xAI MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: analyze_image handles image analysis, chat is for text conversations, generate_image creates images, generate_video creates videos, and live_search performs web searches. The descriptions reinforce these distinct domains, making tool selection unambiguous.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., analyze_image, generate_video, live_search) using snake_case throughout. This predictable naming scheme makes the tool set easy to navigate and understand at a glance.
Tool Count5/5
With 5 tools, this server is well-scoped for its purpose of interacting with xAI's Grok models across different modalities (text, image, video, search). Each tool earns its place by covering a distinct aspect of the AI service without being overly sparse or bloated.
Completeness4/5
The tool set covers core AI functionalities: chat, image analysis/generation, video generation, and web search. A minor gap is the lack of audio-related tools (e.g., speech-to-text or text-to-speech), but agents can work around this given the comprehensive coverage of visual and text domains.
Average 3.2/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool uses 'xAI's vision-capable Grok models', it doesn't describe key behavioral traits like rate limits, authentication requirements, cost implications, error handling, or response format. For a tool with no annotation coverage, this leaves the agent with insufficient operational context.
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 appropriately concise with two clear sentences that efficiently state the tool's purpose and capabilities. It's front-loaded with the core function and avoids unnecessary details. However, it could be slightly more structured by explicitly separating use cases (e.g., 'Use this tool to: 1. Describe images...').
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (image analysis with multiple parameters) and lack of both annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how to interpret results, or any limitations (e.g., image format support, size constraints). For a tool with no structured output documentation, the description should provide more context about expected behavior and results.
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 schema already documents all 4 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain parameter interactions, provide examples, or clarify edge cases. This meets the baseline of 3 when schema coverage is high, but adds no extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze images using xAI's vision-capable Grok models' with specific verbs like 'describe', 'extract text', and 'answer questions'. It distinguishes from siblings like generate_image or generate_video by focusing on analysis rather than creation. However, it doesn't explicitly differentiate from chat or live_search in terms of visual vs. text analysis.
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. It mentions general capabilities but doesn't specify scenarios where analyze_image is preferred over chat for image-related queries or when not to use it. With sibling tools like chat potentially handling multimodal inputs, this lack of differentiation is a significant gap.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions sending messages and receiving responses but lacks details on rate limits, authentication needs, error handling, or response format. For a chat tool with potential API constraints, this leaves significant gaps in understanding operational 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?
The description is extremely concise and front-loaded, consisting of just two sentences that directly state the tool's function. Every word contributes to understanding without redundancy or fluff, making it efficient and easy to parse.
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?
Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage context, and output expectations. With no output schema, the description should ideally hint at response structure, but it doesn't, leaving room for improvement.
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 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as explaining interactions between parameters or typical use cases for optional fields. This meets the baseline for high schema coverage but doesn't enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Chat with xAI's Grok models. Send messages and receive AI-generated responses.' It specifies the action (chat/send/receive) and resource (Grok models), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like analyze_image or generate_image, which prevents a perfect score.
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 live_search or other AI chat tools. It states what the tool does but offers no context about appropriate use cases, prerequisites, or limitations, leaving the agent to infer usage scenarios independently.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return format (image URLs or base64 data) but omits critical details like rate limits, authentication needs, costs, error handling, or generation time. For a generative AI tool, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences that are front-loaded and waste no words. Every sentence directly contributes to understanding the tool's function and output, making it efficient and well-structured.
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?
Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and output but lacks behavioral context, usage guidelines, and deeper parameter insights, leaving gaps for an AI agent to operate effectively.
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 schema fully documents all 5 parameters. The description adds no additional parameter semantics beyond what's in the schema, such as prompt best practices or model differences. Baseline 3 is appropriate when the schema does the heavy lifting.
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 specific action ('Generate images'), resource ('from text descriptions'), and technology ('using xAI's Grok Imagine model'), distinguishing it from sibling tools like analyze_image, generate_video, and chat. It's not a tautology of the name.
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 generate_video or analyze_image. It lacks explicit when/when-not scenarios or prerequisites, offering only a basic functional statement without context.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'real-time' and 'current information', which adds useful context about timeliness. However, it lacks critical details: whether this is a read-only operation, rate limits, authentication needs, response format, or error handling. For a search tool with no annotation coverage, this leaves significant gaps in understanding its 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?
The description is a single, efficient sentence that front-loads the core action ('Perform real-time web search') and includes key details (tool used, sources, purpose). Every word earns its place with no redundancy or fluff, making it easy to parse quickly.
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?
Given the tool's moderate complexity (4 parameters, nested objects) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose and hints at behavior but doesn't fully compensate for missing structured data. For a search tool, users might need more on result format or limitations, but the description provides a functional starting point.
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 schema already documents all parameters thoroughly. The description adds marginal value by mentioning 'real-time' (hinting at date_range relevance) and listing sources (web, news, x/Twitter), which aligns with the 'sources' parameter enum. However, it doesn't provide additional semantics beyond what the schema specifies, such as query formatting tips or source-specific behaviors.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Perform real-time web search using xAI's Grok' with specific resources ('web, news, or X/Twitter') and context ('for current information'). It distinguishes itself from siblings like analyze_image or generate_image by focusing on search rather than content creation or analysis. However, it doesn't explicitly differentiate from 'chat' which might also involve information retrieval.
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 implies usage context ('for current information') and mentions specific sources, suggesting when to use it for real-time data. However, it provides no explicit guidance on when to choose this tool over alternatives like 'chat' (which might handle conversational queries) or when not to use it (e.g., for historical data or non-web sources). The guidance is present but incomplete.
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?
With no annotations provided, the description carries full burden but only mentions capabilities without behavioral details like processing time, rate limits, authentication requirements, output format, or error conditions. It doesn't contradict annotations (none exist).
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 a single, efficient sentence that front-loads the core functionality and lists additional capabilities without wasted words. Every phrase earns its place by clarifying scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex video generation tool with 7 parameters, no annotations, and no output schema, the description is insufficient. It lacks critical context like output format, processing behavior, limitations, or error handling, leaving significant gaps for agent understanding.
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 schema fully documents all 7 parameters. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but no extra value.
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's purpose with specific verbs ('generate', 'animate', 'edit') and resources ('videos', 'images', 'existing videos'), and distinguishes it from siblings like generate_image (static images) and analyze_image (analysis rather than generation).
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 implies usage scenarios (text-to-video, image animation, video editing) but doesn't explicitly state when to choose this tool over alternatives like generate_image for static content or when not to use it. No sibling tool comparisons are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![xai-mcp-server MCP server](https://glama.ai/mcp/servers/joemccann/xai-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/joemccann/xai-mcp-server)

Score Badge

Copy to your README.md:

[![xai-mcp-server MCP server](https://glama.ai/mcp/servers/joemccann/xai-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/joemccann/xai-mcp-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/joemccann/xai-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server