VMware-Monitor

Server Quality Checklist

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

Latest release: v1.7.7
Disambiguation4/5
Most tools target distinct resources or aspects (sessions, tasks, certificates, datastores, etc.), but there is minor overlap between list_all_datastores and datastore_capacity, and between vm_info and vm_list_snapshots. Descriptions help differentiate.
Naming Consistency4/5
Naming is mostly consistent with a verb_noun or noun_verb pattern, though some tools use 'get_' while others use 'list_' or 'active_'. Patterns are predictable enough for an agent.
Tool Count5/5
22 tools is well-scoped for a comprehensive VMware monitoring server, covering many facets of the infrastructure without being excessive.
Completeness5/5
Covers a wide range of monitoring needs: sessions, tasks, alarms, events, hardware, services, logs, performance, datastores, clusters, networks, VMs, snapshots, NTP, licenses, certificates, and resource pools. No obvious gaps for a read-only monitor.
Average 4.6/5 across 22 of 22 tools scored. Lowest: 4/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 50 commits in the last 12 weeks
- Last stable release on August 6, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds content details (host count, DRS/HA, resource totals) and clarifies that 'target' defaults to a configured default. No additional behavioral traits (e.g., permission requirements) are disclosed, but the description does not contradict 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 a clear one-line summary followed by parameter details in bullet form. No superfluous text, and the structure front-loads the purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description adequately covers what the tool returns (host count, DRS/HA, resource totals). It does not discuss pagination or edge cases, but for a simple list tool with two optional parameters, this is sufficient.
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?
Schema coverage is 0%, so the description must carry the burden. It explains 'target' as an optional vCenter/ESXi name with a default, and 'limit' as a maximum number of clusters (None for all). This provides meaningful guidance beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool lists clusters with host count, DRS/HA status, and resource totals. This distinguishes it from sibling tools that list datastores, hosts, or VMs. The '[READ]' prefix further clarifies the operation type.
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 for retrieving cluster information but does not explicitly state when to use this tool over alternatives like list_all_datastores or list_esxi_hosts. No exclusion criteria or selection guidance is provided.
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?
Annotations already declare readOnly, idempotent, and non-destructive. The description adds that it returns one row per license with specific fields (including unlimited flag condition total==0), which provides behavioral context beyond 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 concise with two short paragraphs, front-loaded with '[READ]', and every sentence adds value: purpose, return format, usage context, and parameter explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, usage, return values, and parameter. It is fairly complete for a simple read tool, though it lacks mention of error handling or prerequisites. The presence of an output schema reduces the burden, but the description adds useful detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'target' has 0% schema description coverage, but the description fully compensates: it states it is optional, comes from config, and uses a default if omitted. This adds clear meaning beyond the schema.
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 states it is a read operation for vCenter/ESXi license inventory with usage and expiry, and lists the returned fields. It clearly identifies the resource and action, though it does not explicitly distinguish from sibling tools, several of which are also inventory reads.
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 gives a clear use case: 'catch over-allocation or an approaching license expiry.' However, it does not mention when not to use it or provide alternatives from the sibling tool list, leaving the agent to infer exclusion criteria.
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?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive nature. The description adds context about filtering by severity and time range, but does not conflict with annotations. No additional behavioral traits like rate limits or auth needs are disclosed, but annotations cover the main safety profile.
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 concise with a clear header and bulleted args. Every sentence adds value, no redundancy. Well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (3 params, no required), rich annotations, and existence of an output schema, the description is fairly complete. It lacks mention of result limits or pagination, but that is minor for a filtering tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates by explaining each parameter: hours (default 24), severity (values 'critical', 'warning', 'info'), and target (optional, uses config default). This adds meaning beyond the schema's basic type and default.
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 it retrieves recent vCenter/ESXi events filtered by severity. The [READ] prefix and 'Get' verb specify the action and resource. It is distinct from sibling tools like get_alarms and list_* tools which deal with different resource types.
