Grok MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: create_chat_completion and create_completion handle different types of text generation, create_embeddings is for vector representations, get_model retrieves specific model details, and list_models shows all available models. The descriptions reinforce these distinctions, making misselection unlikely.
Naming Consistency5/5All tools follow a consistent verb_noun pattern (e.g., create_chat_completion, list_models) using snake_case throughout. The naming is predictable and readable, with no deviations in style or convention across the set.
Tool Count5/5With 5 tools, the count is well-scoped for a Grok API server, covering core operations like completions, embeddings, and model management. Each tool earns its place without feeling excessive or insufficient for the domain.
Completeness4/5The tool set provides good coverage for a Grok API domain, including text generation, embeddings, and model listing/retrieval. A minor gap exists in lacking update or delete operations for models or completions, but agents can work around this for typical workflows.
Average 2.9/5 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 status not available
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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
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure but offers none. It doesn't mention that this is a write operation (creates something), potential costs/rate limits, authentication requirements, response format, or any side effects. 'Create' implies mutation but this isn't explicitly stated or explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states exactly what the tool does without unnecessary words. It's appropriately sized and front-loaded with the essential information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 17 parameters, no annotations, and no output schema, the description is severely inadequate. It doesn't explain what a 'chat completion' actually is, what the Grok API provides, what the response looks like, or any behavioral characteristics. The agent would need to rely heavily on the schema alone.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, all 17 parameters are documented in the schema itself. The description adds no parameter-specific information beyond what's in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create') and resource ('chat completion') with the specific API ('Grok API'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'create_completion' - both appear to create completions, so the distinction between 'chat' vs regular completions isn't explained.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 'create_completion' or 'list_models'. There's no mention of prerequisites, appropriate contexts, or exclusion criteria. The agent must infer usage from the tool name alone.
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 for behavioral disclosure but offers minimal information. It states the tool creates completions but doesn't mention whether this is a read or write operation, potential costs/rate limits, authentication requirements, or what the output looks like. For a 17-parameter tool with complex behavior, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a tool with extensive schema documentation and gets straight to the point with zero wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex text generation tool with 17 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what a 'completion' actually returns, doesn't mention typical use cases, and provides no behavioral context beyond the basic action. The agent would need to rely heavily on the schema alone to understand this tool's functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with all parameters well-documented in the schema itself. The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline expectation but doesn't provide extra value. The description doesn't explain relationships between parameters or provide usage examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create a text completion') and target ('with the Grok API'), providing a specific verb+resource combination. However, it doesn't differentiate this tool from its sibling 'create_chat_completion' which suggests a similar purpose but for chat-based interactions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 'create_chat_completion' or 'create_embeddings'. There's no mention of appropriate contexts, prerequisites, or exclusions that would help an agent choose between these text generation options.
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 but offers minimal information. It states the tool creates embeddings but doesn't mention authentication requirements, rate limits, cost implications, or what the output looks like. For a tool that likely involves API calls and computational resources, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point without any fluff. It's appropriately sized for a tool with a clear purpose and well-documented schema, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an embedding tool with no annotations or output schema, the description is incomplete. It doesn't explain what embeddings are used for, potential limitations, error handling, or return format, leaving significant gaps for an agent to understand the tool's full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific information beyond what's already in the schema, which has 100% coverage with detailed descriptions for all 5 parameters. The baseline score of 3 reflects that the schema adequately documents parameters, but the description doesn't enhance understanding with examples or contextual usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('create embeddings') and target resource ('text with the Grok API'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like create_chat_completion or create_completion, which also involve the Grok API but for different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 create_chat_completion or create_completion. There's no mention of use cases, prerequisites, or exclusions that would help an agent decide between similar tools.
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 for behavioral disclosure. It states it 'gets details' which implies a read-only operation, but doesn't specify what details are returned, whether authentication is required, if there are rate limits, or how errors are handled. The description is too minimal for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 5 words with zero wasted language. It's front-loaded with the core purpose and appropriately sized for a simple retrieval tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'details' are returned, the format of the response, or any behavioral aspects. For a tool that presumably returns structured model information, more context about the return value would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100% with the single parameter 'model_id' well-documented in the schema. The description adds no additional parameter semantics beyond what's in the schema, but since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('details about a specific model'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from its sibling 'list_models' which retrieves multiple models rather than a specific one, though this distinction is somewhat implied by 'specific model'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 doesn't mention the sibling 'list_models' for retrieving multiple models or explain that this tool is for getting detailed information about a single identified model versus creating completions with other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe return format, pagination behavior, rate limits, authentication requirements, or any side effects. The description is minimal and lacks essential 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without any wasted words. It's appropriately sized for a simple listing tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is insufficiently complete. For a tool that presumably returns a list of models, the description should at minimum hint at the return format or structure. The current description provides only basic purpose without operational context needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the schema already fully documents the input requirements. The description appropriately doesn't discuss parameters since none exist, which is correct for this case. Baseline 4 applies for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List all models') and the resource ('available for use with the Grok API'), providing specific verb+resource pairing. However, it doesn't explicitly differentiate from the sibling 'get_model' tool, which appears to retrieve a specific model rather than list all available ones.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 'get_model' or other sibling tools. It lacks context about prerequisites, typical use cases, or any explicit when/when-not instructions.
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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