' or 'devices_get_telemetry', leaving the agent without context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds critical behavioral context: the response includes sensitive data (environment_variables and integration credentials), advising caution. This goes 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: the first states the core purpose, the second enriches with return details and a security note. No wasted words, front-loaded, and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read tool with one parameter, output schema, and rich annotations, the description is nearly complete. It covers purpose, return fields, and sensitivity. Minor omission: no mention of error cases or prerequisites, but it's still sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter device_id has 100% schema description coverage ('Device id'). The tool description does not add any additional meaning or constraints beyond the schema, so it meets the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get the device's current runtime configuration' with a specific verb and resource. It distinguishes from sibling tools by focusing on 'current runtime configuration' and listing returned fields, differentiating it from devices_get, devices_get_config_history, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving the full current config but does not explicitly state when to use this tool vs alternatives like devices_get or devices_get_config_history. No exclusions or alternative guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds value by detailing the return structure (pagination with next_cursor, has_more, limit) and key fields (revision_id, created_at, created_by), going 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exceptionally concise: two sentences, no filler, front-loaded with the primary action and ordering. Every sentence adds essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple list tool with pagination, the description is adequate. It explains the return format, ordering, and pagination. It could mention rate limits or error handling, but given the annotations and output schema context, it is sufficiently complete for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for all three parameters. The description does not add new information about parameters beyond what the schema already provides, but it does clarify the return structure's fields, which is acceptable for a baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists prior configuration revisions, specifies newest-first ordering, and distinguishes from sibling tools like devices_get_config (current config) and devices_get_logs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is clear (listing history), but there is no explicit guidance on when to use this tool vs alternatives like devices_get_config, nor any exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate read-only, idempotent, non-destructive. Description adds return structure details, scope requirement, and that device-scoped keys are rejected, providing useful 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Five sentences, each adding value: purpose, return structure, use cases, scope, and caveat. Front-loaded with main action. Minimal and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with an output schema, the description explains the return format, use cases, and authorization requirements. Nothing missing for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters in the input schema, so no explanation needed. Baseline 4 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get the workspace's default device configuration' and explains the return structure {config, patch}. It distinguishes from siblings by specifying that this is workspace-wide and device-scoped keys are rejected, setting it apart from per-device config tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides concrete use cases: inspecting service versions for new devices and seeding payloads to devices_update_config. Mentions required scope. Could explicitly mention when not to use (e.g., for per-device config), but overall strong guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds the return shape (pagination structure) but does not disclose additional behavioral traits beyond what annotations provide. 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences front-loading purpose and return shape with no wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (9 parameters, paginated output), the description is minimal. It states purpose and return structure but does not explain event types or pagination details. However, with rich annotations and output schema present, it is minimally adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with all parameters described in the input schema. The description adds no extra parameter details beyond what the schema already provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists device and stream lifecycle events, using a specific verb and resource. It distinguishes from sibling tools like devices_get_logs and devices_get_telemetry.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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, nor does it mention any exclusions or conditions. It only states the basic purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds specific rate limits (5 req/min per IP, 50 req/min global) and the return format, providing 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: two sentences plus a return format. It is front-loaded with the purpose, then rate limits, then output. No superfluous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers rate limits and return format, and the schema documents all parameters. Minor missing details like time range inclusivity or sorting order, but overall fairly complete given the context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so each parameter is already documented. The description does not add extra meaning to parameters beyond what's in the schema, so a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Fetch device logs from Elasticsearch', which is a specific verb-resource pair. It distinguishes from sibling tools like devices_get (device info), devices_get_events (events), and devices_get_telemetry (telemetry).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides rate limits that inform usage but does not explicitly state when to use this tool versus alternatives or when not to use it. Given the many sibling tools, more guidance would be beneficial.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations (readOnlyHint, idempotentHint, destructiveHint) already indicate safe read operations. The description adds specific rate-limiting details (60 requests/minute steady, burst 10/10s) and return structure, which are valuable beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: first defines purpose, second adds rate constraints, third gives return format. No redundancy, front-loaded, and each sentence serves a clear function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With simple parameters, 100% schema coverage, output schema implied by return format, and annotations covering safety, the description suffices for correct invocation. Lacks guidance on tool selection among siblings, but overall informative for a telemetry retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters with descriptions. The description mentions rate limiting per device and the return structure, which subtly relates to device_id and time_period, but does not add explicit semantic details 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.