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 explains how to use parameters (hours, severity, target) and their defaults, but it does not explicitly state when to use this tool over siblings or when not to use it. Usage context is implied rather than explicit.
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?
Annotations already convey readOnly, openWorld, idempotent, and non-destructive traits. Description adds minimal extra behavioral context beyond listing the returned fields.
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 concise and well-structured: a brief purpose line followed by a clear Args section. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with two optional parameters and an output schema, the description covers all necessary context: output fields and parameter behavior.
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?
Despite 0% schema description coverage, the description explains both parameters (target as optional name, limit as max count) with clear semantics, compensating for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists datastores with specific attributes (capacity, free space, type, VM count), distinguishing it from sibling tools focusing on other resources.
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 explains parameters but does not explicitly contrast with sibling tools; however, sibling tools cover different resource types, making usage context clear.
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?
Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds behavioral details: powered-off VMs are skipped, point-in-time only, and that limit=None returns all VMs. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a summary line followed by details and parameter descriptions. It front-loads the purpose and uses clear language. Slightly verbose with the Args section, but still efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity and existence of an output schema, the description covers key aspects: returned fields, ordering, scope (powered-on, busiest first), and limit behavior. It does not detail error cases or output format, but output schema handles that.
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?
Schema coverage is 0%, so the description must compensate. It explains vm_name for filtering single VM, target as optional vCenter, and limit with default and usage of None. This adds meaningful semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads real-time CPU/memory/disk/network utilization per virtual machine, specifying the resource (VM), verb (READ), and scope (powered-on only, busiest first). It distinguishes from trend tools and siblings like host_performance by focusing on VM-level metrics.
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: when to use (real-time utilization of powered-on VMs), when not (point-in-time only, use metrics store for trends), and default behavior (top 25, limit=None for all). It does not explicitly compare with siblings but the VM-specific focus implies differentiation.
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?
Description adds that reads are batched, cheap on large fleets, and aggregation happens inside the tool, going beyond annotations. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is somewhat lengthy but front-loaded with a clear purpose. Each paragraph adds value: functionality, usage guidance, parameters. Could be slightly more concise but well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and complex output, the description covers inputs, behavior, aggregation, non-trending nature, and relationship to other tools. Adequately complete for an investigative bundle.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, description fully explains each parameter: vm_name (exact name, error handling, source), target (optional, default), hours (look-back window, default 24). Adds critical context missing from schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it collects and correlates everything around a single VM, listing specific data points (state, host, cluster, datastores, snapshots, alarms, performance, event timeline). It distinguishes itself from stitching multiple separate calls.
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?
Explicitly suggests using after cluster_health_summary points to a problem VM or when asked 'what's going on with <vm>?'. Notes it's a point-in-time snapshot, not for trending. Could specify when NOT to use, but the guidance is clear.
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?
Adds useful behavioral info (empty list for no snapshots) beyond annotations. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with [READ] tag and logical sections. Slight clutter from internal issue reference but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers output format and parameter semantics adequately. With output schema present, return values are fully specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Provides clear definitions for both parameters (exact name, optional target with default) where schema had only titles and types.
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?
Clearly states it lists snapshots of a VM with nesting hierarchy. Provides detailed output structure. Distinguishes from siblings as the only snapshot-specific tool.
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?
Explicitly notes read-only nature and directs mutating operations to vmware-aiops. Does not explicitly state 'use when listing snapshots' but that is clear from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, destructiveHint false, idempotentHint, openWorldHint true. The description adds a '[READ]' tag and explains that an empty list may occur for nested ESXi, which is helpful context beyond annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with a header line, field enumeration, usage tip, edge case, and parameter explanations. It is moderately sized; slightly more concise could be achieved, but it remains coherent and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 2 optional parameters, rich annotations, and an output schema (fields listed in description), the description fully covers the return fields, edge cases, and parameter usage. No gaps remain for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 0% description coverage, but the tool description includes an 'Args' section that explains both parameters: target (optional, uses default) and limit (max rows, None = all). This adds essential semantics not found in schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action 'Get', the resource 'hardware sensor status', and scope 'for all hosts'. It also distinguishes the tool from its siblings (alarms, events, services, etc.) by focusing on sensor data, which is unique among them.
