grok-mcp

by jun9100

Overview Schema Source Related Servers Score Discussions Admin

Python

Hybrid

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools target distinct actions (generate, edit, chat, search, video lifecycle). list_models and list_console_catalog overlap slightly but are differentiated by static vs dynamic sources, and get_video_status vs wait_video serve separate polling vs blocking needs.
Naming Consistency4/5
The majority follow a consistent snake_case verb_noun pattern (list_models, generate_image, wait_video). 'chat' and 'web_search' are minor deviations but remain clear and predictable.
Tool Count5/5
9 tools are well-scoped for the server's purpose of exposing Grok chat, web search, image, and video capabilities. Each tool contributes a distinct function without unnecessary bloat.
Completeness5/5
The tool surface covers the full lifecycle for its domain: chat, web search, image generation/editing, and video creation (submit, poll, wait/download). No obvious missing operations are evident.
Average 3.9/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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"
  ]
}
```
Then . Browse examples.
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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?
With no annotations, the description carries the full burden for behavioral disclosure. It does not mention whether the tool is stateless, the response format, any side effects, or rate limits. It only lists models and the optional web search flag, leaving significant behavioral traits undisclosed.
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 concise and front-loaded, opening with a clear purpose statement. The model list adds length but each item is informative. It could be slightly more compact, but it avoids unnecessary fluff.
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?
For a relatively simple tool with an output schema, the description covers the core purpose and model options. However, it omits any guidance on conversation history or response behavior, and does not mention system_prompt at all. It feels adequate but not fully complete.
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 0%, so the description must compensate. It clarifies the default model (grok-4.20-0309-non-reasoning) and lists supported model variants, which adds meaning beyond the schema. However, it does not explain system_prompt or provide detail on prompt semantics beyond the schema titles.
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 performs 'console chat completion via Grok2API POST /v1/chat/completions', which distinguishes it from sibling tools like generate_image or web_search. It specifies the exact resource and action, making the purpose unambiguous.
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 context about default and supported models and mentions optional web search, but it does not explicitly state when to use this tool versus alternatives like web_search. Usage is implied rather than directly guided, and there are no exclusionary statements.
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?
Since no annotations are provided, the description carries the full burden for behavioral disclosure. It reveals key behavior: the poll is one-shot, status values, and the conditional download to local storage when done and materialize=true, returning public_url. However, it omits the behavior of the delete_remote parameter and does not explain the outcome when materialize=false or when the job is not done. This leaves important behavioral gaps.
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: it opens with the core action, then lists statuses, and then the key conditional side effect. Every sentence adds value and there is no filler. It is well-structured for quick comprehension.
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?
The tool has three parameters, one conditional behavior, and an output schema that is not described. The description covers the main flow but leaves delete_remote and the non-materialized path unexplained. Given the moderate complexity and the absence of annotations, the description is not fully complete, though it covers the most critical behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With schema description coverage at 0%, the description must compensate for all three parameters. It only explains the role of materialize in the conditional download, and implicitly references request_id via 'a video job' without naming it. The delete_remote parameter is not mentioned at all, leaving its meaning and effect unexplained. This is incomplete compensation for the schema's lack of 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 starts with the specific verb 'Poll' and the resource 'a video job once', clearly stating the one-shot polling nature. It also lists the possible statuses (pending|done|failed), which distinguishes it from the sibling tool wait_video that likely waits for completion. This is a clear and specific purpose.
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 via 'Poll ... once', suggesting a single status check rather than waiting. However, it does not explicitly mention when to use this tool versus wait_video or other alternatives, and does not provide any exclusions or context for alternating between tools. Guidance is implied but not stated.
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 must disclose behavioral traits. It does reveal the HTTP method (GET /v1/models), implying a read-only network call, and 'currently exposes' indicates the data is dynamic. However, it does not mention failure modes, rate limits, authentication, or how the two model lists are merged, leaving some transparency gaps.
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, well-structured sentence that front-loads the action and resource. Every word earns its place, providing the two key data sources without unnecessary verbosity.
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?
