io.github.zw008/vmware-log-insight

Server Quality Checklist

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Latest release: v1.8.15
Disambiguation5/5
Each tool has a clearly distinct purpose: alert list/get/history handle alert definitions and trigger records, while log_search/log_aggregate/log_fields/log_version handle log exploration and diagnostics. There is no realistic overlap between alert_get and alert_history or between log_search and log_aggregate.
Naming Consistency5/5
All tool names use lowercase snake_case and follow a consistent noun_verb pattern, with alert_ prefixed for alert operations and log_ prefixed for log operations. This creates a predictable and scannable naming scheme.
Tool Count5/5
Seven tools is a well-scoped size for a focused read-only Log Insight API. Each tool earns its place and there is no redundancy or padding.
Completeness4/5
The toolset covers the main read workflows: version checking, field discovery, event search, aggregation, and alert definition/history inspection. The only gap is that the alert surface is intentionally read-only, so creating, updating, or deleting alerts requires external tooling.
Average 4.6/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 47 commits in the last 12 weeks
- Last stable release on August 31, 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.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only/idempotent/destructive hints. The description adds valuable behavioral details beyond annotations: the exact envelope shape, that every matching field is returned, and that truncated is always false — meaning the result is a complete list, not a page. This is helpful for an agent deciding whether to paginate.
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 '[READ]' and the primary action, then flows logically through purpose, return format, and pagination behavior. Almost every sentence earns its place, though the trailing 'Read-only' is redundant given the '[READ]' prefix and annotations.
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 having no output schema, the description explains the return envelope, field item shape, and pagination behavior, so an agent knows exactly what to expect. Combined with the fully documented schema and strong annotations, nothing needed to invoke the tool correctly is missing.
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 optional parameters well. The description adds the important 'no limit / complete list' nuance but does not need to repeat parameter meaning; baseline 3 is appropriate.
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?
States a specific verb ('List'), the resource ('extracted fields'), and the purpose ('available to use in query filters'). Explicitly names the downstream consumers (log_search / log_aggregate), making the tool's role clear and distinguishable 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives clear context: 'Use this to discover valid field names before filtering log_search / log_aggregate.' It does not explicitly describe when not to use it or mention alternatives, but no sibling tool performs field discovery, so the guidance is sufficient.
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 cover read-only, idempotent, and non-destructive behavior. The description adds useful context beyond that: the diagnostic purpose, exact return fields, and the fact that it is the cheapest way to validate target credentials.
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 compact, front-loaded with the verb and resource, and every sentence serves a distinct role. Slight redundancy exists because '[READ]' and 'Read-only' both repeat the readOnlyHint annotation, so it is not maximally lean.
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 one-parameter, no-output-schema tool, this is complete: the return structure is spelled out, the target parameter is fully documented in the schema, and annotations cover the safety profile.
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%, and the target parameter is thoroughly documented including default behavior and the error case. The description adds no parameter-level meaning, so the baseline of 3 is appropriate.
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?
States a clear verb ('Return') and resource ('Log Insight appliance version/build'), and lists the exact output fields. It also differentiates the tool from its siblings by positioning it as a diagnostic precondition for log_search.
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?
Gives an explicit when-to-use rule: 'Use this first when a query behaves unexpectedly' and explains why, namely to confirm the appliance version before trusting log_search. This directly guides selection among siblings.
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, and destructiveHint=false, so the safety profile is covered. The description adds value by disclosing the return contract, including that complete can be False if truncated, and enumerating event fields. This gives the agent useful behavioral expectations beyond the 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 compact, front-loaded with the core action, then organized into WHEN and RETURNS sections. Every sentence contributes: purpose, usage guidance, output contract, and read-only confirmation. No filler or redundant restating of the tool name.
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 tool with 6 optional parameters, no output schema, and multiple siblings, the description is complete: it explains what the tool does, when to prefer alternatives, what the response contains, and how the output should be chained downstream. The input schema covers parameter details, and annotations cover safety, so nothing critical is missing.
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%, and every parameter already has rich documentation covering formats, defaults, constraints, and error behavior. The description itself adds little parameter-level meaning, which is acceptable under the baseline rule, but it does not need to compensate because the schema carries the weight.
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 opens with a specific verb and resource: 'Search Log Insight events within a time window.' It also differentiates itself from log_aggregate by stating the search is for actual log lines behind an incident, and from vmware-monitor by scope. This makes the tool's purpose unmistakable.
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 WHEN section gives explicit conditions for use and names two alternatives: log_aggregate for 'where did logs burst?' and vmware-monitor for vCenter alarms. It also tells the agent to feed returned events into vmware-debug incident_timeline for correlation, leaving no ambiguity about when this tool is the right choice.
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?
The description goes beyond annotations by disclosing the exact return projection shape, including 'raw_keys' and the intentional non-guessing behavior. It redundantly states 'Read-only' when annotations already include readOnlyHint=true, but it adds useful behavioral context about the response contents.