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves aggregated hardware metrics (CPU, memory, disk, GPU) with a specific verb ('Get') and resource ('aggregated hardware metrics'). It is distinct from sibling tools like devices_get (general info), devices_get_events, and devices_get_logs, though it does not explicitly compare to them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 such as devices_get, devices_get_events, or devices_get_logs. It mentions rate limits but does not provide criteria for selection or exclusions, leaving the agent to infer usage from the description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark it as read-only, idempotent, non-destructive. The description adds crucial context: the 5-minute heartbeat status rule, required scope, and the 403 error for device-scoped keys. This goes 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: action first, then return fields, then status explanation, then auth. Every sentence provides value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters and an output schema likely covering return types, the description fully covers what the tool does, what it returns, and key constraints (auth, status). It is complete for a list tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, so the description need not explain them. Baseline for zero parameters is 4, and the description does not add unnecessary parameter info.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it lists devices in the workspace and details the returned fields (id, name, status, etc.), distinguishing it from sibling tools like devices_get (single device) and devices_create (create). The verb 'list' and resource 'devices' are specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use (to get all devices) and includes authentication requirements (device:read scope, device-scoped keys get 403). However, it does not explicitly contrast with other list-like tools or state 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent behavior. The description adds that it returns the Stream object and a 404 error if missing, providing useful 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with no redundant information. Front-loaded with the core action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given its simplicity and the presence of an output schema, the description covers all necessary information: what it does, what it returns, and error behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear parameter descriptions. The description adds no further semantic information beyond what is already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Get a single stream by id', using a specific verb and resource. It clearly distinguishes from the sibling tool devices_streams_list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The purpose is clear, but it lacks context for when not to use or any prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by noting that credential-bearing fields are redacted from the source field, providing useful 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences that are front-loaded: first states the action, then notes the return type and redaction behavior. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, the description does not need to detail return values. It covers the redaction behavior and lists streams adequately. However, it could explicitly state that it returns all streams on the device, but it still is fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a single parameter described as 'Device id'. The description does not add additional meaning for this parameter, so it meets the baseline of 3 but does not exceed it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'List streams configured on the device' which clearly identifies the action and resource. However, it does not differentiate from sibling devices_streams_get (which likely gets a specific stream), so a score of 4 is appropriate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like devices_streams_get. It lacks explicit when-to-use or when-not-to-use information, resulting in a low score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description covers the update's destructive nature (shallow merge replaces top-level fields), validation against schema, and config history appending. It provides rich behavioral context beyond the annotations (destructiveHint=true), including how nested changes should be handled.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each serving a purpose: stating the action, explaining the merge behavior and scope, and noting validation and history. It is concise, front-loaded, and contains no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having an output schema (not shown), the description covers the update operation's key aspects: purpose, required scope, behavior (no deep merge), validation, and historical tracking. It is complete for an agent to correctly invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the description adds substantial meaning for the 'config' parameter by explaining the shallow merge effect and the correct approach for nested updates. For 'device_id', it adds no extra meaning but the schema description is sufficient. Overall, it significantly enhances parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it updates the device's runtime configuration with a specific verb and resource, and distinguishes from sibling tools like devices_get_config and devices_get_config_history by explaining the update behavior.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions the required scope ('device:update') and explains the merge behavior, implying when to use it. It also provides guidance on updating nested values by fetching first. However, it does not explicitly state when to avoid this tool or name alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds valuable context beyond the annotations. Annotations indicate read-only, open-world, idempotent, and non-destructive behavior, but the description explains that the tool returns a pre-built upload URL and instructions, and that the caller must perform the actual upload using curl since the MCP server cannot access local files. This clarifies the tool's operational behavior and limitations, though it doesn't detail rate limits or auth needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured, with three sentences that each serve a distinct purpose: stating the tool's function, explaining the upload process, and providing usage alternatives. There is no wasted text, and information is front-loaded, making it easy to understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, rich annotations (read-only, open-world, idempotent, non-destructive), 100% schema description coverage, and the presence of an output schema, the description is complete. It covers the tool's purpose, behavioral context, and usage guidelines without needing to explain parameters or return values, which are handled by structured fields.