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

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true, which cover safety clearly. The description adds that it checks 'auth status' and 'account info', which is consistent but does not disclose additional behavioral details like side effects or required permissions.

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 short but includes repetitive synonyms (e.g., auth status mentioned twice) and mixes languages, which adds noise. It is not structured or front-loaded in an optimal way, but it is functional.

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 two optional parameters and no output schema, the description is minimally complete. It covers the core purpose but does not explain return values or when to use specific parameters. For a simple tool, it suffices, but lacks detail.

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 parameters (show_all_accounts, format). The description does not add any extra meaning or details 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 tool checks GCP authentication status and account info, using both Korean and English terms. It distinguishes itself from sibling tools like billing or logs by focusing on auth status. However, the multi-term format with pipes is somewhat messy.

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, nor does it mention any context or prerequisites. An agent would not know if this is the right tool for specific auth-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 already mark the tool as readOnly, idempotent, and non-destructive. The description adds no additional behavioral context such as data freshness, authentication requirements, or rate limits. It merely restates the purpose.

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

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

The description is concise (single line with keywords and sentence) and front-loaded with searchable terms. However, the pipe-separated keyword list could be cleaner, but overall it is brief and to the point.

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 the core purpose but lacks details about the output format or the scope of billing data (e.g., time range, cost breakdown). With no output schema, agents may need more context to interpret results.

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% for both parameters (project_id and format). The tool description does not add any meaning 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 tool retrieves GCP billing and cost information, using both Korean and English keywords. The title from annotations reinforces this. It is distinct from sibling tools which handle auth, logs, secrets, 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?

No guidance on when to use this tool versus alternatives. The description does not provide any context on prerequisites, expected input, or comparison to other billing-related tools (though none exist 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, idempotentHint true, etc. The description adds minimal context beyond the source (GCP Cloud Logging). It does not disclose output format, pagination, or error handling.

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

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

Very short (two lines) but includes unnecessary pipe-separated tags. It is concise and front-loaded with the action, but structure could be cleaner.

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?

No output schema exists, and the description does not explain return values or pagination. With rich annotations and schema, it is adequate but leaves some gaps for a read 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% coverage with good param descriptions. The tool description does not add extra meaning beyond what the schema provides, 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 it queries logs from GCP Cloud Logging, and the tags ('로그 조회', '에러 확인') indicate its purpose for error checking. It is distinguished from sibling 'gcp_run_logs' which is likely more specific to Cloud Run 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?

Tags imply use cases (error checking, general log querying) but no explicit when-to-use or alternative guidance is provided. Sibling tools exist but are not 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 provide readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds no additional behavioral context (e.g., rate limits, pagination, authentication). 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?

The description is a single line with pipe-separated keywords followed by a Korean sentence, making it cluttered and less readable. The redundant prefixes ('Cloud Run 로그|배포 로그|서비스 로그|run logs') do not earn their 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 log query tool with 7 parameters and no output schema, the description should explain return format or behavior. It only states 'inquires service logs', missing details on output structure or limit implications. Annotations cover safety but not 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?

All 7 parameters are well-documented in the input schema (100% coverage), so the description adds no extra meaning. Baseline 3 is appropriate as the description does not compensate beyond 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 it queries Cloud Run service logs using a specific verb ('조회합니다'), distinguishing it from sibling tools like gcp_logs_read (general logs) and gcp_run_status (status). The pipe-separated redundant keywords add noise but do not obscure the purpose.

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 gcp_logs_read. The description lacks any explicit 'when to use' or 'when not to use' context, leaving the agent without decision support.

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, destructiveHint, and idempotentHint, but the description adds no additional behavioral context. It does not mention any side effects, authorization needs, or rate limits, merely stating it 'queries' status.

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 short and front-loaded with the main action. The pipe-separated keywords are somewhat redundant but do not significantly bloat the text. Only essential information is included.

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 (read-only status query), annotations covering safety profile, and no output schema required, the description is adequately complete. It covers the what, though it could mention output format implications despite the format parameter.

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 four parameters have schema descriptions (100% coverage), so the baseline is 3. The description does not add extra meaning or context for any parameter, leaving the schema to explain them fully.

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 queries Cloud Run service status, using multiple relevant keywords (상태, 서비스 상태, 배포 상태). The purpose is specific to status checking, distinguishing it from sibling tools like gcp_run_logs or gcp_services_list, though not explicitly differentiating.

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 gcp_run_logs for logs or gcp_services_list for listing services. There is no mention of prerequisites or scenarios where this tool is appropriate or not.

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, but the description adds no behavioral detail beyond 'retrieves secrets'. It does not cover the ability to list versions or show values, nor mention security implications of show_value.

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 short but uses fragmented keywords with pipes, which is somewhat messy. It could be more structured but is not overly 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?