This is a simple zero-parameter list tool with an output schema present, so return values are already specified. The description adequately explains the two sources of models, making it complete for the tool's simplicity. No additional context about pagination or errors is strictly required here.
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 zero parameters, and the schema is empty. Per rubric, a zero-parameter tool receives a baseline of 4 since there are no parameter semantics to clarify. The description adds no parameter information, but none is needed.
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 lists Grok Console models targeted by the MCP plus models from the connected Grok2API gateway via GET /v1/models. This gives a specific resource and scope, but it does not explicitly differentiate from the sibling tool 'list_console_catalog', which may overlap in purpose.
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 when to use the tool (when a list of models from the MCP-targeted console or gateway is needed), but it provides no explicit guidance on when to prefer this over alternatives like 'list_console_catalog'. There is no mention of exclusions or conditions, so the context is only implied.
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 burden. It discloses that the tool uses chat completions with web_search enabled and lists compatible models, implying it performs external web searches. However, it does not discuss safety, rate limits, or whether any modifications occur, though the non-destructive nature of search is assumed.
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 two sentences long, front-loaded with the primary purpose and followed by model options. No redundant information.
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, return values are covered. The description provides model compatibility but lacks guidance on when to use this tool vs alternatives, which is a gap. Overall, it's adequate for a simple search tool.
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 0%, so the description must compensate. It partially does by specifying default and alternative model values for the 'model' parameter, but it does not explain the 'system_prompt' parameter or add format details for 'query' 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 uses specific verbs 'Search / research' and identifies the resource as 'Grok2API chat completions with hosted web_search enabled', clearly distinguishing it from the sibling 'chat' tool by explicitly mentioning web_search.
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 by stating it's for search/research via chat completions with web_search, but it does not explicitly describe when to use this instead of other methods like the 'chat' tool. It provides model selection guidance but no exclusions or alternative suggestions.
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 behavioral disclosure burden. It discloses key behaviors: asynchronous execution, immediate request_id return, creation of a remote gallery job that can be optionally deleted, and file download step via wait_video. However, it omits details like potential costs, rate limits, or what happens if generation fails.
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 relatively concise, with key information front-loaded. The first sentence states the core action and immediate return. The second sentence condenses the follow-up workflow. The parameter list is terse but clear. The run-on nature of the second sentence reduces clarity slightly, but overall it's 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?
The description covers the essential workflow, key constraints, and next steps. It mentions the async nature, immediate request_id, wait_video handoff, and optional deletion. Since an output schema exists, it needn't detail return values. It could add more on prerequisites (e.g., Console context) but is sufficient for an agent to invoke correctly in most cases.
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 0%, so the description must compensate. It adds meaning for duration (1-15s, default 8), aspect_ratio (list of formats), and resolution (480p/720p/1080p). However, it doesn't explain image_urls, model, or prompt beyond obvious inference, leaving some ambiguity for less obvious usage.
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 identifies the tool's function: 'Create an asynchronous Console video job via Grok2API' with a specific model. It distinguishes itself from siblings by mentioning 'Returns request_id immediately' and referencing wait_video for downloading, implying a distinct creation role.
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?
It explicitly advises the next step: 'Then call wait_video to download the finished file...', providing a clear workflow. It also sets expectations for immediate return of request_id. However, it doesn't explicitly state when not to use it or contrast with generate_image, though the asynchronous and video-specific focus implies usage context.
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 and does well: it discloses blocking behavior, the download destination, the returned URL format, and the conditional deletion of the remote job. It stops short of mentioning failure modes or side effects of non-deletion, but covers the key behavioral traits.
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 three sentences and front-loads the primary action (block, download, return). It is efficient and doesn't waste words, though the last sentence about timeout could be integrated or shortened without losing meaning.
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?
The tool has 5 parameters and no annotations, and the output schema exists so return values don't need explanation. The description covers the core workflow but leaves parameter semantics for materialize, delete_remote, and poll_interval unclear, which is a significant gap for such a parameterized tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate for all 5 parameters. It only explains the timeout environment default; request_id, materialize, delete_remote, and poll_interval are not described. The parameter semantics are mostly absent from the description.
