The APIAny MCP Server connects AI agents and developers to 100+ AI models, enabling model discovery, pricing estimation, documentation lookup, code generation, and paid media generation workflows.
List Models: Browse public APIAny models filtered by type (chat, image, video, audio), provider, or search text, with pricing and capability metadata.
Search Models: Search models using natural-language queries, filtered by modality or provider.
Get Model Details: Retrieve detailed information about a specific model by ID or display name.
Estimate Costs: Calculate APIAny credit costs based on number of requests, input/output tokens, and cached tokens.
Get Integration Examples: Generate ready-to-use code examples in cURL, Python, JavaScript, Go, Java, or PHP for integrating with APIAny models.
Get Model Usage: Retrieve endpoint details, payload structure, async behavior, and language-specific examples for one or more models.
Create Image Tasks: Initiate a paid asynchronous image generation task (requires API key and explicit confirmation).
Create Video Tasks: Initiate a paid asynchronous video generation task (requires API key and explicit confirmation).
Check Task Status: Poll the status of an async image or video generation task using its task ID (requires API key).
Get Documentation Context: Fetch compact APIAny documentation from llms.txt for context-aware agent assistance.
All non-paid operations work without an API key, making the server safe for exploratory use.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@APIAny MCP Server show JavaScript example for nano-banana-pro"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
APIAny MCP gives AI agents a structured way to understand and use APIAny:
Discover public APIAny models by provider, modality, capability, and pricing metadata.
Estimate APIAny credits before choosing a model or running a workflow.
Fetch compact documentation context from APIAny docs and llms.txt.
Generate OpenAI-compatible cURL, Python, JavaScript, Go, Java, and PHP examples.
Create image or video generation tasks only when an API key is configured and the user explicitly confirms paid usage.
Package an agent skill at skills/apiany-integration so supported agents can learn the APIAny workflow.
Related MCP server: Puter MCP Server
Run the server directly with npm:
npx -y @apiany-ai/mcpOr install and run it from this repository:
git clone git@github.com:ailingqu/ApiAny.AI-MCP.git
cd ApiAny.AI-MCP
npm install
npm startUse this configuration in Claude Code, Cursor, VS Code, or another MCP-compatible client:
{
"mcpServers": {
"apiany": {
"command": "npx",
"args": ["-y", "@apiany-ai/mcp"],
"env": {
"APIANY_BASE_URL": "https://apiany.ai"
}
}
}
}For paid image or video task tools, also set APIANY_API_KEY:
{
"APIANY_BASE_URL": "https://apiany.ai",
"APIANY_API_KEY": "your_apiany_api_key"
}Example prompts:
Use APIAny MCP to find an image model for product photos, compare pricing, and show JavaScript integration code.Use APIAny MCP to estimate credits for 20 gpt-style chat requests with 10k input tokens and 5k output tokens.Use APIAny MCP to show JavaScript usage for nano-banana-pro and Python usage for veo3-1-fast.Tool | Purpose | API key |
| List public APIAny models with pricing and capability metadata. | No |
| Search models by text, provider, modality, or capability. | No |
| Get one public model by model id or display name. | No |
| Estimate credits from public pricing metadata. | No |
| Return endpoint, payload, async behavior, and language examples for models. | No |
| Return cURL, Python, JavaScript, Go, Java, or PHP examples. | No |
| Return compact APIAny documentation context from | No |
| Create a paid async image task after explicit confirmation. | Yes |
| Create a paid async video task after explicit confirmation. | Yes |
| Read async media task status. | Yes |
The APIAny Integration skill lives in skills/apiany-integration.
It helps agents:
Choose APIAny models by modality, provider, pricing, and capability.
Generate examples for chat, image, video, and media workflows.
Explain async task creation and polling.
Require explicit confirmation before paid generation tasks.
Variable | Required | Description |
| No | APIAny base URL. Defaults to |
| Only for paid tools | API key used for paid image/video task creation and task polling. |
Read-only tools work without an API key. Paid generation tools require both:
APIANY_API_KEY
confirm_paid_request=true
The server does not persist API keys. It only reads them from the current process environment.
npm install
npm run check
npx -y @modelcontextprotocol/inspector node src/server.jsUseful files:
src/server.js: MCP server implementation.
examples/mcp.json: client configuration example.
docs/distribution.md: release checklist for npm, MCP directories, and skill marketplaces.