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description does not mention any input parameters, focusing instead on the tool's purpose and usage. However, the input schema has 100% description coverage, with all parameters well-documented (e.g., 'project_id' as a project slug, 'image_name' as a filename). Since the schema provides comprehensive parameter details, the description's lack of parameter information is acceptable, resulting in a baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get an upload URL to upload a single image to a project.' It specifies the verb ('Get'), resource ('upload URL'), and scope ('single image'), and distinguishes it from sibling tools like 'images_prepare_upload_zip' for bulk uploads and 'annotations_save' for adding annotations. This is specific and avoids tautology.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool versus alternatives. It states: 'This endpoint uploads images only. To add annotations, call annotations_save with the image ID from the upload response. For bulk uploads with annotations, use images_prepare_upload_zip.' This clearly defines the tool's scope and directs users to other 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate this is a non-readOnly, non-destructive operation, which the description aligns with by describing a preparation step (not the actual upload). The description adds valuable behavioral context beyond annotations: it specifies file size/quantity limits (2 GB/10k files), the two-step process (signed URL + polling), and URL expiration (1 hour). However, it doesn't mention rate limits, authentication needs, or error handling, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by key constraints and required steps. Every sentence adds essential information: supported formats, limits, return values, and caller responsibilities. There's no redundant or vague language, and it's structured logically for quick comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a two-step upload process, the description is complete: it explains the tool's purpose, constraints, return values (signed URL and task ID), and required follow-up actions. With an output schema present (implied by 'Has output schema: true'), it doesn't need to detail return values further. The annotations cover safety aspects, and the description fills in behavioral gaps appropriately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents all 4 parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'split' or 'tag_names' interact with the zip content). This meets the baseline of 3, as the schema carries the full burden of parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Prepare a zip upload') and resource ('images and annotations to a project'), distinguishing it from sibling tools like 'images_prepare_upload' (which likely handles individual files) and 'images_upload_zip_status' (which polls status). It explicitly mentions supported annotation formats (COCO, YOLO, Pascal VOC, classification-by-folder), making the purpose highly specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: for bulk uploads of images with annotations in zip format (up to 2 GB/10k files). It also outlines the required follow-up steps (PUT to signed URL, poll task status) and mentions the signed URL expiration (1 hour), which implicitly suggests not using it for small or immediate uploads without polling capability.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds minimal behavioral context beyond this, but doesn't contradict annotations. It implies a search operation which aligns with the read-only nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with zero wasted words. It's appropriately sized and front-loaded, communicating the essential purpose efficiently without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the rich annotations (covering safety and idempotency), 100% schema description coverage, and existence of an output schema, the description provides adequate context for a search tool. However, it lacks guidance on when to use this versus sibling search tools, which is a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, all 13 parameters are well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline but doesn't enhance understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Search for images') and scope ('inside a project'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'universe_search' or 'images_prepare_upload', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'universe_search' or other image-related tools. There's no mention of prerequisites, constraints, or comparative use cases with sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide readOnlyHint=true, idempotentHint=true, etc., covering safety traits. The description adds valuable context beyond annotations: it details return values (status, progress, result with counts like duplicates) and hints at asynchronous task behavior (statuses like 'running'), which helps the agent understand operational semantics without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by specific return details. Both sentences earn their place by providing essential information without redundancy, making it appropriately sized and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter), rich annotations (readOnly, idempotent, etc.), and presence of an output schema, the description is complete enough. It covers purpose, usage hint, and return semantics, aligning well with structured data without needing to explain basic behaviors or output details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the parameter 'task_id' fully documented in the schema. The description adds minimal semantics by referencing the source ('from images_prepare_upload_zip response'), but this is marginal beyond the schema. Baseline 3 is appropriate as the schema carries most of the burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Check') and resource ('status of a zip upload task'), making the purpose specific. It distinguishes from sibling tools like 'images_prepare_upload_zip' (which initiates uploads) and 'images_search' (which searches images), avoiding tautology by not just repeating the name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by referencing 'task_id from images_prepare_upload_zip response', guiding when to use this tool after that sibling. However, it lacks explicit when-not-to-use statements or alternatives, such as whether to use other status-checking tools or retry mechanisms.