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?
Provides explicit use case 'spot failing hardware before it causes an outage' and an edge case 'Returns empty list when no host exposes sensor data (e.g. nested ESXi).' However, it does not contrast with alternatives or specify when not to use.
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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds value by specifying the output fields (CPU cores, memory, version, VM count, uptime) and the config-based target resolution. No contradiction with annotations. Could mention if results are sorted or if pagination applies, but not critical for a simple list.
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?
Very concise: two sentences plus a two-line Args block. The first line is a high-level summary. No wasted words. Front-loaded with the action and output details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given two simple optional parameters and an existing output schema, the description provides enough context to use the tool correctly. It explains the purpose and parameters. Could mention error handling or ordering, but not essential for a basic list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, so the description fully carries parameter semantics. It explains target as an optional vCenter/ESXi name from config (default used if omitted) and limit as max hosts to return (None=all). This adds essential meaning beyond the bare type definitions.
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 it lists ESXi hosts with specific attributes (CPU cores, memory, version, VM count, uptime). The verb 'List' and resource 'ESXi hosts' are explicit, and the [READ] prefix reinforces the read-only nature. Sibling tools like get_alarms, list_virtual_machines are distinct, so no confusion.
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 explains the optional parameters target and limit, indicating that target defaults to a config value and limit sets max results. While it doesn't explicitly state when not to use this tool, the sibling names make the domain clear (hosts vs alarms, VMs, etc.). Some guidance on when to use alternatives would improve score to 5.
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?
While annotations already indicate read-only, idempotent, non-destructive, description adds behavioral details: requires Sessions privilege, behavior for low-privilege accounts (single explanatory row instead of traceback), and lists return fields.
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?
Concise yet complete: one-line header, explanatory sentences, then clean parameter bullet list. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers return fields, privilege requirements, edge cases (low-privilege), and parameter usage. Has output schema, so return format is handled. Complete for a read-only list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but description fully explains both parameters: target (optional, uses default) and limit (max rows, None = all). Adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb ('READ') and resource ('currently authenticated vCenter/ESXi sessions'). Distinguishes from sibling tools like active_tasks or certificate_status by focusing on session login info.
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?
Explicitly states read-only nature and that terminating sessions is not supported. Provides context about low-privilege accounts. Lacks direct comparison to siblings, but usage context is clear.
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?
Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds that each alarm includes 'suggested_actions' with remediation hints, providing useful behavioral context beyond annotations. No contradiction.
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 concise: two short paragraphs covering purpose, return value, and parameter semantics. No extraneous information. Front-loaded with the [READ] tag and clear verb.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (2 optional params, output schema exists), the description is complete. It explains what the tool returns (alarms with suggested_actions), parameter usage, and behavior. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining target (optional, uses default) and limit (max number, use when many alerts). This adds significant meaning beyond the type and default in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'active/triggered alarms' with scope 'across the VMware inventory'. It distinguishes from sibling tools like get_events (events vs alarms) and list_ tools (different resources). The [READ] tag further clarifies the operation.
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 usage guidance for both parameters: target (optional, uses default) and limit (use when many alarms). However, it does not explicitly compare to alternatives like get_events, so the when-to-use context is slightly lacking. Still, the purpose is clear.
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?
Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds value by detailing the output structure and filtering parameters (host_name, target). It does not contradict annotations and provides sufficient behavioral context for the agent.
