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-Mind Map Server
Ein dynamischer MCP-Serververwaltungsdienst, der Model Context Protocol (MCP)-Server dynamisch erstellt, ausführt und verwaltet. Dieser Dienst dient als MCP-Server und startet/verwaltet andere MCP-Server als untergeordnete Prozesse, wodurch ein flexibles MCP-Ökosystem ermöglicht wird.
Index
Related MCP server: CaptureMind
Einführung
Das Projekt mapas_mentais ist eine Python-Anwendung, die automatisierte Mindmaps generiert, um das Studium, die Überprüfung, den Vergleich und die Präsentation verschiedener Themen zu erleichtern. Unter Verwendung der Idee des MCP-Servers bietet das System Erkenntnisse durch die direkte Interaktion mit Claude Desktop über Claude-Modelle. Das Projekt ist ideal für Studenten, Lehrer und Fachleute, die Ideen visuell und effizient organisieren möchten. Es ist leicht erweiterbar und kann in andere Automatisierungssysteme oder virtuelle Assistenten integriert werden.
Projektstruktur
Die Idee zu diesem Projekt entstand durch die Erklärungen von Professor Sandeco Macedo von der UFG (Bundesuniversität Goiás) zu MCPs im Buch „MCP und A2A für Dummies“ . Es handelt sich um einen einfachen MCP-Server, der nur das FastMCP-Paket verwendet und zudem den Richtlinien des offiziellen Repository des Model Context Protocol von Anthropic folgt.
Die sechs Arten von Mindmaps, die in diesem MCP-Server verwendet werden, sind:
präsentiert – Generiert eine Mindmap für Präsentationen zu einem Thema;
Vergleichen – Erstellt eine Mindmap, die zwei Themen vergleicht;
initial – Generiert eine mentale Karte des anfänglichen Wissens über das Thema;
Mittelstufe – Generiert eine Mindmap mit mittlerem Wissen zum Thema;
Probleme – Erstellt eine mentale Karte der Analyse von Problemen, die mit dem Thema zusammenhängen;
Überprüfung – Generiert eine Mindmap, um Inhalte zu einem Thema zu überprüfen.
Eingesetzte Technologien
Anforderungen
Python installiert (Version 3.10 oder höher);
uv-Paket installiert;Claude Desktop installiert.
So installieren Sie auf Claude Desktop
Jetzt erkläre ich im Detail, wie meine Schritte in Windows 11 mithilfe des Terminals (Tastenkombination CTRL + SHIFT + ' ) in VSCode abliefen:
Ich habe die aktuellste Version von Python installiert
In VSCode habe ich das Terminal verwendet, um die Python-Version mit dem Befehl zu überprüfen
python --versionAlso habe ich die
uvmit der Fernbedienung installiertpip install uvUm zu überprüfen, ob alles in Ordnung war, habe ich den Befehl verwendet
uvUm den Projektordner zu erstellen, habe ich diesen Befehl verwendet
mkdir “C:\Users\meu_usuario\OneDrive\area_de_trabalho\mapas_mentais”
[!WICHTIG] Dies bedeutet nicht unbedingt, dass Sie denselben Pfad verwenden. Möglicherweise möchten Sie einen anderen Pfad verwenden, beispielsweise den unten stehenden.
mkdir "C:\Users\seu_usuario\mapas_mentais"Oder Sie können die Zip-Datei dieses Projekts einfach über
Code>Download ZIP.
Ich habe den Ordner, den ich gerade erstellt hatte, benannt
cd “C:\Users\meu_usuario\OneDrive\area_de_trabalho\mapas_mentais”Ich habe den folgenden Befehl verwendet, um ein weiteres VSCode-Fenster zu öffnen und mit den anderen Befehlen direkt im Ordner fortzufahren
code .
