erickwendel-contributions-mcp
erickwendel-contributions-mcp
Ein Model Context Protocol (MCP) Server, der Tools bereitstellt, um Erick Wendels Beiträge über verschiedene Plattformen hinweg abzufragen. Fragen Sie Vorträge, Blogbeiträge und Videos mithilfe natürlicher Sprache über Claude, Cursor oder ähnliche Tools ab. Dieses Projekt wurde mit der Cursor IDE unter Verwendung des Standard-Agenten (Testversion) erstellt.
Schnellstart
Verwenden Sie diesen MCP-Server direkt mit Claude Desktop oder Cursor ohne Installation:
{
"mcpServers": {
"erickwendel-contributions": {
"command": "npx",
"args": ["-y", "@erickwendel/contributions-mcp"]
}
}
}Fügen Sie diese Konfiguration hinzu zu:
Claude Desktop:
~/Library/Application Support/Claude/claude_desktop_config.json(macOS)Cursor:
~/.cursor/mcp.json
Related MCP server: mintlify-mcp
Verfügbare Tools
Dieser MCP-Server bietet die folgenden Funktionen:
Tools
get-talks: Ruft eine paginierte Liste von Vorträgen mit optionaler Filterung abUnterstützt Filterung nach ID, Titel, Sprache, Stadt, Land und Jahr
Kann nach Sprache, Land oder Stadt gruppierte Anzahlen zurückgeben
get-posts: Ruft Beiträge mit optionaler Filterung und Paginierung abUnterstützt Filterung nach ID, Titel, Sprache und Portal
get-videos: Ruft Videos mit optionaler Filterung und Paginierung abUnterstützt Filterung nach ID, Titel und Sprache
check-status: Überprüft, ob die API aktiv ist und antwortet
Prompts
find-content: Generiert Abfragen, um spezifische Inhalte nach Typ, Thema und Sprache zu findensummarize-activity: Erstellt Zusammenfassungen der Inhaltsaktivität nach Jahr
Ressourcen
erickwendel://about: Serverinformationen und Metadaten zu den Funktionenerickwendel://statistics: Inhaltsstatistiken und verfügbare Abfragen
Integration mit KI-Tools
MCP-Server-Funktionen untersuchen
Sie können die Funktionen dieses MCP-Servers mit dem MCP Inspector untersuchen:
npm run inspectDies zeigt Ihnen alle verfügbaren Tools, deren Parameter und wie man sie verwendet.
Einrichtung
Stellen Sie sicher, dass Sie Node.js v20+ verwenden
node -v
# v20.x.x or higherKlonen Sie dieses Repository:
git clone https://github.com/erickwendel/erickwendel-contributions-mcp.git
cd erickwendel-contributions-mcpInstallieren Sie die Abhängigkeiten:
npm ciStarten Sie den Server:
npm startIntegration mit KI-Tools
Cursor-Einrichtung
Fügen Sie die folgende Konfiguration zu ~/.cursor/mcp.json hinzu:
{
"mcpServers": {
"erickwendel-contributions": {
"command": "npx",
"args": ["-y", "@erickwendel/contributions-mcp"]
}
}
}
Stellen Sie sicher, dass sich der Cursor-Chat im Agent-Modus befindet, indem Sie im Dropdown-Menü unten links "Agent" auswählen, und fragen Sie dann: "how many videos were published about JavaScript in 2024":

Lokale Entwicklung
Verwenden Sie für die lokale Entwicklung den absoluten Pfad zum Projekt:
{
"mcpServers": {
"erickwendel-contributions": {
"command": "node",
"args": ["--experimental-strip-types", "ABSOLUTE_PATH_TO_PROJECT/src/index.ts"]
}
}
}Claude Desktop-Einrichtung
Gehen Sie zu den Claude-Einstellungen
Klicken Sie auf den Reiter "Developer"
Klicken Sie auf "edit config"
Öffnen Sie die Konfiguration in einem Code-Editor
Fügen Sie die folgende Konfiguration zu
~/Library/Application Support/Claude/claude_desktop_config.json(macOS) hinzu:
{
"mcpServers": {
"erickwendel-contributions": {
"command": "npx",
"args": ["-y", "@erickwendel/contributions-mcp"]
}
}
}Lokale Entwicklung
Verwenden Sie für die lokale Entwicklung den absoluten Pfad zum Projekt:
{
"mcpServers": {
"erickwendel-contributions": {
"command": "node",
"args": ["--experimental-strip-types", "ABSOLUTE_PATH_TO_PROJECT/src/index.ts"]
}
}
}MCPHost mit Ollama (Kostenlose Alternative)
Wenn Sie keinen Zugriff auf Claude Desktop oder Cursor haben, können Sie MCPHost mit Ollama als kostenlose Alternative verwenden.