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds valuable behavioral context beyond annotations: it explains the proactive nature of calling ('call this proactively'), specifies the types of issues to report, and mentions the return value ('confirmation that feedback was recorded'). Annotations cover safety aspects (non-destructive, non-idempotent, open-world), but the description provides practical usage context that enhances transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly structured and concise: two sentences that efficiently communicate purpose, usage guidelines, and behavioral outcomes. Every sentence earns its place with no redundant information. It's front-loaded with the core purpose followed by specific usage instructions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's straightforward purpose, comprehensive annotations, 100% schema coverage, and existence of an output schema, the description provides complete contextual information. It explains what the tool does, when to use it, and what to expect in return, which is sufficient for this type of feedback submission tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema already fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3. The description focuses on usage context rather than parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's purpose as reporting bugs, missing features, UX friction, or documentation issues. It uses specific verbs ('Report') and resources ('feedback'), and clearly distinguishes itself from all sibling tools which are operational Roboflow tools, while this is a meta-feedback mechanism.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'when you encounter errors using Roboflow tools, when the user expresses frustration, when a tool is missing for the task at hand, or when a parameter is poorly documented.' It clearly defines the triggering conditions without needing to reference alternatives since this is the only feedback tool among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is known to be non-destructive. The description adds further context by detailing what the tool returns (a summary of metrics and an app_url), which goes beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description consists of two concise sentences. The first sentence states the core purpose, and the second elaborates on the return values. Every word contributes meaning without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, full schema coverage, rich annotations, and an output schema), the description provides all essential information: what it does and what it returns. It is complete for a retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with one required parameter 'eval_id' described as 'Model evaluation id.' The description does not add any additional meaning or format details beyond the schema, so the baseline score of 3 is justified.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get'), the resource ('a single model evaluation'), and the scope ('top-level summary'). It distinguishes from sibling tools like model_evals_list and model_evals_get_* by specifying that it returns a summary and an app_url deep link.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 like model_evals_get_map_results or model_evals_get_performance_by_class. There is no explicit statement of use cases, prerequisites, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint. The description adds value by detailing the output structure (perThreshold, optimalThreshold, etc.), which helps the agent understand the response format without accessing the output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences that front-load the main action and then detail the return structure. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides a solid overview of the tool's output, noting splits, thresholds, and per-class sweeps. While it could mention the presence of an output schema, the detail is sufficient for a single-parameter read-only tool with good annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage for the single parameter eval_id with a basic description. The tool description does not add further semantic detail beyond the schema, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves a precision/recall/F1 confidence sweep for an eval, which is a specific resource and action. This distinguishes it from sibling tools like get_confusion_matrix or get_performance_by_class.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool returns but does not provide guidance on when to use it versus alternative eval analysis tools (e.g., confusion matrix, vector analysis). 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare this as safe (readOnly, idempotent, not destructive). The description adds value by detailing the return structure (split, confidenceThreshold, classes, matrix) and how matrix indices are interpreted, which goes beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences: the first states the purpose, and the second explains the return format. There is no unnecessary information or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the moderate complexity (3 parameters, output schema present), the description covers the essential behavior. It explains the return structure in detail, though it omits error cases or caveats. Still, it is sufficient for an API-focused tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all parameters are already documented. The tool description does not add any additional meaning or usage hints for the parameters beyond what is in the schema, meeting the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves the confusion matrix for an evaluation. The verb 'Get' and resource 'confusion matrix' are specific, and the sibling tools (e.g., model_evals_get, model_evals_get_performance_by_class) suggest this is a distinct metric retrieval tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives like model_evals_get or model_evals_get_performance_by_class. The purpose is implied by the name, but no usage context 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds pagination details and return format, which supplements the annotations without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: first states purpose, second details return format, third is absent. Front-loaded and efficient with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema and rich annotations, the description covers the return format and pagination. It is complete for a read-only evaluation stats tool with clear parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All parameters have descriptions in the schema (100% coverage). The description mentions return fields but adds minimal extra meaning beyond the schema, meeting the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Get per-image prediction stats for an eval (paginated).' It specifies the verb, resource, and scope, and distinguishes itself from sibling tools like model_evals_get_confidence_sweep and model_evals_get_confusion_matrix.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 other model_evals_get_* tools. The description only declares what it does, without context on selection or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds behavioral detail by specifying the exact return structure (e.g., splits, map50/75, byObjectSize, perClass), which is valuable 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (3 sentences) and front-loaded with the primary purpose. The code block for return structure is informative, though slightly verbose for a tool description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema and the detailed return description, the tool is fully documented. All key aspects (purpose, parameters, behavior, output) are covered, making it complete for an information-retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the baseline is 3. The description does not add extra meaning to the eval_id parameter beyond what the schema already provides ('Model evaluation id').

