A
licenseNot graded
qualityD
maintenanceEnables conversion of plain text descriptions and Markdown content into interactive mind maps using AI. Automatically uploads generated mind maps to Aliyun OSS and provides online access links.
22 npm
1
MIT
MCP-Server de Mapas Mentais
MCP-マインドマップサーバー
モデル コンテキスト プロトコル (MCP) サーバーを動的に作成、実行、管理する動的 MCP サーバー管理サービス。このサービスは MCP サーバーとして機能し、他の MCP サーバーを子プロセスとして起動/管理し、柔軟な MCP エコシステムを実現します。
索引
Related MCP server: CaptureMind
導入
mapas_mentais プロジェクトは、さまざまなトピックの研究、レビュー、比較、プレゼンテーションを容易にするために、自動化されたマインド マップを生成する Python アプリケーションです。 MCP サーバーのアイデアを使用して、システムは Claude モデルを通じて Claude Desktop と直接対話することで洞察を提供します。このプロジェクトは、アイデアを視覚的かつ効率的に整理したい学生、教師、専門家に最適で、簡単に拡張でき、他の自動化システムや仮想アシスタントと統合できます。
プロジェクト構造
このプロジェクトのアイデアは、UFG (ゴイアス連邦大学) の Sandeco Macedo 教授が「MCP and A2A for Dummies」という書籍で MCP について説明したことから生まれました。これは、FastMCP パッケージのみを使用するシンプルな MCP サーバーであり、Anthropic のモデル コンテキスト プロトコルの公式リポジトリのガイドラインにも従っています。
この MCP サーバーで使用される 6 種類のマインド マップは次のとおりです。
プレゼンテーション - トピックに関するプレゼンテーション用のマインドマップを生成します。
compare - 2 つのトピックを比較するマインド マップを生成します。
initial - トピックについての初期知識のメンタルマップを生成します。
中級 - トピックに関する中級レベルの知識のマインドマップを生成します。
問題 - トピックに関連する問題の分析のメンタルマップを生成します。
レビュー - トピックのコンテンツをレビューするためのマインドマップを生成します。
使用される技術
要件
Python がインストールされている (バージョン 3.10 以上)。
uvパッケージがインストールされています。Claude Desktop がインストールされました。
Claude Desktopへのインストール方法
ここで、VSCode のターミナル (ショートカットCTRL + SHIFT + ' ) を使用して、Windows 11 で実行した手順を詳しく説明します。
Pythonの最新バージョンをインストールしました
VSCodeでは、ターミナルを使用してコマンドでPythonのバージョンを確認しました。
python --versionそこでリモコンで
uvをインストールしましたpip install uvすべてが正常かどうかを確認するために、コマンドを使用しました
uvプロジェクトフォルダを作成するには、このコマンドを使用しました
mkdir “C:\Users\meu_usuario\OneDrive\area_de_trabalho\mapas_mentais”
[!IMPORTANT] 必ずしも同じパスを使用するわけではありません。以下のような別のパスを使用することもできます。
mkdir "C:\Users\seu_usuario\mapas_mentais"または、GitHub の
Code>Download ZIPからこのプロジェクトの zip ファイルをマシンにダウンロードすることもできます。
先ほど作成したフォルダに名前を付けました
cd “C:\Users\meu_usuario\OneDrive\area_de_trabalho\mapas_mentais”以下のコマンドを使用して別のVSCodeウィンドウを開き、フォルダー内で直接他のコマンドを続行します。
code .
