vLLM MCP Server

Server Quality Checklist

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Latest release: v0.1.4
Disambiguation4/5
Most tools have distinct purposes, but there is some potential confusion between vllm_chat and vllm_complete for text generation tasks, and between get_model_info and list_models for model information. Descriptions help clarify, but the overlap could cause misselection in ambiguous scenarios.
Naming Consistency3/5
The naming is mixed with inconsistent patterns: some use verb_noun (e.g., get_model_info, list_models), others use noun_verb (e.g., vllm_chat, vllm_complete), and there are variations like run_benchmark. While readable, the lack of a uniform convention reduces predictability.
Tool Count5/5
With 12 tools, the count is well-scoped for managing a vLLM server, covering model operations, container management, and inference tasks. Each tool appears to serve a specific role without unnecessary duplication, fitting the server's purpose effectively.
Completeness4/5
The tool set provides comprehensive coverage for vLLM server management, including deployment (start/stop/restart), monitoring (status/logs), and usage (chat/completion). A minor gap is the lack of tools for model updates or configuration changes, but core workflows are well-supported.
Average 3.2/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed 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
This repository is licensed under Apache 2.0.
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"
  ]
}
```
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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?
No annotations are provided, so the description carries the full burden. It states this is a read operation ('Get'), implying it's likely safe and non-destructive, but doesn't disclose any behavioral traits such as authentication needs, rate limits, error conditions, or what 'detailed information' includes (e.g., metadata, performance metrics).
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 with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'detailed information' entails in the return value, nor does it address potential complexities like error handling or usage constraints, leaving gaps for an AI agent to infer behavior.
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?
The schema description coverage is 100%, with the single parameter 'model_id' clearly documented in the schema. The description adds no additional meaning beyond implying it's for a 'specific model', which the schema already covers. This meets the baseline of 3 when schema coverage is high.
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 verb 'Get' and resource 'detailed information about a specific model', making the purpose understandable. However, it doesn't differentiate from sibling tools like 'list_models' or 'vllm_status', which might provide overlapping or related information about models.
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. With siblings like 'list_models' (likely listing multiple models) and 'vllm_status' (possibly checking model status), there's no indication of when this specific 'get' operation is preferred or what prerequisites might exist.
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 states what the tool does but doesn't describe how it behaves—such as whether it returns real-time or cached data, what format the output takes, if there are rate limits, or any error conditions. This leaves significant gaps for a tool that interacts with Docker containers.
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 directly states the tool's function without unnecessary words. It's front-loaded with the core purpose and wastes no space, making it easy for an agent to parse quickly.
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 lack of annotations and output schema, the description is incomplete for a tool that lists Docker containers. It doesn't explain what information is returned (e.g., container IDs, statuses, names), how results are formatted, or any behavioral aspects like pagination or error handling, leaving the agent with insufficient context for reliable use.
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?
The input schema has 100% description coverage, with the single parameter 'all' clearly documented in the schema. The description adds no additional parameter information beyond what's in the schema, which is acceptable given the high schema coverage, resulting in a baseline score of 3.
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 action ('List') and target resource ('vLLM Docker containers'), making the purpose immediately understandable. It doesn't differentiate from siblings like 'list_models' or 'get_vllm_logs', but the specificity of 'vLLM Docker containers' provides adequate clarity for a listing operation.
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 'list_models', 'vllm_status', or 'get_vllm_logs'. There's no mention of prerequisites, context for usage, or comparison with sibling tools, leaving the agent to 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 the full burden of behavioral disclosure. It states the action ('Restart') but doesn't explain what this entails (e.g., whether it stops and starts the container, potential downtime, or effects on running processes). No information on permissions, side effects, or error handling is included, which is a significant gap for a mutation 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 a single, direct sentence with zero waste—it states the action and target without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of restarting a container (a mutation with potential side effects), no annotations, and no output schema, the description is incomplete. It lacks details on behavior, outcomes, or error conditions, which are crucial for safe tool invocation in this context.
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?
The input schema has 100% description coverage, with the parameter 'container_name' clearly documented. The description doesn't add any meaning beyond the schema (e.g., it doesn't specify valid container names or examples), so it meets the baseline of 3 where the schema does the heavy lifting.
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 action ('Restart') and target resource ('a vLLM Docker container'), making the purpose immediately understandable. It distinguishes from siblings like 'start_vllm' and 'stop_vllm' by specifying the restart operation, though it doesn't explicitly contrast with them in the text.
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?
No guidance is provided on when to use this tool versus alternatives like 'start_vllm' or 'stop_vllm', or what prerequisites might be needed (e.g., container must be running). The description lacks context about appropriate scenarios or exclusions, leaving usage unclear.
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. While it mentions multi-turn conversation support, it fails to describe important behavioral aspects like authentication requirements, rate limits, error conditions, response format, or whether this is a read/write operation. The description is insufficient for a tool with multiple parameters and no output schema.
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 just two sentences that directly state the tool's purpose and key capability. Every word earns its place, and the information is front-loaded without unnecessary elaboration or redundancy.
