GrokMCP

Server Quality Checklist

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Latest release: v0.1.8
Disambiguation5/5
Each tool targets a distinct operation: chat completion, model listing, task status polling, and the two video generation entry points are clearly separable. The single-task vs batch-task tools are explicitly differentiated, and text_to_video vs image_to_video cross-reference each other to prevent confusion.
Naming Consistency4/5
All tools share a consistent grok_ prefix and snake_case convention, making the set predictable. Minor deviations exist: grok_chat_completions lacks an explicit verb, and text_to_video/image_to_video use a source_to_target pattern rather than verb_noun, but these are readable and do not break the overall style.
Tool Count5/5
Eight tools is a well-scoped size for a Grok API server covering chat and video generation. Each tool serves a clear purpose without redundancy, and the inclusion of model listing, prompt guidance, and task polling makes the set self-contained.
Completeness4/5
The core workflows are covered: chat completion, model discovery, text-to-video, image-to-video, and task status retrieval in both single and batch forms. Minor gaps exist such as no cancellation or deletion of video generation tasks, but the primary create-and-poll lifecycle is fully supported.
Average 4.4/5 across 7 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 17 commits in the last 12 weeks
- Last stable release on August 28, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. It states it returns status and video info, but does not disclose if it is read-only, side effects, rate limits, or error handling on missing task IDs. Basic coverage, not rich.
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?
Extremely concise: one-line summary, efficiency note, bulleted usage list, and returns statement. Every sentence is necessary and well-structured.
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 simplicity (one parameter, output schema exists), the description is reasonably complete. Mentions the return type and usage scenarios. Could briefly address error handling for partial batches, but acceptable.
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% and already documents the parameter well. The description adds context about efficiency but does not significantly extend meaning 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 it queries multiple video generation tasks at once, using a specific verb and resource. It explicitly differentiates from the sibling grok_get_task by noting it is more efficient.
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?
Provides explicit 'Use this when:' scenarios and contrasts with calling grok_get_task multiple times. Lacks explicit when-not-to-use, but the positive guidance is clear and helpful.
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?
With no annotations, the description carries the full burden. It discloses the operation (animating image into video) and the return shape (Task ID, video information, URLs, state). However, it does not explicitly state that generation is asynchronous, whether the response is immediate or requires polling via grok_get_task, or any limitations on input images. This ambiguity is a notable gap.
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 and well-structured: a one-sentence purpose, a brief explanation, a bulleted 'Use this when' list, a sibling-tool pointer, and a return summary. Every sentence earns its place; no fluff.
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?
For an 8-parameter tool with a rich schema and output schema, the description covers purpose, usage, and returns. The main gap is the lack of explicit guidance on the async task lifecycle (e.g., 'returns a task ID immediately; use grok_get_task to poll for completion'). Given the sibling tools include grok_get_task, this context would complete the picture.
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 the baseline is 3. The description adds only minimal context beyond the schema, such as mentioning prompt for motion and reference images for style. It does not explain parameter interactions or constraints beyond what the schema already provides.
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 starts with a specific verb+resource: 'Generate AI video from a reference image using Grok Imagine.' It clearly states the tool animates an input image into a video clip. It distinguishes itself from the sibling tool grok_text_to_video by explicitly directing text-only generation there.
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?
The description provides a 'Use this when' bullet list with three concrete scenarios, and directly names the alternative tool ('For video generation from text only, use grok_text_to_video instead'). This gives clear when-to-use and when-not-to-use 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?
With no annotations, the description carries the behavioral burden, and it delivers the key traits: it sends messages to a remote Grok model and returns the response in the OpenAI-compatible chat completion format, with vision and function-calling capabilities called out. It does not mention cost, rate limits, or data-sharing implications of calling an external API, but those are inherent to the named API and less critical than the operation/response behavior.
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 and well structured with a short definition, scoped usage bullets, an alternative, and a returns note. Minor redundancy exists between the opening sentence and the 'Returns:' line, but no sentence is wasted.
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?
For a 29-parameter tool with a fully described schema and an output schema, the description covers purpose, use cases, exclusions, and response format. It is not a 5 because it omits an example of the required messages structure and model-specific caveats, though the schema compensates for most of this.
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 the input schema already documents all 29 parameters. The narrative mentions vision and tool calling, which aligns with messages/tools, but it does not add syntax or format details beyond what the schema provides; 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?
The description opens with a specific action and resource: 'Create a Grok (xAI) chat completion via the AceDataCloud Grok API' and then clarifies it sends messages to a Grok model and returns an OpenAI-compatible response. It also separates itself from sibling generation tools by telling agents to use grok_text_to_video / grok_image_to_video for video.
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?
It provides explicit 'Use this when' bullets for chat/reasoning, vision, and tool calling, and an explicit exclusion: 'For generating videos, use grok_text_to_video / grok_image_to_video instead.' This tells the agent both when to invoke this tool and when not to.
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 carries full burden. It explains the query nature (read-only), what it returns (status, URLs, metadata), and task states including error handling. This provides sufficient behavioral context for safe invocation.
