Provides tools to search and retrieve knowledge base articles and documentation from the Broadcom Support Portal for various product segments including VMware, Tanzu, and Cybersecurity.
Enables access to VMware-specific technical documentation and support articles through the Broadcom Support Portal.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Broadcom Support MCP Server find vSphere 8.0 upgrade guides and best practices"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Broadcom Support Portal のナレッジベース記事を検索・取得するための MCP サーバーです。 VMware、Tanzu、Cybersecurity など Broadcom 製品群のドキュメントにアクセスできます。
.kiro/settings/mcp.json に以下を追加してください。
# 依存関係のインストール
uv venv broadcom-support-mcp-server/.venv
uv pip install -e broadcom-support-mcp-server -p broadcom-support-mcp-server/.venv/bin/python{
"mcpServers": {
"broadcom-support": {
"command": "/path/to/broadcom-support-mcp-server/.venv/bin/broadcom-support-mcp-server",
"args": [],
"disabled": false,
"autoApprove": [
"search_documentation",
"read_documentation"
]
}
}
}/path/to/ は実際のパスに置き換えてください。
Related MCP server: Adobe Experience League MCP Server
search_documentationBroadcom Support のナレッジベースを検索します。
パラメータ | 型 | 必須 | デフォルト | 説明 |
| string | ✅ | - | 検索クエリ |
| string | - |
| 製品セグメント |
read_documentation指定 URL のドキュメント記事を取得し、Markdown 形式で返します。
パラメータ | 型 | 必須 | 説明 |
| string | ✅ | 記事の URL |
Kiro のチャットで以下のように質問できます:
「vSphere のアップグレード手順を検索して」
「ESXi のネットワーク設定について調べて」
「Tanzu Kubernetes のトラブルシューティング記事を探して」
# venv作成と依存関係のインストール
uv venv broadcom-support-mcp-server/.venv
uv pip install -e "broadcom-support-mcp-server[dev]" -p broadcom-support-mcp-server/.venv/bin/python
# テストの実行
broadcom-support-mcp-server/.venv/bin/pytest broadcom-support-mcp-server/tests/ -vSearchUnify API の仕組みや内部アーキテクチャについては ARCHITECTURE.md を参照してください。
Apache License 2.0
Retrieve the full content of a Broadcom Support article at the given URL. Returns the article title, body text, and metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what the tool does ('Retrieve...') and what it returns, but it lacks details on permissions, rate limits, error handling, or other behavioral traits like whether it's read-only or has side effects. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, consisting of two efficient sentences that directly state the action and return values without any wasted words. Every sentence earns its place by providing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return value documentation), the description is mostly complete. It covers the purpose and parameter semantics adequately, but it lacks usage guidelines and behavioral details, which are needed for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning beyond the input schema by specifying that the 'url' parameter should point to a 'Broadcom Support article'. With schema description coverage at 0% and only one parameter, the description effectively compensates by clarifying the parameter's purpose, though it doesn't detail format constraints (e.g., URL structure).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Retrieve') and resource ('Broadcom Support article'), and it distinguishes the scope ('full content') from the sibling tool 'search_documentation', which likely searches rather than retrieves full content. However, it doesn't explicitly differentiate the sibling's function, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the sibling 'search_documentation', nor does it mention any prerequisites or context for usage. It implies usage by specifying the resource but lacks explicit alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the Broadcom Support knowledge base for articles matching the query. Returns a list of relevant articles with titles, URLs, and snippets.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| segment | No | all |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return format ('list of relevant articles with titles, URLs, and snippets'), which is helpful, but does not cover other important aspects such as authentication needs, rate limits, error handling, or whether the search is paginated or limited in scope. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, consisting of two sentences that efficiently convey the tool's purpose and output. Every sentence adds value without redundancy, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is partially complete. It explains the purpose and return values, but lacks details on behavioral traits and usage guidelines. The output schema likely covers return values, reducing the need for description, but gaps in behavioral transparency and guidelines prevent a higher score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not mention any parameters, and the schema description coverage is 0%, so it adds no semantic information beyond what the schema provides. However, with only 2 parameters and an output schema present, the baseline is 3, as the schema handles the parameter documentation adequately without requiring additional explanation in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Search') and resource ('Broadcom Support knowledge base'), and specifies what it returns ('list of relevant articles with titles, URLs, and snippets'). However, it does not explicitly differentiate from its sibling tool 'read_documentation', which likely serves a different function like reading specific articles rather than searching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as its sibling 'read_documentation'. It mentions the tool's function but lacks explicit instructions on context, prerequisites, or exclusions, leaving the agent to infer usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The two tools have clearly distinct purposes: one retrieves a specific article by URL, while the other searches the knowledge base with a query. There is no overlap or ambiguity between them.
Both tools follow a consistent verb_noun pattern (read_documentation, search_documentation), using the same naming convention and clear, descriptive verbs.
With only two tools, the server feels thin for a support knowledge base domain. While the tools cover basic retrieval and search, typical support systems might benefit from additional operations like filtering by category, viewing recent articles, or handling user authentication.
The tools provide core read and search functionality, but there are notable gaps. For example, there is no way to browse articles by category, list trending or recent content, or manage user-specific features like saved articles or subscriptions, which could limit agent workflows.
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