OpenAIMCP

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes (chat, embeddings, image generation, task management). The overlap between 'openai_chat_completion' and 'openai_create_response' introduces ambiguity, but descriptions clarify their specific use cases (standard vs. dated models/background processing).
Naming Consistency5/5
All tools follow a consistent 'openai_{verb}_{noun}' pattern (e.g., 'openai_create_embedding', 'openai_list_tasks'). The naming is predictable and adheres to a single convention.
Tool Count5/5
With 11 tools, the server covers the major OpenAI functionalities (chat, embeddings, image generation/editing, model listing, async tasks) without being bloated. The count feels well-scoped for a focused AI service.
Completeness4/5
The tool set covers core workflows (create, read, list) for chat, embeddings, and images, plus async task management. Minor gaps exist (e.g., no delete/moderation tools), but these are not critical for the server's stated purpose.
Average 4.2/5 across 11 of 11 tools scored. Lowest: 3.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Only mentions return format (URL or base64). Lacks disclosure of latency, cost, asynchronous behavior (despite callback_url parameter), model-specific limitations, or that some parameters are model-dependent. Insufficient for an image generation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Structured with a title, purpose paragraph, bullet points for usage, and return type. Some redundancy in bullet points (restate primary purpose). Could be slightly more concise but overall effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complex tool with 13 parameters and output schema. Description covers general purpose and usage but misses key context like model-specific behaviors, async callback handling, and image size limitations. Adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed per-parameter descriptions. The tool description adds no additional parameter meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it generates images using specific models (gpt-image-1, dall-e-3, nano-banana) from text descriptions. Distinguishes from sibling tools like openai_edit_image (edits) and openai_chat_completion (text generation). Verb+resource is specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists three use cases (create from text, artwork, mockups) in a 'Use this when' section. Does not explicitly mention alternatives or when not to use, but the sibling context implies which tool to use for editing (openai_edit_image). Clear and practical.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It mentions the return format (JSON with vectors and usage) but does not disclose potential behavioral traits like rate limits, maximum input length, or any side effects. For a nondestructive embedding tool, this is adequate but not exceptional.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening, bulleted use cases, and a returns line. It is slightly redundant (first sentence and second sentence overlap), but overall concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 params, output schema present), the description covers purpose, use cases, and return type. It does not mention error handling or prerequisites, but the presence of an output schema and detailed schema descriptions compensates for these omissions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds no additional parameter details beyond the schema; the intro and use cases are generic. The schema already adequately describes all four parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates text embeddings using OpenAI models, with specific verb 'Create' and resource 'embeddings'. It distinguishes from sibling tools like chat completion and image generation by focusing on vector representations for semantic search and clustering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'Use this when' bullet points covering key use cases (semantic similarity, search, ML tasks). However, it lacks exclusionary guidance or alternatives, such as noting that for text generation one should use openai_chat_completion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries full burden. It mentions AI-powered edits and returns JSON with image URL/base64, but does not disclose important behaviors like model-specific limitations, potential failures, or the irreversible nature of edits. It adds contextual value but lacks comprehensive transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose and use cases, with bullet points for clarity. It is relatively concise, though the mention of 'AceDataCloud' clutters slightly. The return format section is brief. Overall efficient for the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 12 parameters and an output schema, the description covers use cases, return format, and high-level capabilities. It could be more complete by explaining model-specific behavior (e.g., size presets for different models) and multi-image editing support, but it is generally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description does not add extra parameter information beyond what the schema provides. It mentions broad capabilities but no parameter-specific details beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Edit or modify existing images' and the resource 'existing images using OpenAI image models via AceDataCloud'. It distinguishes itself from sibling tool openai_generate_image by specifying that it works on existing images.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists when to use this tool with three bullet points: modify existing image, add/remove/change elements, apply style/transformation. This helps an AI agent select it over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description bears full burden. It discloses that the tool lists models and returns a table with descriptions, which is transparent for a read-only list operation. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with three short sentences. Front-loaded with the main purpose, then additional scope, then return format. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, output schema exists), the description is complete. It covers what it does, scope, and return value adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the description does not need to add parameter info. Baseline of 4 for 0 parameters is appropriate; description adds no unnecessary detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List all available text embedding models' with a specific verb and resource. It distinguishes from sibling tools like openai_list_chat_models and openai_list_image_models by specifying 'embedding models'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 openai_create_embedding or other listing tools. No when-not or explicit context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 explains that the tool sends a conversation to a model and returns a JSON response with the reply and usage information. This adequately covers the main behavior without contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, using a few sentences and bullet points. Every sentence adds value: purpose, supported models, use cases, and return format. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description does not need to detail return values extensively. It mentions the JSON response includes reply and usage information, which is sufficient. Parameters are fully described in the schema. Could be more complete with error handling, but adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema, e.g., mentioning 'all major GPT and o-series models' and the return format. It does not significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a chat completion using OpenAI models. It specifies the verb 'create' and the resource 'chat completion.' The sibling tools like openai_create_embedding and openai_generate_image are distinct, so this description effectively differentiates itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a 'Use this when:' section listing three specific scenarios: conversation, text generation, and structured JSON output. It does not explicitly mention when not to use or alternative tools, but the use cases are clear and distinct from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral traits. It only mentions returning a guide, omitting whether it is read-only, requires authentication, or has any side effects. Minimal disclosure for a safe retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences, front-loaded with the primary purpose. No redundant or extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, the presence of an output schema, and the tool's straightforward nature, the description sufficiently covers what the tool does and what it returns. No additional detail seems necessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema coverage is 100%. The description adds value by outlining the content of the guide (examples, best practices), though parameter-specific details are unnecessary.