[!WICHTIG] Wenn Sie den Ordner nicht über das Terminal erstellen möchten, können Sie einen neuen Ordner auf Ihrem Desktop oder an einem anderen leicht zu merkenden Ort erstellen, um die Tastenkombination
CTRL+Oin VSCode zu verwenden. Suchen Sie dann einfach nach dem gerade erstellten Ordner, klicken Sie darauf und öffnen Sie ihn in VSCode. Oder importieren Sie einfach den kompletten Ordner dieses Repositorys in Ihren VSCode.
Zurück im Terminal habe ich den folgenden Befehl verwendet, um ein neues Python-Projekt zu initialisieren und Konfigurationsdateien und Abhängigkeiten automatisch zu erstellen.
uv initAnschließend habe ich den folgenden Befehl verwendet, um eine isolierte virtuelle Python-Umgebung zum Installieren von Projektabhängigkeiten zu erstellen.
uv venvUm .venv zu aktivieren, habe ich den folgenden Befehl verwendet
.venv\Scripts\Activate.ps1Ich habe die MCP-Abhängigkeit hinzugefügt, die für das Projekt notwendig ist
uv add mcp[cli]Ich habe mit dem folgenden Befehl überprüft, ob alles in Ordnung ist
uv run mcp[!WICHTIG] Wenn die folgenden Informationen auf Ihrem Terminal angezeigt werden, ist alles in Ordnung.
Um die Datei
server.pyzu erstellen, habe ich diesen Befehl verwendet
uv init --script server.py[!TIP] Da Sie den Ordner für dieses Repository möglicherweise bereits heruntergeladen haben, befindet sich die Datei
server.pyzu diesem Zeitpunkt bereits in Ihrem VSCode.
Ich habe das unten stehende JSON vom MCP-Server direkt in die Datei
claude_desktop_config.jsoninstalliert
"mapas_mentais": {
"command": "uv",
"args": [
"--directory",
"C://Users//meu_usuario//OneDrive//area_de_trabalho//mapas_mentais",
"run",
"server.py"
]
}[!IMPORTANT] Wenn Sie Claude Desktop bereits korrekt installiert haben, folgen Sie dem Pfad, um auf die Datei
claude_desktop_config.jsonauf Ihrem Computer zuzugreifen
14. Wenn Claude Desktop geöffnet ist, verwenden Sie die TastenkombinationCTRL+,
14b. Klicken Sie auf die RegisterkarteDesenvolvedorund dann aufEditar configuração
14c. Suchen Sie die Dateiclaude_desktop_config.jsonund bearbeiten Sie sie in VSCode korrekt
14d. Speichern Sie die Datei mitCTRL+S
14e. Schließen Sie Claude Desktop und öffnen Sie es nach einigen Sekunden erneut.
14f. Überprüfen Sie das Konfigurationssymbol, um zu sehen, ob die MCP-Tools „mental_maps“ korrekt installiert sind
Die Tools wurden „Präsentieren“, „Vergleichen“, „Anfänglich“, „Zwischenstufe“, „Probleme“ und „Überprüfen“ genannt.
Nützliche Links
Offizielle Dokumentation des Model Context Protocol - Sie erfahren alle Details dieser Innovation von Anthropic
Offizielle Website von Anthropic - Bleiben Sie über die neuesten Nachrichten und Studien zu Claude-Modellen auf dem Laufenden
So laden Sie Claude Desktop herunter – Direkter Download-Link
So installieren Sie VSCode – Direkter Download-Link
Offizielle UV-Paketdokumentation - Sie erfahren alle Details zu
uvund dessen Bedeutung in Pythonvenv – Erstellen virtueller Umgebungen – Vollständige Erklärung der Funktionsweise von venvs
AI/LMM-Modell-Symbolsatz – Sehr gute Site, um Symbole für das KI-Ökosystem zu erhalten
Devicon – sehr umfassende Site, auch mit allgemeinen Symbolen zur Technologie
Beiträge
Beiträge sind willkommen! Wenn Sie Ideen zur Verbesserung dieses Projekts haben, können Sie das Repository gerne forken.
Lizenz
Dieses Projekt ist unter der MIT-Lizenz lizenziert – Einzelheiten finden Sie in der Datei LICENSE .
Kontakt
Mario Lucio - 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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