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 chat tool with 4 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how errors are handled, authentication requirements, or important behavioral constraints. The description fails to compensate for the lack of structured metadata about this potentially complex interaction.
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?
With 100% schema description coverage, the baseline is 3. The description doesn't add any parameter-specific information beyond what's already documented in the schema. It mentions 'multi-turn conversations' which relates to the messages parameter but doesn't provide additional context about message structure or conversation flow.
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 action ('Send a chat message') and target ('to the vLLM server'), with the specific capability of 'Supports multi-turn conversations' distinguishing it from simpler completion tools. However, it doesn't explicitly differentiate from sibling tools like 'vllm_complete' which might have overlapping functionality.
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 'vllm_complete' or other chat-related tools. It mentions multi-turn conversations but doesn't specify prerequisites, limitations, or appropriate contexts for choosing this tool over others.
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. While 'Check' implies a read-only operation, it doesn't specify what constitutes 'health and status' (e.g., uptime, resource usage, error rates), whether authentication is required, or what the response format might be. This leaves significant 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 a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's perfectly front-loaded and appropriately sized for a simple status-checking tool.
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 status-checking tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what health indicators are checked, what the return values might include, or how to interpret results, leaving the agent with inadequate context for effective use.
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 0 parameters with 100% schema description coverage, so the schema already fully documents the lack of inputs. The description appropriately doesn't add parameter information, maintaining a baseline score of 4 for parameterless tools.
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 as 'Check the health and status of the vLLM server', which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_platform_status' or 'get_vllm_logs' that might also provide status-related information, preventing 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 'get_platform_status' or 'get_vllm_logs'. It doesn't mention prerequisites, appropriate contexts, or exclusions, leaving the agent with minimal usage direction.
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 the tool retrieves information but doesn't specify whether this is a read-only operation, what permissions are needed, if there are rate limits, or what format the output takes. The description is too minimal for a tool with zero annotation coverage.
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 directly states the tool's purpose without any redundant information. It's appropriately sized and front-loaded, with every word contributing value.
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 simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It covers what the tool does but lacks details on output format, error conditions, or behavioral traits. For a status-checking tool, this is the bare minimum to be functional.
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 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description appropriately doesn't mention parameters, which aligns with the schema. A baseline of 4 is applied since no parameters exist and the description doesn't contradict this.
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 with a specific verb ('Get') and resource ('platform information'), and specifies the scope ('including Docker and GPU availability'). It doesn't explicitly distinguish from sibling tools like 'vllm_status' or 'get_model_info', 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 'vllm_status' or 'list_vllm_containers'. It lacks any context about prerequisites, timing, or exclusions, leaving the agent to infer usage from the 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('List all available models') but lacks details on permissions, rate limits, pagination, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 directly states the tool's purpose with zero waste. It's front-loaded and appropriately sized for a simple listing tool, 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 simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate but lacks completeness. It doesn't address behavioral aspects like what 'available' means, potential errors, or how the list is formatted, which could be important for an AI agent to use it correctly in context.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't mention parameters, aligning with the schema. A baseline of 4 is applied as it correctly handles the absence of parameters without adding unnecessary information.
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 verb ('List') and resource ('all available models on the vLLM server'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_model_info' or 'vllm_status', which might provide overlapping or related information about models.
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. With siblings like 'get_model_info' (likely for details on a specific model) and 'vllm_status' (possibly for server status including models), there's no indication of context, prerequisites, or exclusions for this listing operation.
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 offers minimal behavioral insight. It mentions 'performance benchmark' but doesn't disclose what metrics are measured, whether it's destructive (e.g., impacts server performance), authentication needs, rate limits, or output format. The agent lacks crucial context for safe and effective use.
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 purpose without unnecessary elaboration. Every word earns its place by specifying the action, target, and tool used, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (performance benchmarking with 7 parameters) and lack of annotations/output schema, the description is insufficient. It doesn't explain what the benchmark measures, how results are returned, error conditions, or dependencies. For a tool that likely generates significant load, more context is needed for safe operation.
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 parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond implying benchmarking context. This meets the baseline score of 3, as the schema adequately covers parameter details without needing description reinforcement.
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 ('Run a performance benchmark'), target ('against the vLLM server'), and method ('using GuideLLM'). It distinguishes this tool from siblings like 'get_model_info' or 'vllm_status' by focusing on performance testing rather than status retrieval or chat/completion functions.
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 doesn't mention prerequisites (e.g., server must be running), compare it to similar tools (e.g., 'vllm_complete' for single requests), or specify scenarios where benchmarking is appropriate versus unnecessary.
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 states the basic action. It doesn't disclose critical behavioral traits: whether this is destructive (it likely is), what happens if the container doesn't exist, whether it requires specific permissions, or what the response looks like. For a tool that stops containers, this is a significant gap in safety and 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.
Conciseness5/5
Is 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 perfectly front-loaded with the essential information. Every word earns its place in this minimal but complete statement of function.