Without an output schema, the description should explain return values, but it only says 'retrieves secrets'. It does not mention the dual behavior of listing all secrets vs. retrieving a specific secret's versions, or the format options.

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 with detailed descriptions for each parameter. The tool description adds no parameter-level information, so it adds no value beyond the schema. 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 that the tool retrieves Secret Manager secrets, matching the tool name 'gcp_secret_list'. Although it lacks specificity about listing vs. retrieving a specific secret, the purpose is clear and distinct from sibling 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 is provided on when to use this tool versus alternatives. While siblings are different in function, the description does not explicate any usage 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 already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds no further behavioral context beyond restating that it lists services. No contradiction, but no extra value.

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?

Description is short but includes redundant synonyms separated by pipes (e.g., 'API 목록|활성화된 서비스|enabled APIs|services'). While not overly verbose, it wastes space and could be more concise.

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?

Without an output schema, the description should explain the return value format, pagination, or error handling. It only says 'query list of enabled APIs'. Lacks details on what each service entry contains or limits.

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 covers all 3 parameters with 100% description coverage. The description adds no additional meaning beyond the schema; it simply repeats that it returns a list. 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 the tool lists enabled API services in a project, using specific verb 'query' and resource 'API services'. It distinguishes from sibling tools like gcp_auth_status or gcp_billing_info by specifying the exact resource type.

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. No mention of prerequisites, typical use cases, or exclusions. It only describes the function without 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 mark idempotent and non-destructive. Description adds file name (.hi-gcloud.json) but does not elaborate on side effects or permissions. No contradiction 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?

Single sentence plus a hint. The leading keyword list (설정|초기화|...) is slightly redundant but overall 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?

Adequate for a simple config tool. Lacks details on error scenarios, return values, or prerequisites (e.g., authenticated gcloud). No output schema, so return format is unclear.

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 all 5 parameters with descriptions. Description adds contextual hint that project_path should be current working folder, which is helpful beyond schema defaults.

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 it creates project-specific GCP configuration file (.hi-gcloud.json). Keywords like setup/init/configure distinguish it from sibling tools that handle auth, billing, logs, 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?

No explicit guidance on when to use this tool versus alternatives (e.g., gcp_auth_status for auth, gcp_services_list for services). Simply instructs to set project_path to current folder.

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 minimal (readOnlyHint=false). Description fails to disclose that starting a proxy blocks the process, requires Cloud SQL Admin API, or that stopping terminates the connection. Behavioral traits are underspecified.

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?

Very short but mixes Korean and English, with tags in the middle. Could be more concise by focusing on English and structuring the purpose clearly.

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 managing a proxy lifecycle with 5 parameters, the description lacks essential context: behavior of start/status/stop, blocking nature, required permissions. Incomplete even with high schema coverage.

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 already cover all parameters. The tool description adds no extra meaning (e.g., explaining action lifecycle or port defaults). Missed opportunity to compensate.

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 runs Cloud SQL Proxy to connect locally, with tags reinforcing the purpose. However, it doesn't distinguish from sibling tools like gcp_sql_query, though none directly overlap.

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, prerequisites, or context for each action (start/status/stop). The agent must infer from parameter names.

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 readOnlyHint, destructiveHint, and idempotentHint. The description reinforces that queries are read-only but does not add additional behavioral context beyond the annotations. 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?

The description is a single sentence with pipe-separated keywords, which is concise but somewhat messy. It front-loads the purpose effectively, though a more structured format could be clearer.

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 does not detail return values or error behavior, but the format parameter implies output options. Given high schema coverage and annotations, it is adequate but not fully complete for a query 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 schema already documents all parameters well. The description adds no extra meaning to the parameters beyond what is 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 clearly states the tool executes read-only SQL queries on Cloud SQL, using both Korean and English terms. It differentiates from sibling tools like gcp_logs_read or gcp_storage_list by specifying 'Cloud SQL' and '쿼리' (query).

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 indicates read-only queries, and the parameter 'query' specifies only SELECT queries are allowed. Context for when to use this vs. other tools is implied but not explicitly stated, e.g., for database querying rather than log reading or storage listing.

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 safe read operations. The description adds behavioral detail about conditional listing (buckets vs objects) based on the bucket parameter, which exceeds annotation coverage.

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 short but uses a pipe-separated list of synonyms, which can be confusing. It is front-loaded but not optimally structured for clarity.

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 no output schema, the description minimally explains what is retrieved (list of buckets/objects) but omits details on output fields or pagination. Acceptable for a low-complexity 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 covers 100% of parameters with clear descriptions. The description reiterates the bucket parameter's conditional behavior already stated in the schema, adding no new semantic 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 tool retrieves Cloud Storage bucket/object lists, using specific verbs and resources. It provides multiple keywords and distinguishes from sibling tools which handle different GCP services.

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. The description only explains what it does, without context about when to use or not use it, or comparisons to sibling tools.

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