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb 'Get' and resource 'guide for using the OpenAI tools'. It clearly distinguishes from sibling action tools like openai_chat_completion or openai_generate_image, which perform operations rather than provide documentation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool should be used to obtain comprehensive guidance, but it does not explicitly state when not to use it or provide alternatives. However, the context of sibling tools makes the use case clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It discloses that the tool is a read-only listing operation and describes the return format. However, it does not mention authentication requirements or rate limits, which is acceptable given the simplicity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loading the main action in the first sentence. It contains only two sentences plus a returns line, with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and an output schema exists (implied by 'Returns'), the description sufficiently explains the tool's purpose and output. It could mention that it includes both old and new models, but it's adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters with 100% schema coverage. The description adds value by explaining the nature of the returned data (table with descriptions). This meets the baseline for no-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all available chat completion models, distinguishing it from sibling list tools like openai_list_embedding_models. The verb 'list' and resource 'chat completion models' are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool or when to choose alternatives. While the purpose is clear, it does not educate the agent about its role relative to other tools like openai_chat_completion. Implied usage but no explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses background processing behavior (returns immediately with task ID) and return format (JSON with output and usage). Could add more about idempotency 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Highly concise: three sentences plus bullet list. No unnecessary words, front-loaded with purpose, then usage, then return info.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given an output schema exists (context signal), description adequately covers purpose, usage, and behavioral traits. All 6 parameters are described in schema, and description complements with use-case context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds context (e.g., background ties to task ID, model variants mention search-preview), but does not significantly augment parameter meaning beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a response using the OpenAI Responses API, distinguishes it from Chat Completions, and lists specific use cases like accessing dated model variants and background processing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Use this when' section provides three explicit scenarios, including background processing and search-preview models. It implicitly contrasts with Chat Completions via sibling tool name, but lacks explicit 'do not use when' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 explains that tasks are only created when a callback_url was provided, that synchronous calls are not stored, and it lists the returned fields. This provides sufficient behavioral context for a read-only retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections, including a note about task creation and usage guidance. It is concise and front-loaded with the main purpose. Minor redundancy (e.g., mentioning async twice) but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema is implied (the description lists returned fields), the description covers the tool's purpose, usage, and parameter semantics. It is complete for a retrieval tool, including the prerequisite about callback_url and the distinction from synchronous calls.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with descriptions for both 'id' and 'trace_id'. The description adds value beyond the schema by explaining the relationship: at least one must be provided, and trace_id takes precedence when both are given. It also clarifies that 'id' comes from the original request and 'trace_id' is a custom ID.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool retrieves a single async image task by task ID or custom trace ID. It uses specific verbs ('retrieve') and resource ('async image task'), and distinguishes itself from sibling tools like openai_list_tasks (which lists tasks) and openai_generate_image/openai_edit_image (which create tasks).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: after calling openai_generate_image or openai_edit_image with a callback_url, to check status and retrieve result. It explains the prerequisite (callback_url must have been set) and the use case of checking async task status. It does not explicitly state when not to use it, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description effectively communicates a read-only action that returns a table. It discloses the operation's scope and outcome, which is adequate for a non-destructive listing tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is remarkably concise with three short sentences that front-load the purpose and provide essential details without any fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and the presence of an output schema, the description fully covers what the tool does and what it returns, leaving no ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so the baseline is 4. The description does not need to add parameter details, and it correctly focuses on the output.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all available image generation and editing models and specifies the relevant endpoints, effectively distinguishing it from sibling list tools like list_chat_models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states when to use the tool (to list available image models) and mentions endpoints, making the purpose clear. It lacks explicit when-not or alternative guidance, but the context is sufficient for a simple listing tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behavioral info: tasks only created with callback_url, return format (JSON with items and count), and pagination via limit/offset. Lacks rate limit or auth details but sufficient for core behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise, well-structured with bullet points. First sentence fronts the purpose. Every sentence adds unique information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 optional parameters, no annotations, but presence of output schema, the description covers essential constraints (required filter, callback_url condition) and return format. Sufficient for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% so baseline is 3. Description adds value by stating the 'at least one filter' requirement and explaining callback_url context, which are not in the schema's parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists async image tasks using batch query filters, with the verb 'list' and resource 'async image tasks'. It distinguishes from sibling tool openai_get_task which retrieves single tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides 'Use this when' scenarios (listing multiple tasks, viewing all tasks for an application or user). Also notes that tasks are only stored with callback_url, implying when not to use. Could explicitly mention single-task retrieval via get_task.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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