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (Get) and the resource (per-split mAP results for an eval), specifying exactly what data is returned, which distinguishes it from sibling tools like model_evals_get_confidence_sweep or model_evals_get_image_predictions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies it's for obtaining mAP metrics per split, but it does not explicitly state when to use this tool versus other model_evals_* tools, leaving it to the agent to infer from the specialization.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds the return structure but no additional behavioral traits (e.g., auth needs, rate limits, side effects). It does not contradict annotations (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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first states purpose, second gives return format. No extraneous text. Front-loaded and efficient. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (two parameters, one required) and presence of an output schema, the description covers the essentials. It explains what is returned and the default split behavior. Could add more context about when classes appear or edge cases, but it is complete for most use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and both parameters have descriptive schema text. The description repeats the split default and rejection of 'all' already in the schema, adding no new semantics. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Get per-class performance metrics for a single split' with a specific verb and resource. It also provides the return format. However, it does not explicitly differentiate from sibling tools like model_evals_get_map_results or model_evals_get_confidence_sweep, though the name and description imply the scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The description does not mention when-not-to-use, prerequisites, or exclusions. Sibling tools exist for other metrics (e.g., confusion matrix, recommendations), but the description offers no direction on choosing among them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true; description adds behavioral context by stating it returns {generated: false} until the job runs and then includes recommendations. No auth or rate limit details are disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences covering purpose, return format, and behavioral nuance. No extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Output schema exists (not shown here), so description explains return shape. With annotations, it's fairly complete, though could mention if recommendations are always available or prerequisites.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% description coverage for eval_id. The description does not add additional meaning beyond what the schema provides, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets LLM-generated recommendations for an eval, distinguishing it from sibling tools like model_evals_get which likely retrieves the eval itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied (when you want recommendations after an eval), but no explicit when-not or alternatives are provided. The description lacks guidance on prerequisites or context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint; description adds value by detailing the return structure and cluster attributes 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences: first states purpose, second shows return format, third explains utility. No wasted words and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, the description sufficiently covers purpose, return structure, and use case. No gaps for this moderately complex tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage; the description does not add extra meaning beyond what is already in the schema for eval_id and confidence.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool retrieves UMAP and HDBSCAN clustering of image embeddings for an eval, distinguishing it from other model_evals_get_* tools like confusion matrix or confidence sweep.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions it is useful for finding poor-performing image pockets, implying when to use, but lacks explicit guidance on when not to use or alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds constraints on filter combinations and the return structure, providing 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two focused sentences plus a constraint note. No fluff, front-loaded with purpose, and uses clear structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With output schema present, description doesn't need to detail return values. It covers filtering constraints, sibling differentiation, and basic behavior. Complete for a list tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions cover all 5 parameters (100% coverage). Description adds the rule that at most one of project/version/model may be set, returning 400 otherwise, which is crucial usage info not in schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it lists model evaluations in the current workspace, specifying the returned fields. Distinguishes from sibling model_evals_get by directing to it for detailed summary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use model_evals_get instead for detailed summaries on a specific eval. Also warns that at most one filter out of project/version/model can be set, avoiding errors.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate this is a read-only, non-destructive, idempotent operation with open-world data, so the description doesn't need to repeat these safety traits. It adds value by specifying that it retrieves 'details' for a 'trained model', which implies comprehensive metadata beyond basic info, but doesn't elaborate on what those details include or any behavioral nuances like error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the annotations cover key behavioral aspects (read-only, non-destructive, etc.) and an output schema exists (so return values are documented elsewhere), the description is reasonably complete for a simple lookup tool. However, it could be more helpful by clarifying the scope of 'details' or distinguishing it from sibling tools, especially since the context includes many model-related alternatives.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema fully documents the single required parameter 'model_id' as a 'Model id (URL slug)'. The description doesn't add any parameter-specific information beyond implying that 'model_id' corresponds to a 'trained model', which is already inferred from the schema. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get details') and resource ('trained model'), making the purpose immediately understandable. However, it doesn't differentiate this from sibling tools like 'models_get_training_status' or 'models_list', which also retrieve model-related information but with different scopes or details.