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 concise, front-loaded with the [READ] tag and core purpose, and organized in a clear docstring format. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (two optional parameters, read-only query), the description covers all necessary aspects: purpose, return fields, parameters, and example use. An output schema exists and the description mentions return fields, so completeness is high.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero description coverage, but the description fully compensates by explaining each parameter: host_name filters to a single host by exact name (None = all hosts), target is an optional vCenter/ESXi target name. This adds significant meaning beyond the schema's type and default information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get host service status (running state and startup policy).' It specifies the exact fields returned (host, service key, label, running state, policy) and gives concrete examples (SSH, NTP, firewall). This effectively distinguishes it from sibling tools like get_alarms or get_host_sensors.
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 usage guidance by stating 'Use to check whether SSH, NTP, or the firewall service is in the expected state.' This gives a clear context. However, it does not explicitly mention when not to use it or compare directly to siblings, but the use case is well-defined.
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?
Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds context about the returned fields and parameter behavior, which aligns with annotations. No additional behavioral traits are hidden.
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 concise: a one-sentence purpose followed by a bullet list for parameters. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only list tool with an output schema, the description covers purpose, parameters, and output fields. Annotations handle safety and idempotency, making the definition complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description fully compensates by explaining the meaning and defaults of both 'target' and 'limit' parameters in clear language.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists networks and specifies the information returned (name, attached VM count, accessibility). It is distinct from sibling tools which target different resources (clusters, datastores, etc.).
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 explains the parameters (target and limit) with clear semantics. Although it does not explicitly state when to use this tool versus alternatives, the sibling tools are for different resources, so usage context is clear.
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?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds useful behavioral details: sorted by memory usage descending, meaning of -1 for unlimited, and optional parameters. This complements annotations well without contradiction.
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 concise (under 100 words) and front-loaded with the purpose and key return fields. It uses clear bullet-like formatting for fields and separates parameter documentation. No extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of annotations (safety, idempotency) and an output schema, the description covers all needed aspects: what is returned, sorting, parameter details, and usage intent. It is complete for a read-only monitoring tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (no descriptions in schema), but the description fully explains both parameters: target (optional, uses default) and limit (max rows, None=all). This adds significant meaning beyond the raw schema properties.
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 it returns per-resource-pool CPU/memory metrics. The verb 'returns' and explicit field list make the tool's function unambiguous. It is distinct from sibling tools like host_performance or vm_performance because it specifically targets resource pools.
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 explicitly states 'Use to spot pools near their reservation/limit', providing a clear use case. However, it does not mention when not to use this tool or suggest alternatives (e.g., for VM-level metrics). With many sibling tools, explicit guidance would improve the score.
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?
Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds value by specifying the types of information returned (CPU, memory, etc.) and the parameter defaults, providing useful behavioral context beyond 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 very concise: a two-sentence overview plus bullet-style parameter explanations. Every sentence is relevant and efficiently communicates the tool's purpose and usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with rich annotations, the description fully covers purpose, parameters, and return content. No gaps remain given the absence of an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description fully compensates by explaining 'vm_name' as the exact name and 'target' as an optional vCenter/ESXi name with default behavior. This adds critical meaning absent from the schema titles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves detailed information about a specific VM, listing components like CPU, memory, disks, NICs, and snapshots. The leading '[READ]' emphasizes the action, and the specificity distinguishes it from sibling tools that list or query other resources.
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 for obtaining details on a single VM by name, and the optional target parameter is explained. While it does not explicitly state when to avoid or list alternatives, the context from sibling names makes it sufficiently clear.
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context about three batched server-side passes, opinionated status assignment, and point-in-time snapshot nature—going beyond annotations without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat long but well-structured. It front-loads the purpose, details behavior, then parameters. Every sentence adds value, though it could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explains the return structure (totals, top_issues, issues_total, clusters, snapshot, customization_hint) and tells the agent how to use them (lead with top_issues, show clusters, echo customization_hint). Complete for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description thoroughly explains all four parameters: target, cluster_filter, include_vms, top_n. It provides defaults, allowed values, and practical usage tips like setting top_n=5 for a tighter view or 0 to omit the list.