CHANGELOG.md: release history.
Website: https://apiany.ai
Models: https://apiany.ai/models
Docs context: https://apiany.ai/llms.txt
MIT
Create a paid APIAny asynchronous image generation task. Requires APIANY_API_KEY and confirm_paid_request=true.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| prompt | Yes | ||
| size | No | Aspect ratio such as 1:1 or 16:9, or a pixel size when supported. | |
| quality | No | Resolution/quality tier such as 1k, 2k, 4k, standard, or hd. | |
| image_urls | No | Optional reference/input images for image-to-image or editing. | |
| callback_url | No | Optional public HTTPS callback URL. | |
| extra_json | No | Optional extra request fields to merge into the JSON body. | |
| confirm_paid_request | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the task is asynchronous and requires a paid request, which adds behavioral context. However, it does not describe other important traits such as error handling, rate limits, idempotency, or what the response looks like.
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, which is concise, but contains a typo ('APIAny') that reduces clarity. It could be slightly restructured for better readability without adding length.
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 8 parameters, nested objects, and no output schema, the description provides minimal context. It does not explain return values, error handling, or parameter interactions, leaving significant gaps 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 coverage is 63% (baseline 3). The description adds value by specifying that 'confirm_paid_request' must be set to true, which goes beyond the schema's default of false. However, it does not explain other parameters like 'model', 'prompt', 'size', etc., leaving interpretation to 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 creates an 'asynchronous image generation task' with a 'paid' characteristic. However, the phrase 'paid APIAny' contains a typo that slightly obscures clarity. It does not explicitly distinguish itself from the sibling tool 'create_video_task', but the resource type (image vs. video) is implicitly different.
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 mentions prerequisites (APIANY_API_KEY and confirm_paid_request=true) but provides no guidance on when to use this tool versus alternatives like 'create_video_task'. There are no explicit when-to-use or when-not-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a paid APIAny asynchronous video generation task. Requires APIANY_API_KEY and confirm_paid_request=true.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| prompt | Yes | ||
| duration | No | ||
| aspect_ratio | No | Video aspect ratio such as 16:9 or 9:16. | |
| resolution | No | Optional video resolution such as 720p or 1080p. | |
| image_urls | No | Optional reference images for image-to-video models. | |
| video_urls | No | Optional reference videos for models that support video input. | |
| audio_urls | No | Optional reference audio for models that support audio input. | |
| callback_url | No | Optional public HTTPS callback URL. | |
| extra_json | No | Optional extra request fields to merge into the JSON body. | |
| confirm_paid_request | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It reveals the task is paid and asynchronous but omits details such as whether it is destructive, idempotent, or any rate limits/authentication nuances. The description is insufficient for safe agent invocation.
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 very short (one sentence) but contains a typo ('APIAny') that reduces clarity. It is front-loaded with purpose, but the awkward phrasing and missing structure (e.g., no bullet points or organization) make it less effective.
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 an 11-parameter tool with no output schema and no annotations, the description is far too minimal. It does not explain return values, error handling, or the nature of the async task (e.g., how to poll status). Essential context for a paid API is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 64% description coverage, so baseline is 3. The description adds value by clarifying that 'confirm_paid_request' must be set to true for paid requests, which is not explained in the schema (only shows default false). This extra context raises the score to 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Create' and the resource 'video task', and it specifies that it is an async video generation task. However, it does not explicitly distinguish itself from the sibling tool 'create_image_task', though the resource name implies the difference. The typo 'APIAny' slightly detracts but does not obscure meaning.
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 a prerequisite (APIANY_API_KEY and confirm_paid_request=true) but does not guide when to use this tool versus alternatives like 'create_image_task' or 'estimate_cost'. There is no when-not-to-use or explicit context for choosing this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Estimate APIAny credits for a model using public pricing metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| requests | No | ||
| input_tokens | No | ||
| output_tokens | No | ||
| cached_input_tokens | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description gives minimal behavioral context. It mentions 'using public pricing metadata' but does not disclose whether the tool is read-only, requires authentication, has rate limits, or any side effects. The agent cannot assess safety or cost of invocation.
