OpenAI Tools MCP 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 only states the basic action and does not mention the output format, whether the operation is long-running, potential rate limits, cost implications, or any other behavioral traits beyond the literal action. This is a significant gap for a tool that likely generates network-based results.

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 filler or redundancy. It is appropriately front-loaded and every word adds value, though its brevity is a trade-off against comprehensiveness.

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 (nested object with 5 parameters), absence of annotations, and lack of an output schema, the description is insufficient. It does not explain what the endpoint returns, how it behaves with different parameter values, or how it differs from the sibling image-editing and variation tools. The one-line description leaves too much to inference.

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 top-level parameters 'createImageBody' and 'x-hapi-auth-state' lack schema descriptions, and the tool description only references 'prompt' without covering other parameters like 'n', 'size', or 'response_format'. Although the nested schema provides descriptions for these fields, the description itself does not compensate for the low schema coverage at the top level, failing to clarify how the body should be used or what defaults apply.

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 with a specific verb ('creates') and resource ('an image'), and mentions the key input ('prompt'). However, it does not differentiate between 'createImage' and the sibling tools 'createImageEdit' and 'createImageVariation', which also create images but in different modes, so it misses the opportunity to fully distinguish 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?

No guidance is provided on when to use this tool versus alternatives. The sibling tools 'createImageEdit' and 'createImageVariation' suggest related but distinct use cases, yet the description gives no exclusions or contextual hints. The phrase 'given a prompt' only weakly implies the tool is for generating an image from scratch.

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 fully disclose behavioral traits. It only states that it creates an edited/extended image, but does not mention output format, side effects, permissions, or failure modes. This leaves the agent uninformed about critical runtime 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 a single sentence with no redundant wording. It is front-loaded with the core action and resource, making it instantly scannable.

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 complex (multiple optional parameters, binary inputs, formatting choices), and there is no output schema. The description does not explain return values, pagination, or error handling, leaving significant gaps for an agent to discover through trial and error.

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 0% for top-level parameters, and the description fails to compensate. It only mentions 'original image' and 'prompt,' leaving `n`, `size`, `response_format`, and `mask` undocumented in the description. The nested schema does contain descriptions, but the description itself adds minimal parameter 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's function with a specific verb ('Creates') and resource ('edited or extended image'). It distinguishes from siblings by requiring both an original image and a prompt, which separates it from `createImage` (prompt-only) and `createImageVariation` (image-only variation without a prompt).

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 input requirement ('given an original image and a prompt') implies the use case, but the description does not explicitly state when to use this tool over alternatives or when not to use it. No exclusions or alternative tools are named.

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, but it only states 'Creates a variation' without disclosing side effects, return behavior, or constraints like the image being square and under 4MB. Some of these are in the schema, but the description adds no behavioral context beyond the obvious mutation.

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, well-structured sentence with no redundant information. It is concise 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 tool has a nested schema and no output schema, but the description only states the basic function. It lacks context about the expected output format, variations concept, or use cases, making it insufficient for an agent to fully understand the tool's 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 description coverage is 0%, and the description mentions no parameters. While the schema provides detailed descriptions for each parameter, the description itself adds no parameter semantics and does not compensate for the low 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 'Creates a variation of a given image' is specific with a clear verb and resource. It distinguishes from sibling tools like createImage (creates from text) and createImageEdit (edits with a mask), so the purpose is unambiguous.

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 no guidance on when to use this tool versus alternatives, nor any prerequisites. It merely states the action without context on selection criteria 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?

The description discloses that the tool classifies text, which effectively conveys it's a read-only analysis operation. However, it does not describe the response structure, error behavior, or any side effects. With no annotations provided, the description carries the full burden and leaves room for ambiguity about what the agent can expect.

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 directly states the tool's function. It is front-loaded with the action verb and avoids any redundant or extraneous details.

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, but without an output schema or annotations, the description fails to explain what the classification result looks like or how to interpret it. The agent is left without guidance on the response format, which is crucial for a moderation tool that outputs policy violation categories.

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 itself includes detailed descriptions for both parameters (input and model), with the model parameter having a thorough explanation of the two options and their differences. The tool description adds no parameter-specific information, but the schema compensates, making the baseline of 3 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 uses the specific verb 'Classifies' and clearly identifies the resource ('text violates OpenAI's Content Policy'). This unambiguously distinguishes it from sibling tools like createImage or createTranscription, which serve different domains.

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 context is clear: this tool is for content moderation. While no explicit alternatives or when-not-to-use instructions are given, the sibling tools are clearly unrelated (image/audio/model operations), so the intended use is obvious. However, no direct comparison or exclusions are stated.

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 must fully disclose behavioral traits. It merely states 'Transcribes audio into the input language' and fails to mention return format, permissions, limitations, or side effects. This is a significant gap for a tool with no annotation safety hints.

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, focused sentence with no redundancy, front-loading the core action. It is concise and efficiently states the purpose, though it may be too terse given the tool's complexity.

