CloudLab MCP Server

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity. The tools cover different actions on experiments (create, get, list, extend, terminate) and nodes (powercycle, reboot, reload), with clear boundaries between them. An agent can easily tell them apart based on their specific functions.
Naming Consistency5/5
All tools follow a consistent verb_noun naming pattern throughout. The verbs are clear and descriptive (e.g., create_experiment, reboot_node), and the nouns consistently specify the target resource (experiment or node). There are no deviations in style or convention.
Tool Count5/5
With 9 tools, the count is well-scoped for managing CloudLab experiments and nodes. Each tool earns its place by covering essential operations like lifecycle management (create, terminate), status retrieval (get, list), and node control (reboot, reload). This is neither too sparse nor overwhelming for the domain.
Completeness4/5
The tool set provides strong coverage for the CloudLab domain, including CRUD-like operations for experiments (create, get, list, terminate) and comprehensive node management (reboot, powercycle, reload). A minor gap is the lack of tools for modifying experiment configurations or profiles after creation, but agents can work around this by recreating experiments if needed.
Average 3.1/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

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

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

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It implies a write operation ('Create') but doesn't disclose critical traits like required permissions, whether creation is idempotent, typical response time, or what happens on failure (e.g., if the profile doesn't exist).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every element ('new CloudLab experiment from a profile') directly contributes to understanding the tool's 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?
For a creation tool with 5 parameters, no annotations, and no output schema, the description is inadequate. It doesn't cover behavioral aspects (e.g., what the tool returns, error conditions), usage context, or compensate for the lack of structured metadata, leaving significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema (e.g., it doesn't explain relationships between 'profile_project' and 'project', or typical 'bindings' values).
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 ('Create') and resource ('new CloudLab experiment from a profile'), making the purpose immediately understandable. It distinguishes from siblings like 'get_experiment' or 'terminate_experiment' by specifying creation, though it doesn't explicitly differentiate from 'extend_experiment' which also modifies experiments.
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., needing an existing project or profile), when not to use it (e.g., for modifying existing experiments), or refer to sibling tools like 'extend_experiment' for updates.
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 ('Extend') but doesn't describe what happens upon execution—e.g., whether it's idempotent, requires specific permissions, has rate limits, returns a confirmation or updated experiment object, or affects experiment status. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
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 unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place, and there's no redundancy or fluff.
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 a mutation tool (extending an experiment's expiration) with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like side effects, error conditions, or return values, leaving gaps for an AI agent to understand how to use it correctly. The description should do more to compensate for the lack of structured data.
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%, with both parameters ('experiment_id' and 'hours') fully documented in the input schema. The description doesn't add any meaning beyond what the schema provides—it doesn't explain parameter interactions, constraints (e.g., 'hours' must be positive), or default behaviors. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Extend') and the target resource ('expiration time of an experiment'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from potential alternatives like 'renew_experiment' or 'update_experiment_expiry', though no such siblings exist in the provided list. The purpose is unambiguous but lacks sibling differentiation.
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., the experiment must be active or not yet expired), exclusions (e.g., cannot extend beyond a maximum limit), or related tools like 'terminate_experiment' for ending experiments. Usage is implied only by the tool name and description.
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, the description carries full burden but provides minimal behavioral insight. It mentions 'reboot' but doesn't clarify if this is destructive (e.g., interrupts processes), requires specific permissions, has side effects, or details response behavior (e.g., success/failure indicators). This is inadequate 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, efficient sentence with zero wasted words. It is front-loaded with the core action and target, making it highly concise and well-structured for quick understanding.
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 (a mutation affecting multiple nodes), lack of annotations, and no output schema, the description is incomplete. It fails to address critical aspects like safety, permissions, or expected outcomes, leaving significant gaps for an agent to use it correctly.
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 the 'experiment_id' parameter fully. The description adds no additional meaning beyond implying the parameter's role in targeting nodes, aligning with the baseline for high schema coverage.
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 ('reboot') and target ('all nodes in an experiment'), making the purpose immediately understandable. It distinguishes from sibling tools like 'reboot_node' (singular) and 'powercycle_node' (different action), though it doesn't explicitly contrast them.
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 'reboot_node' for individual nodes or 'reload_node' for a different action. The description lacks context about prerequisites (e.g., experiment state) or exclusions, leaving usage ambiguous.
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 ('Reboot') but does not explain what 'reboot' entails (e.g., graceful restart vs. forced, downtime implications, permissions required, or side effects). For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
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 unnecessary words. It is appropriately sized and front-loaded, with zero waste, 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 tool's complexity (a mutation operation with potential side effects), lack of annotations, and no output schema, the description is incomplete. It does not cover behavioral aspects like what happens during the reboot, error conditions, or return values, leaving critical gaps for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (experiment_id and node). The description does not add any additional meaning beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the schema handles 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 ('Reboot') and target ('a specific node in an experiment'), providing a specific verb+resource combination. However, it does not explicitly differentiate from sibling tools like 'reboot_all_nodes' (which reboots all nodes) or 'powercycle_node' (which might have different behavior), leaving some ambiguity in sibling differentiation.
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 does not mention prerequisites (e.g., needing an experiment ID from 'list_experiments'), exclusions (e.g., when not to reboot), or comparisons to siblings like 'reboot_all_nodes' or 'powercycle_node', leaving the agent to infer usage 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Reload/reimage', implying a potentially destructive or resetting operation, but doesn't specify permissions needed, whether data is lost, or other behavioral traits like rate limits or response format.