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, very concise. However, the ambiguous term 'APIAny' detracts from clarity. The verb 'Estimate' is front-loaded, but the sentence could be improved by clarifying the ambiguous term.
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 5 parameters, no output schema, and no annotations, the description is grossly incomplete. It does not explain the output format, pricing assumptions, or how the estimate is calculated. An agent lacks essential context to use the tool effectively.
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%, meaning the description must compensate, but it adds no meaning beyond parameter names. Parameters like 'model', 'requests', 'input_tokens' are self-explanatory, but the description does not explain their role in estimation or any constraints not already 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 verb 'Estimate' and the resource 'APIAny credits for a model', distinguishing it from sibling tools which are unrelated to cost estimation. However, the term 'APIAny' is ambiguous, possibly a typo for 'API' or 'API any', slightly reducing clarity.
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 prerequisites or exclusions. Given the lack of context, the agent has no information to decide appropriateness.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return compact APIAny documentation context from llms.txt.
| Name | Required | Description | Default |
|---|---|---|---|
| max_chars | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as side effects, permissions, or rate limits. It simply states the return value without context.
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, which is concise, but it is too sparse and lacks important information. It is not optimally structured as it fails to front-load key details like parameter usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (one optional parameter, no annotations, no output schema), the description is incomplete. It does not explain the format of the returned documentation or any constraints.
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 one parameter (max_chars) with 0% schema coverage. The description does not mention this parameter or its purpose, leaving the agent without guidance on its usage.
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 compact APIAny documentation context from llms.txt, using a specific verb and resource. It distinguishes from sibling tools which are about creating tasks or listing models.
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 any conditions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return APIAny integration examples for a model and language.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | curl | |
| model | No | gpt-5.5 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It does not mention idempotency, authentication requirements, or any side effects. The description is too minimal to understand the tool's behavior beyond its basic 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 a single, efficient sentence with no wasted words. It is front-loaded with the action and resource, achieving maximum clarity in minimal space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should explain what the return value contains (e.g., code snippets, endpoints). It does not, leaving the agent without critical context about the response format or structure.
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%, meaning the description adds no meaningful detail about the parameters beyond the schema. It simply references 'model and language' without explaining format, constraints, or expected values.
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 ('Return'), the resource ('APIAny integration examples'), and the scope ('for a model and language'). It distinguishes the tool from siblings that handle creation tasks or cost estimation.
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 get_docs_context or search_models. The description lacks context about typical use cases or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get one APIAny model by public model id or display name.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a read-only operation ('Get'), but with no annotations, it fails to disclose potential behaviors such as error handling (e.g., what happens if the model is not found), rate limits, or authorization requirements. Adequate for a simple retrieval but could be more transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. It efficiently conveys the essential information.
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 and the lack of output schema or annotations, the description is adequate but not comprehensive. It covers the basic operation and parameter meaning, but omits details about return values or error conditions.
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 0% description coverage, but the description adds that the `model` parameter can be either a public model id or a display name. This provides crucial context beyond the raw schema, though an example or format hint would strengthen 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 clearly states the verb 'Get', the resource 'one APIAny model', and the method of identification 'by public model id or display name'. It effectively distinguishes from sibling tools like `list_models` and `search_models`.
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 the tool (when you have a specific model id or display name), but it does not provide explicit guidance on when not to use it or direct comparisons to sibling tools. No exclusions or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return endpoint, payload, async behavior, and language example for one or more APIAny models.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Optional model id or display name. Omit to list usage for models. | |
| type | No | ||
| language | No | curl | |
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only lists output types without explaining behavioral traits like idempotency, pagination, or error handling. It does not confirm read-only or disclose any side effects.
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, which is concise but not structured. Key information (returned items) is present but not front-loaded. It could be organized more effectively.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 4 parameters, low schema coverage, and no output schema, the description is insufficient. It mentions output types but not their structure, limitations, or combinations. Missing details like pagination behavior of 'limit' 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?
Schema description coverage is only 25% (only 'model' has a description). The tool description does not elaborate on 'type', 'language', or 'limit' parameters, adding minimal value beyond the schema. Users must infer their roles.
