scoring

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

Annotations already indicate read-only and idempotent behavior. The description adds useful context by naming the HTTP method and endpoint (GET /v1/limits) and enumerating the response fields (plan limits, trial quota, upgrade state), which goes beyond the name alone.

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, front-loaded sentence with no wasted words. It states the verb, resource, and endpoint efficiently.

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 parameterless, read-only tool with no output schema, the description adequately covers what the tool does and what it returns. There is no ambiguity or missing information for an agent to invoke it 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?

The tool has zero parameters, so the schema is trivially complete. The description does not need to add parameter details, and the baseline of 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 identifies the action ('Retrieve') and the specific resource ('plan limits, trial quota, and upgrade state') with an explicit endpoint. This distinguishes it from sibling tools like get_usage, which focuses on usage data.

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 by specifying the exact data returned, making it obvious when this tool is appropriate. It does not mention alternatives or exclusions, but the resource scope is unambiguous for a simple getter.

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 idempotentHint=true, so the tool's safety profile is known. The description adds the HTTP method and endpoint, but does not go beyond that to explain behaviors like what happens if 'month' is omitted or what the response contains.

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?

A single sentence that is front-loaded with the action and resource, followed by the endpoint. Zero wasted words or unnecessary detail.

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-only tool with one optional parameter, the schema and annotations cover most needs. However, the description does not specify the default behavior when 'month' is omitted, leaving a minor gap in 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 has 100% description coverage, with the 'month' parameter described as 'Optional billing month in YYYY-MM format.' The tool description does not add any additional parameter semantics, so it provides no extra value 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 the specific verb 'Retrieve' and names the resource 'monthly scoring call usage', along with the explicit HTTP method and endpoint (GET /v1/usage). This clearly distinguishes it from sibling tools like get_limits or get_workflow_schema.

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 phrase 'monthly scoring call usage' provides clear context for when to use the tool, implying it is for retrieving usage metrics by month. No explicit exclusions or alternatives are mentioned, but the context is sufficient.

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, and the description adds value by enumerating exactly what the response contains (required fields, optional fields, enums, example payload). It also mentions the GET method, reinforcing the safe 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, front-loaded sentence that conveys all essential information without any filler. Every word 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 tool with one parameter and clear annotations, the description fully explains what the tool returns and its scope. No output schema exists, but the description adequately compensates by listing response contents.

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 provides 100% coverage for the single parameter, including a description and examples. The description does not add additional parameter semantics, so the baseline 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 the tool fetches required fields, optional fields, enums, and an example payload for a workflow. This specific verb+resource combination distinguishes it from sibling tools like list_workflows or validate_workflow_payload.

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 by specifying the HTTP method and resource path, but it does not explicitly state when to use this tool over alternatives. Usage is implied rather than directly guided.

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 annotations already declare readOnlyHint=true and idempotentHint=true, establishing a safe read operation. The description adds the GET endpoint and path, which is useful context, but it does not describe the return format, pagination, or any other behavioral traits. Given the annotation coverage, a score of 3 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, compact sentence that clearly conveys the tool's function without any superfluous detail. It earns a perfect score for conciseness and front-loading.

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 tool is simple with no parameters, and the description explains the core functionality and endpoint. While there is no output schema, the description explicitly states what is listed, making it sufficiently complete for the 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?

The tool takes zero parameters, and the schema is an empty object (100% coverage). With no parameters, the baseline is 4, and the description does not need to explain parameter semantics further.

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 supported workflows and current model versions, with the HTTP endpoint via GET /v1/workflows. It uses a specific verb and resource, distinguishing it from sibling tools like get_workflow_schema and score_workflow.

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 discovering available workflows and model versions, but does not explicitly state when to use it over alternatives or provide any exclusions. Since the tool's purpose is straightforward, the context implies usage but lacks direct 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 and idempotentHint, and the description adds the sequential processing behavior and the batch limit. This provides additional contextual value 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, front-loaded sentence that delivers the key facts (action, scope, limit, endpoint) with no 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?