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 and front-loaded with a [READ] tag and a clear purpose. The return-shape explanation is slightly elaborate but earns its place because there is no output schema; only the repeated 'Read-only' at the end is redundant with the annotations.
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 no output schema, the description fully explains what the agent will receive, how to get the alert id, and which sibling covers the alternative case. Input schema and annotations complete the rest.
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 100%, so the baseline is 3. The description adds meaningful parameter guidance beyond the schema, especially for alert_id: it must match an alert_list row's 'id', is not the alert's name, empty strings are refused, and unknown ids return a 404.
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?
States a specific verb and resource: 'Get the stored definition of one alert.' It also distinguishes itself from siblings by saying 'Use this after alert_list' and routing alert firing questions to alert_history, so an agent can pick the right tool.
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 instructs when to use the tool ('after alert_list') and when not to use it ('For when the alert fired, use alert_history'). The alert_id parameter description also clarifies that the id must come from an alert_list row, not the alert's name.
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?
The description discloses the exact return envelope, item shape, and the semantics of 'total' and 'truncated' — including the actionable 'raise limit when true' behavior. This goes well beyond the annotations' readOnlyHint and gives the agent operational understanding of the tool's output.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: a [READ] tag, a clear purpose sentence, a usage note, and a return-format explainer. Every sentence contributes value, and nothing is wasted or redundant with the schema.
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?
With no output schema, the description fully documents the return envelope and item fields, plus the truncation/limit behavior. Combined with a 100%-covered input schema, an agent has everything needed to call this tool correctly and interpret its results.
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 baseline is 3. The description reinforces the limit/truncated relationship, but the input schema already explains it in detail; the description adds no new parameter-level meaning beyond what the schema provides.
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 opens with a specific verb-resource pair: 'List recent trigger-history records for an alert.' It also disambiguates from sibling tools by clarifying this concerns when an alert fired ('trigger-history') rather than how it's defined, which clearly separates it from alert_list and alert_get.
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 statement 'Use this for when an alert fired, not how it's defined' is an explicit when/when-not guardrail. It tells the agent to choose this tool for firing events, not definition/configuration, and directs the agent away from definition-focused siblings without needing to inspect schemas.
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 the annotations (readOnlyHint, idempotentHint), the description discloses the spike detection threshold (≥2 standard deviations above the mean), the edge case behavior for short or flat series, and explicitly warns that an empty spikes list is not evidence of calm in short windows. It also declares 'Read-only,' consistent with the 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 tightly organized into READ, WHEN, and RETURNS sections, with no filler. It front-loads the core purpose, gives a usage signal, and tucks the return shape and edge-case caveat into the RETURNS block — every sentence earns its place.
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?
The description covers purpose, usage context, return shape, and behavioral nuances (spike detection caveats). With 7 parameters fully documented in the schema and no output schema, the description supplies the missing output contract and enough context for an agent to invoke the tool correctly and interpret results, including the important empty-spikes caveat.
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 already has 100% parameter description coverage, with detailed explanations for last, text, begin_ms/end_ms, target, aggregation, and bin_width_ms. The tool description does not repeat parameter details, which is appropriate; the baseline of 3 applies because the description adds no parameter-level semantics beyond what the schema already provides.
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 opens with a specific verb and resource: 'Aggregate matching events into a time series and detect spikes.' This clearly distinguishes the tool from siblings like log_search (raw events) and log_fields (schema inspection), and the '[READ]' prefix immediately signals it is a non-mutating operation.
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 WHEN section explicitly states the intended scenario: 'to find when/whether log volume burst without pulling raw events,' and directs the agent to 'Follow up with log_search on the spike window.' This names the alternative tool and provides actionable routing guidance rather than leaving the decision implicit.
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 declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, and the description reinforces these with 'Read-only — this skill never creates/edits/deletes alerts.' It adds meaningful behavioral context about the pagination envelope, total/truncated semantics, and where to go next, which annotations do not cover.
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 compact and well-structured: a clear read header, a concise return-shape summary, routing guidance, pagination semantics, and a safety note. Every sentence earns its place 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?
For a list tool with rich annotations and fully documented parameters, the description covers the return envelope, pagination behavior, sibling routing, and read-only guarantee. Nothing needed to select or invoke the tool correctly is missing.
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 100%, so the baseline is 3. The description adds value by explaining how limit and name_filter interact with total and truncated, e.g., 'raise limit or narrow name_filter when true,' which is not fully captured by the schema alone.
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 opens with a specific verb and resource: '[READ] List defined Log Insight alerts.' It also distinguishes itself from siblings by explicitly saying 'Start here, then pass an id to alert_get or alert_history,' making the tool's role in the family clear.
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?
It gives explicit guidance: use this as the entry point, then route to alert_get or alert_history with an id. It also instructs when to raise limit or narrow name_filter based on the truncated flag, which is actionable usage guidance beyond the schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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