[!重要] ターミナル経由でフォルダーを作成したくない場合は、VSCode のショートカット
CTRL+Oを使用するために、デスクトップまたは覚えやすい別の場所に新しいフォルダーを作成できます。次に、作成したフォルダーを探してクリックし、VSCode で開きます。または、このリポジトリの完全なフォルダーを VSCode にインポートするだけです。
ターミナルに戻り、以下のコマンドを使用して新しいPythonプロジェクトを初期化し、構成ファイルと依存関係を自動的に作成しました。
uv init次に、以下のコマンドを使用して、プロジェクトの依存関係をインストールするための分離された Python 仮想環境を作成しました。
uv venv.venvをアクティブ化するには、以下のコマンドを使用しました。
.venv\Scripts\Activate.ps1プロジェクトに必要なMCP依存関係を追加しました
uv add mcp[cli]以下のコマンドですべてが正常かどうかを確認しました
uv run mcp[!重要] 以下の情報が端末に表示されれば、問題ありません。
server.pyファイルを作成するには、次のコマンドを使用しました。
uv init --script server.py[!TIP] このリポジトリのフォルダーはすでにダウンロードされている可能性があるため、この時点では VSCode に
server.pyファイルがすでに存在しているはずです。
MCP-Serverから以下のjsonを直接
claude_desktop_config.jsonファイルにインストールしました。
"mapas_mentais": {
"command": "uv",
"args": [
"--directory",
"C://Users//meu_usuario//OneDrive//area_de_trabalho//mapas_mentais",
"run",
"server.py"
]
}[!重要] Claude Desktop がすでに正しくインストールされている場合は、パスに従ってコンピューター上の
claude_desktop_config.jsonファイルにアクセスしてください。
14日。 Claude Desktopを開いた状態で、ショートカットCTRL+ , を使用します,
14b.Desenvolvedorタブをクリックし、Editar configuraçãoをクリックします。
14c.claude_desktop_config.jsonファイルを見つけて、VSCodeで正しく編集します。
14日。CTRL+Sでファイルを保存します
14e. Claude Desktopを閉じて、数秒後に再度開きます
14f.構成アイコンをチェックして、MCP「mental_maps」ツールが正しくインストールされているかどうかを確認します。
ツールには、「提示」、「比較」、「初期」、「中間」、「問題」、「レビュー」という名前が付けられました。
便利なリンク
モデルコンテキストプロトコルの公式ドキュメント- Anthropicのこのイノベーションの詳細をすべて知ることができます
アントロピックの公式ウェブサイト- クロードモデルに関する最新ニュースや研究を常に把握できます
Claude Desktopのダウンロード方法- 直接ダウンロードリンク
VSCode のインストール方法- 直接ダウンロードリンク
公式のUVパッケージドキュメント-
uvに関する詳細と、それがPythonでどのように重要であるかをすべて知ることができます。venv — 仮想環境の作成- venvsの仕組みの完全な説明
AI/LMM モデルアイコンセット- AI エコシステムのアイコンを入手できる非常に優れたサイト
Devicon - テクノロジーに関する一般的なアイコンも掲載された非常に充実したサイト
貢献
貢献を歓迎します!このプロジェクトを改善するためのアイデアがあれば、お気軽にリポジトリをフォークしてください。
ライセンス
このプロジェクトは MIT ライセンスに基づいてライセンスされています - 詳細についてはLICENSEファイルを参照してください。
接触
マリオ・ルシオ - Deadline®
Available Tools
6 toolsapresentaC
Gera um mapa mental para apresentações sobre um tema.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tema | Yes |
TDQS
C2.8/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates a mind map but doesn't describe what format the output takes (e.g., text, image, structured data), whether it's a read-only or mutating operation, or any performance characteristics. For a generation tool with zero annotation coverage, this leaves significant behavioral gaps.
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 - a single sentence that directly states the tool's function. There's no wasted language or unnecessary elaboration. It's appropriately sized for a simple tool with one parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and minimal parameter documentation, the description is incomplete. It tells what the tool does at a high level but doesn't provide enough information about how to use it effectively, what to expect as output, or how it differs from sibling tools. For a generation tool, more context about output format and behavioral characteristics would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 0% description coverage, and the tool description doesn't mention any parameters at all. While the parameter 'tema' (topic) is self-explanatory, the description provides no additional context about what constitutes a valid topic, format expectations, or examples. With low schema coverage, the description fails to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Gera um mapa mental para apresentações sobre um tema' (Generates a mind map for presentations on a topic). It specifies the verb ('gera' - generates) and resource ('mapa mental' - mind map) with the context of presentations. However, it doesn't differentiate from sibling tools like 'compara' or 'revisa' which might have related functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or suggest when to choose this over sibling tools like 'compara' or 'revisa'. The usage context is implied (for presentations on a topic) but lacks explicit when/when-not instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
comparaC
Gera um mapa mental comparando dois temas.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tema1 | Yes | ||
| tema2 | Yes |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'gera' (generates) implies a creation operation, the description doesn't specify whether this is a read-only or mutating action, what permissions might be required, whether there are rate limits, or what the output format looks like. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Gera um mapa mental comparando dois temas.' It's front-loaded with the core action and includes all essential elements (action, resource, scope) without any wasted words. Every part of the sentence contributes directly to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a mind map generation tool with two parameters), lack of annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the mind map output contains, how comparisons are structured, whether there are limitations on theme complexity, or what happens if themes are invalid. For a creative/generation tool, more contextual guidance would be helpful.