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 has a nested schema with multiple parameters, no annotations, and no output schema, yet the description is only one sentence. It omits essential context like supported audio formats, output formats, model options, and when to use this tool, making it incomplete for correct invocation.

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 0%, so the description must compensate, but it does not explain any parameters. It only loosely hints at 'audio' and 'input language' which relate to the file and language parameters, but provides no details on file formats, model ID, response_format, or other inputs.

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 'Transcribes audio' and the resource (audio), with 'into the input language' indicating the output is in the audio's original language. However, it does not explicitly distinguish this from the sibling tool createTranslation, leaving some ambiguity for an agent choosing between 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?

The description provides no guidance on when to use this tool versus alternatives like createTranslation, nor does it mention exclusions, prerequisites, or appropriate contexts. It only states what the tool does, offering no usage direction.

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, but it only says 'translates audio into English'—no mention of supported languages, output formats, or processing limitations. It adds minimal behavioral context beyond the name.

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 (one short sentence) and front-loaded, but it's under-specified for a tool with 5 parameters and a nested object. It's not fluff, but it's too sparse to be considered 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?

The description is severely incomplete: no mention of output schema, return format, or when to use translation vs transcription. Given the tool's complexity (nested body, multiple formats), this is inadequate.

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 provides no parameter-level information. Schema coverage is 0%, and while the schema itself has detailed parameter descriptions, the tool description does not compensate or highlight which parameters matter most (e.g., file, model).

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 a specific verb ('Translates') and resource ('audio') with a clear target ('into English'), which distinguishes it from sibling createTranscription (which presumably transcribes without translating). However, it omits that the output is text/transcript, so it's not fully explicit.

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 vs alternatives, e.g., when audio is in a non-English language and English output is needed. It does not mention 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?

With no annotations, the description carries the full burden. It discloses a required role (Owner) but does not mention irreversibility of deletion, potential cascading effects, or what happens to associated resources. This is a key gap for a destructive operation.

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 one sentence with a clear action and a key prerequisite. It is front-loaded and every word earns its place, achieving high conciseness without 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?

Despite being a simple delete operation, the description lacks important context such as response format, whether the operation is asynchronous, error conditions, or clarification of the auth-state parameter. Given the sparse schema and no annotations, the description does not adequately complete the picture.

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 no descriptions for the parameters (0% coverage), and the description does not explain what 'model' should contain (e.g., model ID or name) or the purpose of 'x-hapi-auth-state'. The description adds no parameter-level meaning beyond the verb itself.

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 'Delete a fine-tuned model' with a specific verb and resource, distinguishing it from sibling tools like retrieveModel and listModels. It also adds the targeted object type (fine-tuned model), which is precise.

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 (when you need to delete a fine-tuned model) and states a prerequisite (Owner role), but does not explicitly contrast with alternatives or provide when-not-to-use guidance. Since no sibling is a delete operation, the context is clear but not fully explicit.

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

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

With no annotations, the description carries the burden of disclosing behavior. It implies a read-only listing operation via the verb 'Lists' and specifies output contents (owner, availability), but it does not explicitly state that no modifications occur, mention authentication needs, or describe potential limitations such as pagination.

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 (20 words), front-loaded with the action 'Lists'. Every word adds value, with no filler 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?

For a simple list tool, the description covers the core function and gives some indication of return contents. However, with no output schema, the return format is unspecified, and the auth parameter is unexplained. Given no annotations, more context is needed for a fully complete picture.

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 sole parameter, 'x-hapi-auth-state', is not mentioned in the description. Schema description coverage is 0%, and the description does not compensate by explaining the parameter's purpose or optionality, leaving the agent completely uninformed about this oddly named parameter.

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 tool as listing available models and describing their owner and availability. It uses a specific verb ('Lists') and resource ('models'), which distinguishes it from sibling tools like retrieveModel (specific model lookup) and deleteModel.

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 like retrieveModel. The description does not state that this is for enumeration while retrieveModel is for individual lookups, nor does it mention any prerequisites or contexts.

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. It discloses that the return is 'basic information' with examples (owner, permissioning), giving a sense of scope. However, it does not mention authentication needs, error behavior, or explicitly confirm read-only semantics beyond the verb 'retrieves'.

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, well-structured sentence that focuses on the core purpose and return value. No filler or redundant text, making it easily scannable by an agent.

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 has no annotations, no output schema, and sparse parameter documentation. The description gives a basic purpose and some return content, but lacks response shape, parameter semantics, and usage context. For reliable invocation, an agent would still have to guess about critical 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 coverage is 0% and the description does not compensate. The 'model' parameter is implied by the purpose but its format (ID vs. name) is not explained. The 'x-hapi-auth-state' parameter is entirely omitted, leaving the agent without any guidance on how to populate 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 uses a specific verb ('retrieves') and resource ('model instance'), clearly indicating it fetches a single model's details. It provides extra context about return content (owner, permissioning). It does not explicitly differentiate from siblings like listModels, but the singular 'instance' implies the distinction.

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 listModels or deleteModel. There are no prerequisites, exclusions, or alternative tool references. The description only states what the tool does, not when to use it.

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