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 it provides a 'one-glance health rollup' for clusters, aggregating hosts, VM state, CPU/memory, and alarms. It distinguishes itself from sibling tools by recommending use instead of stitching list_all_clusters, list_esxi_hosts, and get_alarms.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this FIRST for a cross-cluster triage view' and contrasts with manual stitching. It also advises when to set include_vms=False for large fleets, providing clear when-to-use and when-not-to-use guidance.
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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral context: point-in-time snapshot (no trending), graceful degradation (unreachable targets listed with reason), and that top_n caps the display but total count is reported. This exceeds what annotations provide.
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 concise (~100 words) with excellent structure: first sentence states purpose, then explains behavior and graceful degradation, then parameter details. Every sentence adds value with zero redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description mentions key output components (top_issues, issues_total, unreachable) but lacks detailed structure of the returned items. For a global health view, this is mostly adequate, but additional detail on the per-target rollup format would make it complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully explains both parameters: cluster_filter as case-insensitive substring applied to every target (None=all), and top_n as the cap on the merged focus list with note that issues_total reports pre-cap count. This adds substantial meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it's a read operation that aggregates cluster health across all configured vCenters into a single globally ranked list. It uses specific verbs like 'rolls' and 'aggregate' and distinguishes itself from sibling tool 'cluster_health_summary' by being the global cross-vCenter view.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this instead of calling cluster_health_summary once per target and merging yourself,' providing clear when-to-use guidance and naming the alternative. It also implies when not to use (per-target views).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already state readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds rich behavioral detail: what data is collected and correlated (state, cluster, VMs, datastores, alarms, performance, event timeline), batching, and aggregation. No contradictions.
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?
Well-structured with READ tag, bullet-like list of contents, usage instructions, and parameter details. Every sentence is informative without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, inputs, and usage well. No output schema, so description could be more explicit about return format, but it hints at operational language and aggregated data. Slightly incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description explains each parameter: host_name (exact name, teaching error, source), target (optional, default null), hours (look-back window). Adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description starts with '[READ]' and specifies 'correlated drill-down' around an ESXi host. It clearly distinguishes from sibling tools like cluster_health_summary and datastore_investigation_bundle.
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?
Explicitly states 'Use this AFTER cluster_health_summary flags a host, or when the operator asks...' and notes it's a point-in-time snapshot. Provides clear context but doesn't exhaustively list when not to use.
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?
Annotations already include readOnlyHint, openWorldHint, idempotentHint. Description adds that data is from a 20-second interval, skips disconnected hosts, and returns live point-in-time data, providing context beyond 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?
Concise and well-structured: summary line, comparative explanation, edge cases, then parameter list. Every sentence adds value; no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given annotations (rich hints), output schema existence (not shown but present), and tool complexity, the description fully covers what the tool does, what it returns, its limitations, and parameter usage.
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?
Schema coverage is 0%, but description lists all three parameters with brief explanations ('host_name: Filter to a single host', 'target: Optional target', 'limit: Max rows'), adding meaning beyond the schema's title and type.
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?
Starts with '[READ]' and clearly states 'Real-time CPU/memory/disk/network utilisation per ESXi host.' Directly contrasts with sibling list_esxi_hosts by specifying static vs live metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Unlike list_esxi_hosts (static config), this returns LIVE utilisation' and notes 'Busiest hosts first' and 'Disconnected hosts... skipped.' Also states 'Point-in-time only — no historical trend' to clarify limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description goes beyond by revealing auto-compact logic (when no limit/fields and inventory > 50 VMs, returns compact fields) and the folder_path field. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose and structured with sections. The parameter list is thorough but slightly lengthy. Overall efficient with minimal redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but the description explains the return dict structure ({total, mode, vms, hint}) and key field folder_path. All 6 parameters are explained with types, defaults, and behavior. Example given for folder_filter. Complete for a read-only list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description provides full parameter documentation: target (optional), limit (max VMs), sort_by (enum values), power_state (filter), fields (available options), folder_filter (case-insensitive substring match). This adds significant meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with '[READ] List virtual machines with optional filtering, sorting, and field selection.' It clearly states the verb 'list' and the resource 'virtual machines,' and distinguishes from siblings like vm_info (for a single VM) and other list tools.