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 uses a specific verb ('Block until a video job finishes') and identifies the resource (video job) and the resulting artifact (mp4 download with public_url). It clearly differentiates from the sibling tool get_video_status by describing the blocking and download behavior.
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 clear context: this tool is used when you need to wait for a video job to complete and obtain the mp4. However, it does not explicitly state when not to use it or name alternatives like get_video_status for just checking status without downloading.
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 of behavioral disclosure. It mentions downloading results locally, returning MCP Image content, and critically, 'optionally delete remote gallery assets after success' – a potentially destructive side effect. This is transparent about the tool's major behaviors, though it omits details like authentication requirements or error handling.
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 two sentences, front-loaded with the core purpose, and contains no filler. Every clause adds information: editing, downloading, returning MCP content, optional deletion, and URL constraints. It is a model of concise, well-structured tool documentation.
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?
The description gives a reasonable overall workflow (edit, download, return, optionally delete) and specifies the input constraint of 1-8 publicly accessible URLs. However, with no output schema, the return type is only vaguely described as 'MCP Image content', and several parameters remain unaddressed. The sibling list provides context, but the tool's full behavior is not completely specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate for eight undocumented parameters. The description adds meaning for image_urls ('1-8 publicly reachable image URLs') and implicitly for delete_remote ('optionally delete remote gallery assets'), but leaves model, size, quality, resolution, and aspect_ratio unexplained. This is insufficient for a tool with this many 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 starts with the specific verb 'Edit existing image(s)', clearly indicating the tool's function of modifying existing images rather than creating new ones. It also mentions the optional deletion of remote assets, which adds a distinctive behavioral scope that separates it from siblings like generate_image.
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 phrase 'Edit existing image(s)' and 'Provide 1-8 publicly reachable image URLs' establishes clear usage context: users should call this when they have existing images to modify. It does not explicitly name alternatives or state when not to use it, but the contrast with sibling tools like generate_image is implicit.
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 burden. It discloses the download-to-host flow, remote asset deletion when admin credentials are set, default model behavior, and quality flag. This goes beyond the schema, though it could expand on auth prerequisites and the effect of n.
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 three sentences, starts with the core purpose, then packs default behavior, cleanup logic, and aspect ratio examples. 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 7-parameter surface and no annotations/output schema, the description is quite informative. It covers the main workflow, key defaults, and side effects. However, it leaves some parameter semantics (e.g., n, resolution) vague and does not detail output structure beyond mentioning MCP Image content.
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 0%, so the description must compensate. It adds meaning for model, quality, and aspect_ratio (with examples), but leaves n, resolution, and delete_remote under-specified, making it a partial compensation at best.
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 action (generate images), the resource (Grok2API Console models), and the follow-through (download to host, return MCP Image content). This separates it from siblings like edit_image or generate_video.
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 clear context on how the tool works, including model defaults and quality switching, but it does not explicitly state when to prefer this over alternatives such as edit_image.
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 that the catalog is 'static' and that the tool works 'without calling the gateway', implying a read-only, local operation with no side effects. It does not detail auth or error behavior, but for a simple catalog read 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 a single sentence that front-loads the primary action and resource, contains no filler, and is immediately understandable.
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 (0 parameters), the presence of an output schema, and the clear scoping to chat/image/video with the 'without calling the gateway' behavior, the description is complete for an agent to select and invoke the tool 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?
The tool has zero parameters, so the baseline of 4 applies. The description correctly avoids parameter details since there are none to document.
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 uses the specific verb 'Return' and names the resource ('static Grok Console model catalog') with explicit scope (chat/image/video). It distinguishes itself from the sibling 'list_models' and other tools by noting 'without calling the gateway', making its purpose unmistakable.
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 clear context for when to use this tool: when you need the static catalog without hitting the gateway. It does not explicitly name alternatives or exclusions, but the phrase 'without calling the gateway' implies a usage scenario different from tools that do call the gateway.
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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