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 returns endpoint, payload, async behavior, and language example for one or more models, specifying the tool's output. However, it does not explicitly differentiate from siblings like 'get_model' or 'list_models', though it implies a different focus on usage details.
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 like 'get_model' (which returns model metadata) or 'search_models'. The description lacks context on typical use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get an APIAny async generation task status. Requires APIANY_API_KEY.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It only states the API key requirement and that it gets status. It does not disclose whether the operation is read-only, idempotent, or what happens if the task_id is invalid. No mention of side effects, rate limits, or error 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 short and to the point at one sentence plus an API key note. However, the brevity sacrifices necessary information; it is adequately concise 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 the tool's simplicity (single parameter, no output schema), the description is incomplete. It does not explain the return value, possible statuses, or how to interpret the response. An agent using this tool would lack key information for handling its output.
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 one parameter (task_id) with no description, and schema description coverage is 0%. The description adds no meaning about the parameter, such as where to obtain the task_id or its expected format. It fails to compensate for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'APIAny async generation task status', which distinguishes it from sibling tools like create_image_task (creation) and get_model (model details). However, it does not elaborate on what the status entails.
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. It omits context such as the need to first create a task using create_image_task or create_video_task, or that this tool is for polling completion. The API key requirement is mentioned but is a prerequisite, not usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List public APIAny models with pricing and capability metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Optional model search text. | |
| type | No | ||
| provider | No | Optional provider name, such as OpenAI or Google. | |
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral disclosure. It only states the tool lists models, implying a read operation, but lacks details on rate limits, authentication, pagination, or whether the operation is safe and non-destructive.
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, which is concise, but includes a typo ('APIAny') and is somewhat vague. It could be improved with clearer phrasing.
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 4 parameters and no output schema or annotations, the description is incomplete. It does not describe the return format, pagination behavior, or what 'pricing and capability metadata' entails.
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 50% (only 'q' and 'provider' have descriptions). The tool description adds no additional meaning beyond the schema and does not explain the remaining parameters ('type', 'limit').
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists public models with pricing and capability metadata. Although there is a typo ('APIAny'), the purpose is still understandable and distinguishes from sibling 'get_model' which retrieves a single model.
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 'search_models'. There are no explicit usage conditions, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search APIAny models by natural-language text, provider, modality, or capability.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| type | No | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It only states search dimensions but omits whether the tool is read-only, requires authentication, has rate limits, or returns a list versus single item. No side effects or behavioral traits are mentioned.
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, which is concise, but the typo 'APIAny' and ambiguity reduce clarity. It could be more structured and accurate.
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 3 parameters and no output schema. The description fails to explain the return format, pagination (limit behavior), or how to effectively use the search. It leaves significant gaps for an agent to invoke 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?
Schema parameter descriptions are empty (0% coverage). The description hints that 'query' is for natural-language text and 'type' for modality, which adds some meaning. However, it also introduces 'provider' and 'capability' which are not in the schema, causing confusion. The 'limit' parameter is not addressed.
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 identifies the tool as searching models by natural-language text, provider, modality, or capability, but contains a typo ("APIAny") and overpromises search dimensions not present in the schema (provider, capability). The core purpose is vaguely clear but lacks precision.
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 over siblings like list_models or get_model. The description gives no context about appropriate use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0create_image_taskcreate_video_taskestimate_costget_docs_contextget_integration_examplesget_modelget_model_usageget_task_statuslist_modelssearch_modelsAll tools have clearly distinct purposes: image/video task creation, cost estimation, documentation retrieval, model lookup, usage examples, task status, listing, and searching. No overlap is evident.
All tool names follow a consistent verb_noun pattern with underscores (e.g., create_image_task, list_models). Verbs like create, get, list, search are used uniformly, and naming conventions are fully consistent.
With 10 tools, the set is well-scoped for an API abstraction layer. It covers discovery (list, search), details (get_model, get_model_usage), cost estimation, task creation (image/video), status checking, and documentation—each tool earns its place.
The tool surface covers key workflows: model discovery, cost estimation, async task creation and status checking. Minor gaps include lack of task cancellation or listing, but these are non-essential for core usage, so it's nearly complete.
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