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 bullet points and clear sections. It is front-loaded with purpose and usage guidelines. Minor duplication in 'Use this when' list could be trimmed, 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 the tool's simplicity (1 parameter, output schema exists), the description covers purpose, input, states, and return info. Missing info like rate limits or prerequisites is not critical for this query tool.
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% with one parameter well-described. The description adds value by specifying that the task_id comes from generation tool responses, which helps the agent understand the parameter's origin 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 tool queries the status and result of a video generation task. The verb 'Query' and resource 'task' are specific, and the context distinguishes it from sibling grok_get_tasks_batch (plural) for multiple 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 provides three explicit 'Use this when' scenarios covering completion check, URL retrieval, and full details. It also lists task states. However, it does not explicitly exclude usage when alternative tools like grok_get_tasks_batch are more appropriate, or mention prerequisites like authentication.
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 the description carries full burden. It specifies that it returns tables with capabilities, which is transparent but could mention if it makes a network call or requires authentication. For a simple read operation, this is adequate.
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 four sentences, well-structured with a summary, details on what it shows, and a return note. No fluff, every sentence adds value.
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 and an output schema, the description covers purpose, usage, and return format. It is complete for a list tool with siblings, and does not need to explain return values due to the output schema.
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 no parameters, so schema coverage is 100% trivial. The description adds context about what the tool returns (chat + video models, capabilities), which is valuable beyond the empty schema. Baseline 4 for zero params 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?
The description clearly states it lists all available Grok models (chat + video) and their capabilities, using a specific verb ('list') and resource ('models'). It distinguishes from siblings like grok_chat_completions (which uses models) and the video generation tools.
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 says 'Use this to understand which model to choose,' providing clear usage guidance. While it doesn't mention when not to use or alternatives, the context is sufficient for a list 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?
No annotations are provided, so the description fully bears the burden of behavioral disclosure. It accurately describes a read-only operation that returns a guide, which is non-destructive and safe. No contradictions or omissions.
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 brief and front-loaded with the core purpose, but the docstring format includes redundant backticks and a 'Returns:' section that could be integrated. Overall efficient with minimal waste.
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?
For a parameterless tool with an output schema, the description fully explains what the guide covers (structure, examples, tips). Given zero complexity and the output schema handling return details, it is complete.
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?
There are no parameters, and the schema coverage is 100% (empty). The description adds value by detailing the content of the returned guide (prompt structure, examples, tips), exceeding the baseline of 4 for zero-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 provides guidance on writing prompts for Grok Imagine video generation, using a specific verb ('Get') and resource ('prompt guide'). It distinguishes itself from sibling tools like grok_text_to_video, which perform actual generation.
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 use when needing help with prompt structuring for video generation, but does not explicitly state when not to use it or provide alternatives among siblings. The context from sibling names makes it fairly clear, but lacks explicit exclusions.
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 discloses that it returns a categorized list of tools and example workflows, which is sufficient for a read-only 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 concise with three short sentences, no unnecessary words, and front-loaded purpose. Every sentence adds value.
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?
For a simple listing tool with no parameters and an output schema present, the description fully covers purpose, usage context, and return format.
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?
With no parameters, schema coverage is 100%. The description does not need to add parameter details, meeting the baseline of 4 for zero-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 Grok Imagine tools and workflows, and serves as a reference guide. This distinguishes it from sibling tools that perform specific actions.
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 use for discovery and understanding tool relationships but does not explicitly state when to use versus alternatives or when not to use.
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 carries the full burden. It discloses the async nature by stating 'Returns: Task ID and generated video information including URLs and state,' which implies polling. It also mentions model-specific durations, cost implications of resolution, and callback behavior. Minor gap: it doesn't explicitly state that generation is asynchronous or mention failure/error handling, but the return-type disclosure is strong.
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 well-structured: purpose sentence, context, explicit use cases, sibling-tool reference, model compatibility note, and a concise returns section. Every sentence earns its place, and it is front-loaded with the primary action. No redundancy or filler.
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 six parameters with complex model constraints, the description covers all necessary operational context: supported models, duration limits, resolution cost implications, aspect ratio usage, callback behavior, and return type. It is complete for a task-creation tool with rich schema and no need to explain return values beyond the stated task ID and URLs.
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 the baseline is 3. The description adds critical value beyond the schema by clarifying which models support text-to-video and explicitly warning against using 'grok-imagine-video-1.5:official' for this tool. This prevents a common mistake and adds semantic meaning not present in the schema enum alone.
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 opens with a specific verb+resource statement: 'Generate AI video from a text prompt using Grok Imagine.' It clearly distinguishes the tool from its sibling grok_image_to_video by explicitly noting that image-to-video should use that sibling tool. The 'Use this when' bullets further narrow the purpose to text-only generation with creative freedom.
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?
Explicit 'Use this when' bullets provide clear usage context, and the description directly names the alternative tool for reference-image inputs. It also lists the exact models that support text-to-video, warning against using an incompatible model. This gives unambiguous when-to-use and when-not-to-use guidance.
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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