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'models_get' over 'models_list' (for listing all models) or 'models_get_training_status' (for status details), nor does it specify prerequisites like needing a specific model ID.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare this as read-only, non-destructive, idempotent, and open-world, covering the core safety profile. The description adds valuable behavioral context by specifying what information is returned (training status, progress, metrics, dashboard URL) and that it's for monitoring ongoing training jobs, which enhances understanding beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly structured: first sentence states the purpose, second provides usage guidance, third details the return values. Every sentence earns its place with zero waste, and it's front-loaded with the most important information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that annotations cover safety aspects, schema covers parameters fully, and an output schema exists (so return values don't need explanation in description), the description provides exactly what's needed: clear purpose, usage guidance, and context about what information is returned. It's complete for this tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both parameters (project_id and version_number) fully documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline of 3 for adequate coverage without extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get the training progress and metrics') and resource ('for a dataset version'), distinguishing it from sibling tools like models_train (which starts training) and models_get (which likely retrieves model metadata). It precisely identifies what this tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: 'Use this tool to check on a training job started with models_train.' This provides clear context and directly names the alternative tool (models_train) for comparison, giving perfect guidance on when this tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds valuable behavioral context beyond annotations: it specifies the output format ('Returns JSON predictions only'), which annotations don't cover. Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, so the agent knows this is a safe, repeatable query operation. The description complements this by clarifying the output type, earning a high score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded: the first sentence states the core purpose, and the second provides critical usage guidance. Every sentence earns its place with no wasted words, making it highly efficient for an AI agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (inference with multiple parameters), rich annotations (covering safety and behavior), and the presence of an output schema, the description is complete enough. It clarifies the purpose, distinguishes from siblings, and specifies output format, addressing key contextual gaps without redundancy.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description does not add any parameter-specific information beyond what the input schema provides. However, with schema description coverage at 100%, the baseline score is 3. The schema already fully documents all parameters (image, overlap, model_id, confidence, project_type), so no additional compensation is needed from the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Run hosted inference on an image using a trained model') and distinguishes it from a sibling tool ('For visualized/annotated images, use workflow_specs_run with a visualization block instead'). It identifies both the verb (run inference) and resource (image with trained model) with precision.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool versus an alternative: 'For visualized/annotated images, use workflow_specs_run with a visualization block instead.' This directly addresses the key decision point between this tool and its sibling, offering clear exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate this is a read-only, non-destructive, idempotent operation with open-world data. The description adds value by clarifying the scope ('trained models associated with a project'), which isn't covered by annotations. It doesn't contradict annotations—listing is consistent with read-only behavior—and provides useful context about what data is returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence earns its place by specifying scope ('trained models') and context ('associated with a project').

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one required parameter), rich annotations (covering safety and behavior), and presence of an output schema (which handles return values), the description is reasonably complete. It covers the essential 'what' and 'scope', though it could be enhanced with more usage guidance. For a simple list operation with good structured data, this is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the single parameter 'project_id' fully documented in the schema. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., no examples or constraints). Baseline is 3 since the schema handles parameter documentation adequately, and the description doesn't compensate with extra insights.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 ('trained models associated with a project'), making the purpose immediately understandable. It distinguishes from siblings like 'models_get' (singular) and 'models_train' (creation), though it doesn't explicitly name alternatives. The description avoids tautology by specifying scope beyond just the name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by mentioning 'associated with a project', suggesting this tool is for retrieving models within a specific project. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'models_get' (for single model details) or 'projects_list' (for listing projects). 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate idempotency and non-destructiveness. The description adds critical behavioral context: triggering TRT compilation and the server error for non-NAS models. No contradictions; description enriches understanding 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, straight to the point, with no extraneous information. Every sentence adds value: first defines action and scope, second explains limitation and side effect. Highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 parameters, 1 required) and the presence of an output schema, the description is fully adequate. It covers purpose, constraints, and side effects, leaving no major gaps for an agent to understand correct usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and each parameter has a clear description in the schema. The tool description does not add significant new semantic meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Star or unstar a NAS-trained model', identifying the verb and resource. It distinguishes from sibling tools by specifying NAS-only scope and triggering TRT compilation, making the tool's unique role evident.