VMware Fusion MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: get_vm_info retrieves detailed VM data, get_vm_power_state focuses only on power status, list_vms enumerates all VMs, and power_vm performs power operations. There is no overlap or ambiguity between these functions.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern with snake_case: get_vm_info, get_vm_power_state, list_vms, and power_vm. The naming is predictable and uniform throughout the set.
Tool Count4/5With 4 tools, the count is reasonable for basic VM management, covering listing, info retrieval, power state checks, and power actions. It might be slightly thin for advanced operations like configuration changes, but it's well-scoped for core functionality.
Completeness3/5The tools cover core VM operations (list, info, power state, power actions), but there are notable gaps for a full VM management domain, such as creating/deleting VMs, modifying settings, or handling snapshots. Agents can perform basic tasks but may hit dead ends for more complex workflows.
Average 3.3/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 2 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 MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool gets 'detailed information' but doesn't specify what that includes (e.g., configuration, status, metadata), whether it's read-only, or any prerequisites like authentication. This leaves significant gaps in understanding the tool's behavior beyond the basic action.
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 directly states the tool's purpose without any fluff. 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.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter) and the presence of an output schema (which handles return values), the description is somewhat complete. However, with no annotations and minimal parameter guidance, it lacks details on behavioral traits and usage context, making it adequate but with clear gaps.
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 input schema has 1 parameter with 0% description coverage, so the schema provides no semantic information. The description adds minimal context by implying 'vm_id' identifies a specific VM, but doesn't explain what format the ID should be (e.g., UUID, name) or where to find it. This partially compensates for the schema gap but remains vague.
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 ('detailed information about a specific VM'), making the purpose unambiguous. It distinguishes from siblings like 'list_vms' (which lists multiple VMs) and 'power_vm' (which controls power state), though it doesn't explicitly mention 'get_vm_power_state' which might overlap in scope.
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?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention that 'list_vms' might be better for discovering VMs, or that 'get_vm_power_state' might be sufficient if only power state is needed. The description implies usage for a specific VM but offers no exclusions or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool gets power state, implying a read-only operation, but doesn't specify if it requires authentication, has rate limits, returns specific error conditions, or details the output format. This leaves significant gaps in understanding the tool's behavior beyond the basic purpose.
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, clear sentence with no wasted words. It's front-loaded with the core purpose and efficiently conveys the essential information without unnecessary elaboration, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no nested objects) and the presence of an output schema (which should document return values), the description is minimally adequate. However, it lacks context on usage guidelines and behavioral details, which are important for a tool that interacts with system resources like VMs, leaving room for improvement.
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 input schema has 0% description coverage, so the description must compensate. It mentions 'a specific VM', which implies the 'vm_id' parameter identifies the VM, but doesn't clarify the ID format, source, or constraints. This adds minimal semantic value beyond what the schema's title ('Vm Id') suggests, meeting the baseline for low schema coverage.
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 ('power state of a specific VM'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_vm_info' (which might include power state among other details) or 'list_vms' (which lists VMs rather than querying a specific one's state), so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 sibling tools like 'get_vm_info' (which might retrieve power state along with other info) or 'power_vm' (which changes power state), leaving the agent to infer usage context without explicit 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action but doesn't describe what 'list' entails—e.g., whether it returns names, IDs, statuses, or pagination details. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and output.
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 directly states the tool's purpose without any wasted words. It is front-loaded and appropriately sized for a simple listing operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, output schema exists), the description is minimally adequate. However, with no annotations and an output schema present, it doesn't explain what the listing includes or how to interpret results, leaving some context gaps despite the structured output.
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 input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details, which is appropriate since there are none. A baseline of 4 is applied for zero parameters, as no compensation is needed.
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 ('List') and resource ('all VMs in VMware Fusion'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like get_vm_info (which retrieves detailed info for a specific VM) or power_vm (which controls power state), but the basic action is unambiguous.
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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention that list_vms returns a broad overview while get_vm_info provides detailed data for a specific VM, or that it might be a prerequisite for operations like power_vm. Usage context is implied but not explicit.
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 what actions are valid. It doesn't disclose behavioral traits like whether actions are immediate or queued, permission requirements, whether 'shutdown' is graceful or forced, side effects on other VMs, or error handling. The description is minimal beyond stating valid actions.
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 with zero waste. It's front-loaded with the core purpose and follows with essential action details, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (power operations on VMs) and no annotations, the description is incomplete. It covers valid actions but lacks crucial context like permissions, side effects, or differences between actions. The presence of an output schema helps, but the description doesn't reference it or explain return values.
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 description adds significant value beyond the 0% schema coverage by explicitly listing all valid action values ('on', 'off', 'shutdown', 'suspend', 'pause', 'unpause'), which the schema doesn't document. However, it doesn't explain the 'vm_id' parameter or provide context about action differences.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('perform a power action') and resource ('on a VM'), specifying the exact scope of operations. It distinguishes from sibling tools like 'get_vm_info' or 'list_vms' by focusing on power state changes rather than information retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for power state changes but doesn't explicitly state when to use this tool versus alternatives like 'get_vm_power_state' for checking status. It provides valid action values but no guidance on prerequisites, error conditions, or when certain actions are appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/yeahdongcn/vmware-fusion-mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server