Installieren Sie MCPHost:
go install github.com/mark3labs/mcphost@latestErstellen Sie eine Konfigurationsdatei (z. B. ./mcp.jsonc):
{
"mcpServers": {
"erickwendel-contributions": {
"command": "npx",
"args": ["-y", "@erickwendel/contributions-mcp"]
}
}
}Ausführen mit Ollama:
ollama pull MODEL_NAME
mcphost --config ./mcp.jsonc -m ollama:MODEL_NAMELokale Entwicklung
Aktualisieren Sie für die lokale Entwicklung die Konfiguration:
{
"mcpServers": {
"erickwendel-contributions": {
"command": "node",
"args": ["--experimental-strip-types", "ABSOLUTE_PATH_TO_PROJECT/src/index.ts"]
}
}
}Beispielabfragen
Hier sind einige Beispiele für Abfragen, die Sie Claude, Cursor oder einem beliebigen MCP-Client stellen können:
"How many talks were given in 2023?"

"Show me talks in Spanish"

"Find posts about WebXR"

Entwicklung
Funktionen
Erstellt mit dem Model Context Protocol (MCP)
Volle MCP-Funktionalität: Tools, Prompts und Ressourcen
Typsicher mit TypeScript und Zod-Schema-Validierung
Native TypeScript-Unterstützung in Node.js ohne Transpilierung
Generiertes SDK unter Verwendung von GenQL
Modulare Architektur mit Trennung der Zuständigkeiten
Standard-I/O-Transport für einfache Integration
Strukturierte Fehlerbehandlung
Kompatibel mit Claude Desktop, Cursor und MCPHost (kostenlose Alternative)
Hinweis: Dieses Projekt erfordert Node.js v23+, da es die native TypeScript-Unterstützung verwendet, die im letzten Jahr hinzugefügt wurde.
Architektur
Die Codebasis folgt einer modularen Struktur:
src/
├── config/ # Configuration settings
├── types/ # TypeScript interfaces and types
├── tools/ # MCP tool implementations
├── utils/ # Utility functions
├── services/ # API service layer
└── index.ts # Main entry pointTesten
So führen Sie die Test-Suite aus:
npm testFür den Entwicklungsmodus mit Watch-Funktion:
npm run test:devMitwirken
Beiträge sind willkommen! Bitte fühlen Sie sich frei, einen Pull Request einzureichen.
Autor
Lizenz
Dieses Projekt ist unter der MIT-Lizenz lizenziert – siehe die Datei LICENSE für Details.
Available Tools
4 toolscheck_statusA
Check if the API is alive and responding.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool performs a liveness check, implying a read-only operation, but it does not disclose what response to expect, error behavior, or whether any side effects occur. This is adequate for a zero-parameter tool but lacks depth.
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 a single sentence, perfectly concise and front-loaded. It conveys the tool's purpose without any extraneous information or repetition of the tool name.
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 simplicity (zero parameters, no output schema, no annotations), the description adequately conveys the core purpose. It could be improved by mentioning the expected return value or how to interpret 'responding,' but for a basic health check, the description is sufficiently complete.
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 tool has zero parameters, so the input schema fully covers any parameter semantics. The description adds no parameter details because none are necessary. Per the rubric, 0 parameters warrants a baseline score of 4.
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 uses a specific verb 'check' and clearly identifies the resource ('the API'), making the tool's purpose unambiguous. It is distinctly different from sibling tools like get_talks, get_posts, and get_videos, which retrieve content rather than verify API health.
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 implies usage: if you need to verify the API is alive, use this tool. However, there is no explicit guidance on when to use it versus alternatives, nor any mention of prerequisites or exclusions. The context of sibling get_* tools suggests a read-only health check, but this is not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_postsB
Get a list of posts with optional filtering and pagination.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Filter posts by ID | |
| title | No | Filter posts by title | |
| language | No | Filter posts by language | |
| portal | No | Filter posts by portal | |
| skip | No | Number of posts to skip | |
| limit | No | Maximum number of posts to return |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, but it only says "Get a list of posts" and mentions filtering/pagination. It does not disclose whether the operation is read-only, requires authentication, has rate limits, or what the response structure is.