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: 'NAS-only by design — the server rejects non-NAS modelTypes' tells exactly when to use and when to avoid. It also explains the effect of starring, but does not explicitly contrast with alternatives (though none exist). Leaves little ambiguity about applicability.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds valuable behavioral context beyond what annotations provide: it explains the asynchronous nature ('returns immediately', 'training runs in the background'), specifies the prerequisite checks needed, and mentions the return includes a progress monitoring URL. Annotations already indicate this is not read-only, not idempotent, and not destructive, but the description enriches this with operational details without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with clear sectioning: it states the core purpose upfront, provides two IMPORTANT prerequisite warnings, explains the asynchronous behavior, and describes the return value. Every sentence serves a distinct purpose with zero redundancy, making it easy to scan and understand.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (asynchronous training operation with prerequisites), the description provides complete contextual guidance. It covers prerequisites, behavioral expectations, and return information. With both comprehensive annotations and an output schema available, the description appropriately focuses on operational context rather than repeating structured data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema already documents all 7 parameters thoroughly. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain default behaviors for optional parameters like speed or epochs). This meets the baseline expectation when schema coverage is complete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Start training a model') on a specific resource ('on a dataset version'). It distinguishes this tool from siblings like models_get_training_status (monitoring) and versions_generate (prerequisite). The verb 'start' accurately reflects the asynchronous nature of the operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit, imperative guidance on when to use this tool: it names two prerequisite tools (versions_generate and versions_get) that must be called first, specifies the conditions that must be met (dataset version must exist, must have train/validation images), and distinguishes this from monitoring tools by noting it 'returns immediately' while training runs in background.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate this is a write operation (readOnlyHint=false) that's non-destructive and non-idempotent. The description adds minimal behavioral context beyond this - it mentions the return values (which the output schema would cover) but doesn't provide additional behavioral details like authentication requirements, rate limits, or what happens if creation fails.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two focused sentences: one stating the action and one describing the return values. Every word earns its place, and the information is front-loaded with the primary purpose stated first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a creation tool with comprehensive annotations and a complete input schema, the description provides adequate context. The presence of an output schema means the description doesn't need to detail return values. However, it could better address when to use this tool versus alternatives given the sibling tools available.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema already fully documents all 5 parameters. The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate coverage when the schema does all the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create a new computer vision project') and the resource ('computer vision project'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'workflows_create' or 'annotation_jobs_create' which might also create related resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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. There's no mention of prerequisites, when this should be used instead of other creation tools like 'workflows_create', or what context should trigger its use. The agent must infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description fully discloses the async nature, the need to poll for completion, the expected wait time (up to 30 seconds), and the minimum response fields. This adds significant value beyond the annotations, which only provide hints about readOnly, openWorld, idempotent, and destructive behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (6 sentences), front-loaded with the main action, and every sentence adds necessary information without redundancy. It is well-structured and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (async, polling needed, two parameters), the description covers all critical aspects: how to invoke it, what the response contains, how to check completion, and approximate timing. The output schema is mentioned, so further detail is not needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for both parameters, and the description reinforces that either url or source_project should be provided, with an example URL. While the schema already documents the parameters, the description adds context about their relationship and usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool enqueues an async fork of a public Universe project into the user's workspace, specifying inputs (url or source_project) and the response shape, which distinguishes it from sibling tools like projects_create or projects_get.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context: it tells the agent to provide either a Universe url or source_project, and explains that the response includes a taskId for polling via async_tasks_get. It does not explicitly state when not to use it or list alternatives, but the guidance is strong enough for correct selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide comprehensive behavioral hints (readOnly, openWorld, idempotent, non-destructive). The description adds context about what information is returned (workspace, project info, versions, classes) which helps the agent understand the scope of data retrieved. However, it doesn't mention rate limits, authentication requirements, or pagination behavior beyond what annotations cover.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two clear sentences that efficiently convey the tool's purpose and return value. The first sentence states what the tool does, the second clarifies the scope of returned data. No redundant information or unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has comprehensive annotations, 100% schema coverage, and an output schema exists, the description provides adequate context. It clearly explains this is a retrieval operation for detailed project information. The main gap is lack of explicit guidance on when to use versus sibling tools like 'projects_list'.