Given the simplicity of the tool, the description provides the endpoint, item limit, and sequential behavior, while the schema explains the parameters. It lacks an explicit return-value description, but this is not critical for a batch scoring tool with no output schema.

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 both parameters (items and continue_on_error) with clear descriptions, and the description itself adds no extra parameter-level detail. With 100% coverage, 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 the verb 'Score', the resource 'workflow items', and the specific batch scope 'up to 25 ... sequentially in one request' with the endpoint. This distinguishes it from the sibling tool 'score_workflow' which likely handles single items.

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 conveys usage for batch scoring of up to 25 items, which is clear context. However, it does not explicitly mention when not to use it or point to the single-item alternative 'score_workflow'.

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 idempotentHint=true, covering the safety profile. The description adds the HTTP method 'POST' and domain context but does not disclose auth requirements, response behavior, or any side effects 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 a single, front-loaded sentence with no wasted words. It clearly states the action, resource, and endpoint in 8 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 no output schema, the description should explain what the tool returns or how scoring works. It only gives a terse endpoint reference, omitting expected output, prerequisites, and practical usage context despite the nested payload 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 coverage is 100%, with descriptions for all three parameters (workflow, payload, options). The tool description adds no extra parameter context, so the schema already carries the semantic 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 uses a specific verb 'score' with a clear resource 'Sentinel healthcare workflows' and includes the HTTP endpoint. It distinguishes from siblings like score_batch (batch) and validate_workflow_payload (validation).

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 given on when to use this tool versus alternatives such as score_batch or get_workflow_schema. The description simply states the action without context 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 are all false, providing no safety or behavioral hints. The description simply says 'Submit' without disclosing effects such as whether feedback can be overwritten, duplicated, or what happens on invalid payloads.

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, direct sentence with a clear verb-first structure and no filler. It is appropriately concise for a simple tool.

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 tool with one parameter and no output schema, the description covers the core purpose but omits expected response behavior, error conditions, or the typical workflow context. It is acceptable but leaves gaps.

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 already documents the single 'feedback' parameter well with a description referencing 'prior Sentinel scoring event.' The tool description adds the phrase 'claims outcome' but does not meaningfully enrich the 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 clearly identifies the action ('Submit'), the resource ('structured claims outcome feedback'), and the transport method/endpoint ('POST /v1/feedback'). This is specific and distinct from sibling scoring tools like score_batch and score_workflow.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention that feedback is intended for prior scoring events, nor does it explain any prerequisites or exclusions, leaving the agent to infer 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 and idempotentHint, lowering the burden. The description adds valuable behavioral context: it does not consume a scoring call and it normalizes the payload in addition to validating. It does not describe response format or errors, but the safety profile is covered and the added behavior is meaningful.

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?

One sentence, front-loaded with the action and resource, and no filler. Every part of the description contributes to understanding the tool's purpose and distinguishing it from scoring tools.

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 clearly states the tool's purpose and endpoint, and annotations cover the read-only/idempotent nature. However, with no output schema, the description should explain return values or expected response shape; it does not. It also omits error behavior, leaving a moderate gap for an otherwise simple 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 clear descriptions for both 'workflow' and 'payload', so the baseline is 3. The description does not add parameter-level details beyond the endpoint path, which slightly reinforces how workflow and payload are used but does not meaningfully exceed 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 ('Validate and normalize') tied to a clear resource ('workflow payload') and explicitly names the endpoint. It distinguishes itself from siblings by adding 'without consuming a scoring call', making it clear this is not score_workflow or score_batch.

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 phrase 'without consuming a scoring call' clearly implies this tool is for pre-scoring validation and avoids the side effect of scoring. It lacks explicit 'when not to use' or named alternatives, but the intended context is strong enough to guide tool selection.

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