api-mock-server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the return value (endpoint URL) but does not mention side effects, validation requirements, uniqueness constraints, permissions, or error handling. For a creation tool, this is a significant gap.

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 concise sentences: the first states the action and input, the second states the output. Every word earns its place, with no redundancy 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?

Given there is no output schema, the description appropriately explains the key return value (an immediately usable endpoint URL). It covers the core create-tool behavior and the essential output. However, it does not mention potential prerequisites (e.g., valid spec format) or asynchronous behavior, leaving 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?

The input schema covers all three parameters with descriptions (name, spec, description), so schema coverage is 100%. The description adds no extra parameter semantics beyond what is already in the schema, as it restates the spec input and output. 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 that the tool creates a mock API server from an OpenAPI/Swagger spec (JSON) and returns an immediately usable endpoint URL. This specific verb-resource combination ('creates' + 'mock API server') distinguishes it from the sibling tools like delete_mock, list_mocks, and update_mock_response.

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 the use case (when you have an OpenAPI spec and need a mock server) but does not explicitly state when to use this tool over alternatives. It lacks exclusions or alternative tool mentions, so it provides only implied 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?

With no annotations, the description carries the full burden for behavioral disclosure. It reveals it deletes a mock server but doesn't mention irreversibility, cascading effects on associated endpoints/logs, or any required permissions. This is minimal and leaves transparency 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 a single, compact sentence that directly conveys the tool's function. It is front-loaded and contains no unnecessary words, making it 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?

For a simple delete operation with one parameter and no output schema, the description is minimally viable. However, it lacks context about side effects, usage context, or any effect on related resources. It is complete enough for the basic action but not fully contextual.

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 provides a description for mock_id ('삭제할 목업 서버 ID') with 100% coverage. The tool description adds no parameter-specific meaning beyond what the schema provides, so the 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 action ('삭제합니다' - deletes) and the resource ('목업 서버' - mock server) with a specific verb. It distinguishes from sibling tools like create_mock, get_mock_endpoint, list_mocks, and update_mock_response.

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, no prerequisites, and no warnings. It simply states the action without any usage 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?

With no annotations, the description must carry behavioral context. It indicates a read-only operation via '반환' but does not disclose error handling, authentication needs, or exact content of 'detailed information.' The read-only nature lowers the risk, but gaps remain.

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 no redundant information. It is concise and front-loaded with the action and target.

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 adequately names the return content (details and endpoint list) but does not specify the structure or possible error cases. For a simple one-parameter getter, this is largely 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% and the parameter description '목업 서버 ID' is already present. The tool description adds no additional meaning or format details 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 returns detailed information and endpoint list for a specific mock server. The verb '반환합니다' is specific and the resource is distinct from sibling tools like get_mock_logs or list_mocks.

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 '특정 목업 서버' implies use when a single server's details are needed, contrasting with list_mocks for all servers. However, it does not explicitly state when not to use it or mention 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?

With no annotations, the description carries the full burden. It implies a read-only operation but does not explicitly confirm non-mutation, nor does it disclose any limits, error behavior, or whether the logs are the raw requests or processed summaries. There is no mention of authentication requirements 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?

A single, clear sentence with no wasted words; it front-loads the key action (조회) and the object (logs).

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 two parameters and no output schema, the description is minimally sufficient but lacking in details about return format, pagination behavior, or what constitutes a 'log.' The schema covers the limit parameter, but the overall context is thin.

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% for both parameters with clear descriptions. The description adds no extra parameter meaning, but per the rubric baseline of 3 is appropriate when schema handles the semantics.

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 uses specific verb '조회' (retrieve) and identifies the specific resource 'request logs of the mock server.' It clearly distinguishes from siblings like list_mocks or get_mock_endpoint, which concern mock definitions rather than 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?

No guidance on when to use this tool vs alternatives, no mention of prerequisites or exclusions. The description merely states the function; it does not provide context for selection among 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates a read operation returning a list, implying no side effects, but it does not mention any details such as pagination, sorting, or potential empty results. This is acceptable for a simple listing tool but lacks explicit 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 a single, concise Korean sentence that is front-loaded with the action and resource. It contains no filler or redundant information, perfectly matching the tool's simple purpose.

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 is minimal and lacks details about the return value structure (e.g., what fields each mock server object contains) since there is no output schema. For a simple list tool, this is adequate but not fully complete; an agent might need more context to interpret the returned list 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 0 parameters and schema coverage is trivially 100%. Baseline for 0 params is 4, as there are no parameter semantics to clarify. The description correctly omits parameter information since none exist.

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 '반환합니다' (returns) with a clear resource '생성된 모든 목업 서버 목록' (list of all created mock servers). It explicitly mentions 'all' which distinguishes it from sibling tools like get_mock_endpoint that likely return a single mock, thus providing clear purpose and sibling differentiation.

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: call this tool when you need the full list of mock servers. However, it does not explicitly state when to prefer this over alternatives or provide any exclusions, so the guidance is only implied.

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 must carry the transparency burden. It does state the dual behavior (modify or add), but it fails to disclose important behavioral traits such as overwrite behavior, whether existing configurations are replaced, required authentication, or side effects. This is a significant gap for a mutation tool.

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, concise sentence with no redundant information. It is front-loaded with the core action and clearly phrased, making it easy 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?

The description is adequate but leaves gaps. It mentions the modify-or-add behavior but does not explain that the endpoint is identified by mock_id, method, and path, nor that status/headers can be updated without 'response'. It also lacks context about whether the tool is scoped to a specific mock server or what happens on conflict. The schema fills in parameter details, but the overall workflow is not fully described.

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 baseline is 3. The description does not add extra meaning beyond the schema; it only references endpoint response, which partially maps to 'response' and 'status' parameters but does not elaborate on format or syntax. The schema already documents all parameters clearly.

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 modifies an endpoint's response or adds a new endpoint, using specific verbs (수정/추가) and resource (특정 엔드포인트의 응답). This distinguishes it from sibling tools like create_mock or delete_mock, as it targets endpoint configuration within a mock server.

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 you need to update an existing endpoint's response or add a new endpoint to a mock. However, it does not explicitly contrast with alternatives (e.g., create_mock for creating a whole mock server) or provide when-not-to-use guidance, leaving the context merely implied.

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