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 description mentions 'dois temas' (two themes), which aligns with the two parameters (tema1 and tema2) in the schema. However, with 0% schema description coverage, the schema provides no details about these parameters. The description adds basic semantic context (they represent themes to compare) but doesn't elaborate on format, constraints, or examples. This meets the baseline for minimal parameter information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Gera um mapa mental comparando dois temas' (Generates a mind map comparing two themes). It specifies the verb ('gera' - generates), resource ('mapa mental' - mind map), and scope ('comparando dois temas' - comparing two themes). However, it doesn't explicitly distinguish this from sibling tools like 'apresenta' or 'revisa', which might also involve presentation or review functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There are no explicit instructions about when this tool is appropriate, when it should not be used, or what sibling tools might serve as alternatives for related tasks. The agent must infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inicialC
Gera um mapa mental de conhecimentos iniciais sobre o tema.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tema | Yes |
TDQS
C2.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions generation but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires authentication, rate limits, or what format the mind map output takes. The description is minimal and lacks essential operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function. It's appropriately sized and front-loaded with the core action, though it could be more structured with additional context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (generating a mind map), lack of annotations, no output schema, and minimal parameter details, the description is incomplete. It doesn't explain what the output looks like, how the mind map is structured, or any limitations, leaving significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It implies the parameter 'tema' is the topic for the mind map, adding some meaning beyond the bare schema. However, with only one parameter, the baseline is 4, but the description doesn't fully detail the parameter's semantics (e.g., format, scope), so it scores slightly lower.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'generates an initial knowledge mind map about the topic', which provides a clear verb ('generates') and resource ('mind map'). However, it doesn't specify what distinguishes this from sibling tools like 'apresenta' or 'revisa', leaving the purpose somewhat vague in context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or exclusions, leaving the agent with no usage direction beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
intermediarioC
Gera um mapa mental de conhecimentos intermediários sobre o tema.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tema | Yes |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates a mind map, implying a read-only or creative operation, but doesn't clarify if it requires specific inputs beyond the topic, how the output is structured, whether it's cached or real-time, or any error conditions. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence in Portuguese: 'Gera um mapa mental de conhecimentos intermediários sobre o tema.' It is front-loaded with the core action and resource, with no wasted words. Every part of the sentence contributes to understanding the tool's purpose efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations, no output schema, and low schema description coverage (0%), the description is incomplete. It doesn't explain what 'conhecimentos intermediários' (intermediate knowledge) means, how the mind map is returned (e.g., text, image, structured data), or any limitations. For a tool that likely produces complex output, more context is needed to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'sobre o tema' (on the topic), which aligns with the single parameter 'tema' (topic) in the input schema. However, schema description coverage is 0%, so the schema provides no additional details about the parameter. The description adds minimal semantic context by implying the parameter is a topic string, but doesn't specify format, length, or examples. With one parameter and low coverage, this is adequate but basic.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Gera um mapa mental de conhecimentos intermediários sobre o tema' (Generates a mind map of intermediate knowledge on the topic). It specifies the action (generate), the resource (mind map), and the scope (intermediate knowledge on a topic). However, it doesn't explicitly distinguish this tool from its siblings like 'inicial' or 'revisa', which might also be related to knowledge mapping or topic exploration.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for 'intermediate knowledge', or how it differs from sibling tools such as 'apresenta', 'compara', 'inicial', 'problemas', or 'revisa'. Without this information, an AI agent must guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
problemasC
Gera um mapa mental de análise de problemas relacionados ao tema.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tema | Yes |
TDQS