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 explains when to use (listing VMs with filters) and details auto-compact behavior that guides usage when no limit/fields are set. It does not explicitly compare to alternatives or state when not to use, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and idlempotentHint. The description adds valuable context: uses native API (no PEM parsing), returns specific fields, and orders results by expiry. It explains the behavior beyond what annotations provide.
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 well-structured with a header, context, return fields, and parameter explanations. Each sentence is informative and concise, with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description sufficiently covers return values and behavior. It explains the purpose, impact, ordering, and API source, making it complete for an agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Even though schema coverage is 0%, the description explains all three parameters: warn_days (default 30), target (optional), and limit (max rows). It adds meaning beyond the schema by stating defaults and behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks ESXi management certificate expiry, specifies it's a READ operation, and explains the consequence of expired certs. It distinguishes from sibling tools by focusing on certificate status specifically.
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 proactive use to prevent outages but does not explicitly contrast with sibling tools. However, the context of checking cert expiry is clear, and the return ordering (soonest-to-expire first) guides use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, etc.), the description explains that it returns specific fields (capacity_gb, free_gb, committed_gb, provisioned_gb, used_pct, overcommit_pct), orders riskiest first, is point-in-time, and clarifies the meaning of overcommit >100%.
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 concise, front-loaded with the READ tag and purpose, followed by a clear explanation, return fields, and parameter list. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 optional params, output schema exists), the description covers all necessary aspects: purpose, behavior, parameters, return fields, and differentiation from sibling. It is fully adequate for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully explains both parameters: target (optional, uses default) and limit (optional, None=all). This adds essential meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns per-datastore capacity with thin-provisioning over-commit, and explicitly differentiates itself from the sibling tool list_all_datastores by adding the overcommit_pct risk signal.
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 when to use this tool (when overcommit risk is needed) and contrasts with list_all_datastores, but does not explicitly state when not to use it or provide multiple alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond readOnlyHint and idempotentHint, it reveals reading the last `lines` entries from specific logs, matching known trouble patterns, and returning an empty list if none found. This adds concrete behavioral detail without contradicting 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 efficiently structured: a one-line summary, a behavioral paragraph, a usage tip, and parameter details. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given annotations and output schema, the description covers input, behavior, output (empty list), and performance guidance. It mentions return fields (severity, source, message) even though output schema exists, enhancing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully explains each parameter: host_name (filter or null), lines (count, default 500), target (optional, defaults). This adds critical meaning beyond the schema types and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with '[READ] Scan recent ESXi host syslog lines for error/warning patterns', clearly stating the verb 'scan' and the specific resource 'ESXi host syslog lines' with a focused purpose. It distinguishes from sibling tools that list or retrieve data without pattern filtering.