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 this is a potentially destructive operation with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'stop' entails (graceful shutdown vs force kill), what happens by default (the remove parameter defaults to true), or what the tool returns. For a container management tool, more operational context is needed.
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 all parameters are documented in the schema. The description adds no parameter information beyond what's in the schema. The baseline score of 3 reflects adequate coverage through the schema alone, but the description provides no additional semantic context about parameter interactions or implications.
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 ('Stop') and target resource ('a running vLLM Docker container'), distinguishing it from sibling tools like 'restart_vllm' or 'list_vllm_containers'. It uses precise technical terminology that leaves no ambiguity about what the tool does.
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 'restart_vllm' or 'list_vllm_containers'. It doesn't mention prerequisites (e.g., that a container must be running) or typical use cases (e.g., cleanup, resource management). The agent must infer usage from context 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. It states the tool generates text but fails to mention critical traits: whether it's read-only or mutative, authentication needs, rate limits, error handling, or output format. For a generation tool with potential resource usage, this lack of transparency is a significant gap, though it doesn't contradict any annotations.
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-free. The first sentence states the core purpose, and the second adds contextual usage hints without redundancy. Every word earns its place, making it efficient and well-structured.
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 (generation with multiple parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like safety, performance, or output format, and relies entirely on the schema for parameter details. For a tool in this context, more comprehensive guidance is needed to ensure proper agent usage.
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%, providing full parameter documentation in the structured schema. The description adds no parameter-specific semantics beyond the schema, such as explaining prompt formatting, model selection implications, or temperature effects. This meets the baseline of 3 since the schema handles the heavy lifting, but the description doesn't compensate with additional insights.
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 verb ('Generate') and resource ('text completion using vLLM'), making the purpose evident. It distinguishes from siblings like vllm_chat by specifying 'completion' rather than conversational interaction, and from monitoring/management tools (e.g., vllm_status, restart_vllm) by focusing on generation. However, it doesn't explicitly contrast with run_benchmark or list_models, which slightly limits differentiation.
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 implied usage guidance with 'Good for code completion and text generation,' suggesting appropriate contexts. However, it lacks explicit when-to-use vs. when-not-to-use criteria, doesn't mention alternatives like vllm_chat for conversational tasks, and offers no prerequisites or constraints. This leaves gaps in operational guidance.
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 mentions the purpose ('check loading progress or errors') but doesn't describe what the logs contain, format, whether they're real-time or historical, authentication requirements, rate limits, or error conditions. For a read operation with zero annotation coverage, this leaves significant 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 a single, efficient sentence that communicates the core purpose without unnecessary words. It's appropriately sized for a simple tool and front-loads the essential information ('Get logs from a vLLM container') followed by the specific use case.
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 2 parameters with full schema coverage but no annotations and no output schema, the description provides basic purpose but lacks behavioral context needed for a logging tool. It doesn't explain what the logs contain, their format, or how to interpret them for 'checking progress or errors.' For a tool that presumably returns textual log data, more guidance would be helpful.
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 both parameters ('container_name' and 'tail') with their types and default. The description doesn't add any parameter-specific information beyond what's in the schema, such as examples of container names or clarification about tail behavior. Baseline 3 is appropriate when schema does the documentation work.
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 action ('Get logs') and target resource ('from a vLLM container'), with a specific purpose ('to check loading progress or errors'). It distinguishes from siblings like 'vllm_status' or 'get_platform_status' by focusing on container logs rather than status information. However, it doesn't explicitly differentiate from potential log-related tools that might exist in the future.
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 ('to check loading progress or errors') but doesn't provide explicit guidance on when to use this tool versus alternatives like 'vllm_status' for general status or 'list_vllm_containers' to identify containers first. No exclusions or prerequisites are mentioned, leaving the agent to infer appropriate usage scenarios.
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 provided, the description carries the full burden. It discloses behavioral traits like automatic platform/GPU detection and Docker container usage, but lacks details on permissions, side effects (e.g., resource consumption), error handling, or what happens if a container already exists. It's adequate but has gaps for a complex 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 two concise sentences with zero waste—it states the core action and key behavioral traits (platform detection, GPU handling) without redundancy. It's front-loaded with the main purpose, making it easy to scan and understand 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 complexity (9 parameters, no output schema, no annotations), the description is minimally complete. It covers the basic action and some behavioral context but lacks details on output (e.g., what's returned after starting), error scenarios, or dependencies. It's adequate but could be more informative for such a multifaceted 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 100%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining interactions between parameters (e.g., 'gpu_memory_utilization' with 'cpu_only'). Baseline 3 is appropriate as 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 ('Start a vLLM server in a Docker container') and resource (vLLM server), distinguishing it from sibling tools like 'stop_vllm', 'restart_vllm', or 'vllm_status' which have different purposes. It also mentions platform detection and GPU availability, which adds specificity.
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 usage context by mentioning platform detection and GPU availability, suggesting it's for initializing a vLLM server. However, it doesn't explicitly state when to use this tool versus alternatives like 'restart_vllm' or 'vllm_status', nor does it provide exclusions or prerequisites beyond what's implied.
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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