C2.4/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates a mind map but doesn't describe what the output looks like (e.g., format, structure), whether it's a read-only or mutative operation, or any constraints like rate limits or permissions. For a tool with zero annotation coverage, this is a significant gap in 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized for a simple tool, though it could be more front-loaded with additional context if needed. The structure is clear but minimal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple with 1 parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return values (e.g., what the mind map output entails), behavioral traits, or detailed parameter usage. For a tool with no structured data support, the description should provide more comprehensive context to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter ('tema') with 0% description coverage in the schema itself. The tool description mentions 'related to the theme', which loosely maps to the 'tema' parameter, but doesn't add meaningful semantics such as what constitutes a valid theme, examples, or constraints. With low schema coverage, the description fails to adequately compensate for the lack of parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'generates a mind map for problem analysis related to the theme', which provides a clear verb ('generates') and resource ('mind map'). However, it doesn't distinguish this from sibling tools like 'apresenta' or 'compara', leaving the specific differentiation unclear. The purpose is understandable but lacks sibling context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'apresenta' or 'compara'. It implies usage for problem analysis related to a theme, but doesn't specify prerequisites, exclusions, or comparative contexts with other tools. This leaves the agent with minimal direction for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
revisaC
Gera um mapa mental para revisão de conteúdo sobre um tema.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tema | Yes |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'gera' (generates) implies a creation operation, the description doesn't specify whether this is a read-only or mutative action, what permissions might be required, whether the output is stored or temporary, or any rate limits. It mentions the output type (mind map) but not its format or structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Portuguese that directly states the tool's function. It is appropriately sized and front-loaded with the core action, with no unnecessary words or redundant information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a tool that generates a mind map), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what the mind map output looks like, how it's structured, whether it's visual or textual, or any behavioral aspects like error handling. For a generative tool with no structured data, this leaves significant gaps.
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 description adds minimal meaning beyond the input schema. It mentions 'tema' (topic) as the subject for the mind map, which aligns with the single parameter 'tema' in the schema. However, with 0% schema description coverage, the parameter is undocumented in the schema, and the description doesn't elaborate on what constitutes a valid 'tema' or provide examples. The baseline is 3 since schema coverage is low but the description partially compensates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Gera um mapa mental para revisão de conteúdo sobre um tema' (Generates a mind map for content review on a topic). It specifies the verb ('gera' - generates), resource ('mapa mental' - mind map), and context ('revisão de conteúdo' - content review). However, it doesn't differentiate from sibling tools like 'apresenta' or 'compara', which likely have different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this tool is appropriate, when to use sibling tools instead, or any prerequisites. The context is implied (content review on a topic) but lacks explicit usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
6 tool updates
- First observed
apresenta - First observed
compara - First observed
inicial - First observed
intermediario - First observed
problemas - First observed
revisa
TDQS
B3.1/5.0
Scored across 6 tools
Disambiguation3/5
The tools have overlapping purposes as they all generate mind maps, but their descriptions help differentiate them by specifying distinct contexts like presentations, comparisons, knowledge levels, problem analysis, and review. However, 'inicial' and 'intermediario' could be confused as they both relate to knowledge levels without clear boundaries.
Naming Consistency5/5
All tool names follow a consistent pattern using Portuguese verbs in a simple, uniform style (e.g., 'apresenta', 'compara', 'inicial'). There are no deviations in naming conventions, making them predictable and readable.
Tool Count5/5
With 6 tools, the count is well-scoped for a mind map generation server, covering various use cases like presentations, comparisons, and reviews. Each tool appears to serve a distinct purpose, making the set appropriately sized.
Completeness4/5
The tool surface covers key mind map generation scenarios, including creation for different contexts and review. A minor gap exists in lacking explicit update or delete operations for existing mind maps, but agents can likely work around this by regenerating maps as needed.
Maintenance
ActivityInactive
ResponsivenessNo issues
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- MindlifyOAuthco.mindlify
Turn AI conversations into visual knowledge maps. Create, connect, search, and organize thoughts.
Create, edit, and organize MindMeister mind maps from your AI assistant.
Turn outlines and hierarchical notes into interactive mind maps through a hosted remote MCP server.
Visual AI for strategic thinking — SWOT, flowcharts, mindmaps, Gantt diagrams as polished SVG.
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