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 unnecessary words. It's front-loaded and appropriately sized for its 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 the complexity of a reload/reimage operation (likely destructive), no annotations, and no output schema, the description is incomplete. It lacks critical behavioral details like safety warnings, expected outcomes, or error handling, making it inadequate for informed 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 clear documentation for 'experiment_id' and 'node'. The description doesn't add any semantic details beyond what the schema provides, such as explaining the relationship between these parameters or their impact on the reload operation.
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 ('Reload/reimage') and the resource ('a node with its disk image'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'reboot_node' or 'powercycle_node', which likely perform different operations on nodes.
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 'reboot_node' or 'powercycle_node', nor does it mention prerequisites or context. It's a basic statement of function without usage instructions.
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 this is a read operation ('Get'), implying it's non-destructive, but doesn't cover aspects like authentication needs, rate limits, error handling, or what 'detailed status' includes (e.g., format, timestamps). For a tool with zero annotation coverage, this is inadequate.
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 ('Get detailed status of a specific experiment') and adds a key detail ('including node states') without waste. Every word earns its place, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (read operation with one parameter) and no annotations or output schema, the description is minimally complete. It covers the basic purpose but lacks details on behavior, usage context, and return values, leaving gaps that could hinder effective agent invocation.
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 'experiment_id' documented as 'Experiment UUID (from list_experiments)'. The description adds no additional parameter semantics beyond this, such as format details or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 the resource 'detailed status of a specific experiment including node states', which specifies what the tool does. It distinguishes from siblings like 'list_experiments' by focusing on a single experiment's details rather than listing multiple. However, it doesn't explicitly contrast with all siblings (e.g., 'create_experiment'), so it's not a perfect 5.
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., needing an experiment_id from 'list_experiments'), exclusions, or compare to other read operations. This leaves the agent with minimal context for tool selection among 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 the full burden of behavioral disclosure. It states the tool lists experiments but doesn't describe output format, pagination, permissions needed, or other behavioral traits like rate limits or side effects.
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, clearly front-loading the purpose. It's appropriately sized for a simple tool.
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 simple list tool with no parameters and no output schema, the description is minimally adequate. However, without annotations or output details, it lacks completeness in explaining what the list includes or how results are structured.
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, and the schema description coverage is 100%, so no parameter information is needed. The description doesn't add param semantics, but this is acceptable given the lack of parameters, aligning with the baseline for 0 params.
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 resource ('CloudLab experiments'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_experiment' which might retrieve a specific experiment, 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 Guidelines2/5
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 'get_experiment' for retrieving a single experiment. The description implies usage for listing all experiments but lacks explicit context or exclusions.
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 action without behavioral details. It doesn't disclose potential impacts (e.g., service interruption, data loss), permissions required, rate limits, or what happens if the node is unavailable. 'Hard reboot' implies forceful restart but lacks 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 with zero waste. It is front-loaded with the core action and resource, and the parenthetical 'hard reboot' adds useful clarification without verbosity.
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 destructive tool like power cycling with no annotations and no output schema, the description is insufficient. It doesn't cover behavioral risks, success/failure outcomes, or integration with sibling tools (e.g., relationship to 'list_experiments' for getting experiment_id). More context is needed given the tool's potential impact.
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 ('experiment_id' and 'node') adequately. The description adds no additional parameter semantics beyond implying the node is part of an experiment, which is already suggested by the schema. Baseline 3 is appropriate when 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 ('power cycle') and resource ('a node'), with 'hard reboot' providing additional technical clarification. It distinguishes from siblings like 'reboot_node' (likely soft reboot) and 'reboot_all_nodes' (multiple nodes).
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 'reboot_node' or 'reload_node'. The description doesn't mention prerequisites, dependencies, or scenarios where a hard reboot is preferred over other options.
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 effectively communicates the destructive nature ('destroys all data'), which is crucial for a mutation tool. However, it lacks details on permissions, reversibility, or response behavior, leaving some gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of a single sentence that directly states the purpose and includes a critical warning. Every word earns its place, with no unnecessary information or redundancy.
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 (destructive mutation) and lack of annotations or output schema, the description is minimally adequate. It covers the core action and warning but omits details like error handling, confirmation steps, or return values, which could enhance completeness for such a high-stakes 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?
The schema description coverage is 100%, with the parameter 'experiment_id' fully documented in the schema. The description does not add any additional meaning or context beyond what the schema provides, such as format examples or usage tips, so it meets the baseline of 3.
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 ('Terminate') and the resource ('an experiment'), distinguishing it from siblings like create_experiment, extend_experiment, get_experiment, and list_experiments. It uses a precise verb that indicates a destructive operation, which is distinct from the read operations (get, list) and other maintenance actions (powercycle, reboot, reload).
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 usage through the warning 'destroys all data', implying this tool should be used when permanent deletion is intended. However, it does not explicitly state when not to use it (e.g., vs. extend_experiment for modifications) or name specific alternatives, which prevents a score of 5.
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:

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

Score Badge

Copy to your README.md:

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

Latest Blog Posts

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

MCP directory API

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

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ArdaGurcan/cloudlab-mcp'

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