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 notes that only errors/warnings are returned, keeping output small, and suggests filtering with host_name for speed. It implies when to use (need error patterns) but does not explicitly state alternatives or when not to use, though it effectively guides efficient usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral details: tasks are returned with running/queued first, includes error info for failed tasks, and explicitly states 'Read-only'. No contradictions with 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 concise (3 sentences + args block), front-loaded with purpose and behavior, and each sentence earns its place. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 optional params and an output schema, the description covers purpose, return fields, behavior, parameter semantics, and even cross-reference to cancellation. It is complete for an AI agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description fully explains all three parameters: include_recent (default True, controls inclusion of completed/failed tasks), target (optional, uses default if omitted), and limit (max rows, None=all). This adds crucial meaning beyond the schema's type/default information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with '[READ]' and clearly states it returns in-flight and optionally just-completed vCenter tasks. It answers a specific question ('why is the cluster busy?') and lists returned fields (name, entity, state, progress_pct, etc.), making the tool's purpose immediately clear and distinct from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the tool is read-only and directs users to 'vmware-aiops' for canceling tasks. It implies usage for monitoring cluster activity. While it doesn't list all alternatives, the context and sibling list (e.g., get_events, host_log_scan) make usage boundaries clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, etc.), the description discloses batching of reads, aggregation within the tool, a teaching error for unknown names, and that it is a point-in-time snapshot. This adds significant behavioral 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 well-structured with a clear lead, bullet-like list of inclusions, usage guidance, and parameter explanations. Every sentence adds value, and it is front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and no output schema, the description adequately explains what the tool returns by listing components. It also provides context on error handling and operational language. The parameter descriptions are thorough, making the tool complete and actionable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description fully compensates by detailing each parameter: datastore_name (exact name, teaching error, suggestion to get from list_all_datastores), target (optional, default if omitted), and hours (default 24). This adds crucial meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as a 'correlated drill-down' for a single datastore, listing specific components (capacity, hosts, VMs, alarms, events). It distinguishes from siblings by explicitly mentioning when to use it after cluster_health_summary and by noting that per-datastore latency is separate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use this AFTER cluster_health_summary flags storage pressure, or when the operator asks "what's going on with datastore <x>?"' It also clearly states what is not included (per-datastore latency) and that it provides a point-in-time snapshot with no trending.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds crucial context: it only reports configuration health, not live offset, and explains the implication of the healthy flag. No contradictions with 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?
Description is extremely concise: a one-line title, a two-line summary, a one-line caveat, and two lines of parameter docs. Every sentence is necessary and well-organized, front-loaded with the main purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has only 2 optional parameters and an output schema exists, the description provides all necessary context: return fields, the healthy flag logic, and the limitation compared to live offset. Complete for a health check tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 0% description coverage, so the description fully bears the burden. It explains both parameters: host_name for filtering to a single host (None=all), and target for optional target name. This adds complete semantics beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it is a read operation for per-host NTP configuration health, specifying the exact resource (NTP servers and ntpd service state) and what it returns (healthy flag). This distinguishes it from sibling tools like host_performance or get_host_sensors.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description explicitly states what the tool does ('returns configuration health') and what it does NOT do ('does not expose live clock offset/stratum'). It also provides an alternative method ('use esxcli on the host') for when the actual offset is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false. The description adds that est_size_mb is a lower-bound due to API limitations, mentions the return structure, and reinforces read-only behavior. This provides context beyond 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 well-structured: starts with a [READ] header, a concise one-sentence summary, a clarifying contrast, return format details, and a parameter list. Every sentence is informative without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explains the exact return structure (total_snapshots, old_snapshots, vms_with_snapshots, etc.) and details about est_size_mb. Given the tool's complexity (sweep all VMs, return structured data), this is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description carries the full burden. It lists all four parameters with defaults and meanings: age_threshold_days, only_old, target, limit. This adds significant value beyond the input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool sweeps all VMs for snapshots and flags old/sprawling ones, contrasting with vm_list_snapshots which covers only one VM. This provides a specific verb (sweep, flag) and resource (all VMs snapshots), distinguishing it from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly contrasts with vm_list_snapshots and notes that snapshot deletion should be done via another tool (vmware-aiops). This tells the agent when to use this tool and when not to, including alternatives.
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:

[![VMware-Monitor MCP server](https://glama.ai/mcp/servers/vmware-skills/VMware-Monitor/badges/card.svg)](https://glama.ai/mcp/servers/vmware-skills/VMware-Monitor)

Score Badge

Copy to your README.md:

[![VMware-Monitor MCP server](https://glama.ai/mcp/servers/vmware-skills/VMware-Monitor/badges/score.svg)](https://glama.ai/mcp/servers/vmware-skills/VMware-Monitor)

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/vmware-skills/VMware-Monitor'

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