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with the single parameter 'project_id' fully documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Get detailed info about a project' with specific components mentioned (versions, classes, splits, trained models). It distinguishes from siblings like 'projects_list' (which likely lists projects without details) and 'projects_create' (which creates rather than retrieves). However, it doesn't explicitly contrast with 'projects_list' in the description text itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when detailed project information is needed, but doesn't explicitly state when to use this tool versus alternatives like 'projects_list' (for listing projects) or 'versions_get' (for specific version details). No explicit 'when-not' guidance or prerequisite context is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds context about potential multi-minute latency and the nature of returned metrics, which is useful 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: purpose, caveat, then return items. Every sentence adds value, no fluff, well front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, the description adequately covers purpose, timing, and return contents. It is complete for a 2-param tool with good annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage, with both parameters explained. The description only adds a timing remark for regenerate, which does not significantly enhance semantic understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Get' and resource 'dataset health check', and distinguishes from siblings as no other tool mentions health checks. It clearly states what it returns (class balance, image quality, etc.).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a timing caveat for first use or with regenerate=True, implying when to expect delays. However, it does not explicitly state when to use this tool vs alternatives, nor does it mention 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide comprehensive behavioral hints (readOnlyHint: true, openWorldHint: true, idempotentHint: true, destructiveHint: false), so the description's burden is lower. It adds some value by specifying the return format ('list of projects with id, name, type, image count, and more'), but doesn't disclose additional behavioral traits like pagination, rate limits, or authentication requirements beyond the API key mention.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and well-structured with just two sentences: the first states the purpose, and the second describes the return format. Every word earns its place, and it's front-loaded with the core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, rich annotations, and an output schema exists), the description is reasonably complete. It covers the purpose and return format, though it could benefit from usage guidelines relative to siblings. The output schema likely details the return values, so the description doesn't need to fully explain them.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0 parameters and 100% schema description coverage, the schema fully documents the lack of inputs. The description appropriately doesn't discuss parameters, which is correct for this tool, earning a high baseline score. No additional parameter semantics are needed or provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List all projects') and resource ('projects in the workspace associated with the API key'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this from sibling tools like 'projects_get' or 'projects_create', which would be needed for a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'projects_get' (for a single project) or 'projects_create' (for creating new projects). It also doesn't mention any prerequisites, context, or exclusions for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark destructiveHint=true and idempotentHint=true. The description adds useful behavioral context: it works for any architecture and for NAS runs the same call cancels mining or training phases. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with primary action, efficient coverage of applicability and NAS details. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given output schema exists and parameters are well-documented, the description covers the core action and edge cases (NAS). Missing explicit differentiation from sibling 'trainings_stop', but overall sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage with clear parameter descriptions. The description adds no additional parameter semantics beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Cancel' and resource 'training run', with specificity 'in-flight'. It distinguishes from siblings like 'trainings_stop' by noting it works for any architecture and handles NAS phases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use (cancelling in-flight runs) and adds NAS-specific nuance. However, it does not explicitly compare to sibling 'trainings_stop' or state when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations (readOnlyHint, idempotentHint, destructiveHint) already indicate a safe read operation. The description adds behavioral detail about conditional returns (NAS vs non-NAS), though it doesn't elaborate on error states. 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the purpose. It uses a clear structure with examples for NAS and non-NAS, though the code block makes it slightly longer than necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (different return structures for NAS vs non-NAS) and the presence of an output schema, the description adequately covers what the agent needs to know. It explains when to use the tool and what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with clear descriptions for both parameters. The description does not add extra meaning beyond the schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns 'Run-level training results bundle' and distinguishes between NAS sweeps (returns many models) and non-NAS trainings (minimal bundle). This ensures a specific verb+resource match and differentiates from siblings like models_list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use this for the run-level dashboard. For full per-model metadata, call models_list with the returned modelGroup.' This provides clear context on when to use this tool versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description discloses that the stop is graceful (finishes current phase), adding context beyond annotations which mark it as destructive but not contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, first gives the action, second contrasts with a sibling. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Has output schema, only two required parameters, and the description covers purpose and behavioral nuance adequately for a straightforward tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already provides 100% coverage for parameters (project_id and version_number), and the description adds minimal extra meaning beyond mentioning 'in-flight training run'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool requests an early stop on an in-flight training run, and distinguishes it from cancel by explaining it finishes the current phase gracefully.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.