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 a single concise sentence that is front-loaded with the core action and resource. It avoids redundancy and is appropriately sized for a simple list operation.
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?
For a tool with 6 optional parameters and no output schema, this description is minimal but functionally adequate. It lacks context on filtering semantics (e.g., exact vs partial match), ordering, or intended use cases, but the core purpose is clear.
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?
Schema description coverage is 100%, so the baseline is 3. The description echoes the schema by mentioning "optional filtering" but adds no new detail about parameter values, defaults already in the schema, or filtering behavior.
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 gets a list of posts, using a specific verb ("Get") and resource ("posts"), and distinguishes it from siblings like get_talks and get_videos by the resource type.
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?
No guidance is provided on when to use this tool versus alternatives. It does not mention that get_talks or get_videos should be used for other content types, or any conditional context for choosing this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_talksA
Get a list of talks with optional filtering and pagination.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Filter talks by ID | |
| title | No | Filter talks by title | |
| language | No | Filter talks by language (e.g., 'spanish', 'english', 'portuguese' or direct codes like 'es', 'en', 'pt-br') | |
| city | No | Filter talks by city | |
| country | No | Filter talks by country | |
| year | No | Filter talks by year | |
| skip | No | Number of talks to skip | |
| limit | No | Maximum number of talks to return | |
| count_only | No | If true, returns only the count without talk details | |
| group_by | No | Group counts by a specific field (language, country, city) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It mentions optional filtering and pagination, adding some behavioral context. However, it doesn't disclose details like whether count_only changes the response shape, how group_by interacts with filters, or any rate limits. It's acceptable but not rich.
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 a single concise sentence that captures the core functionality without waste. It is front-loaded with the main purpose and includes key capabilities (filtering, pagination) in an efficient way.
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 has 10 parameters, no output schema, and no annotations, the description is minimal. It covers the basic purpose and mentions pagination/filtering, but it does not explain return values, edge cases, or how parameters combine. The schema descriptions help, but the overall context is not fully complete for a complex tool.
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?
Schema description coverage is 100%, meaning every parameter has a description in the schema. The tool description itself lists no parameter details, but since the schema already documents all ten parameters, the description does not need to repeat them. The baseline of 3 is appropriate because the schema does the heavy lifting.
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 states the tool lists talks with optional filtering and pagination, which is clear and specific. It does not explicitly distinguish it from sibling tools like get_posts or get_videos, but the resource 'talks' is unique enough that it's not misleading. The verb 'get a list' is clear.
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 implies this is for retrieving talk data, but it does not provide explicit when-to-use guidance or contrast with alternatives. Sibling tools are present, but no exclusions or preferred scenarios are given, so usage context is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_videosA
Get a list of videos with optional filtering and pagination.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Filter videos by ID | |
| title | No | Filter videos by title | |
| language | No | Filter videos by language | |
| skip | No | Number of videos to skip | |
| limit | No | Maximum number of videos to return |
TDQS
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 conveys that the tool is read-only and supports filtering and pagination, but it does not disclose return format, default values, ordering, or other behaviors. This is acceptable for a simple list tool but leaves room for ambiguity.
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 a single sentence that is concise, front-loaded with the core purpose, and contains no filler 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With five parameters and no output schema, the description is simplistic. It covers the basic functionality but omits return structure and parameter behaviors (e.g., exact vs. partial matching, default pagination values). For a straightforward list tool, this is adequate but not complete, especially given the lack of annotations or output schema.
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?
Schema description coverage is 100%, with each parameter (id, skip, limit, title, language) described in the schema. The description adds a high-level mention of 'optional filtering and pagination' but no additional detail beyond the schema, so the baseline 3 applies.
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 lists videos, using the verb 'Get' with the resource 'videos', and mentions optional filtering and pagination. It is specific enough to distinguish from sibling tools by resource name, though it does not explicitly differentiate itself from get_talks or get_posts beyond that.
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 implies when to use this tool: when listing videos with optional filters or pagination. However, it provides no explicit guidance on when not to use it or mention of alternatives like get_talks or get_posts, leaving usage context implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
4 tool updates
v1.1.4- First observed
check_status - First observed
get_posts - First observed
get_talks - First observed
get_videos
TDQS
Scored across 4 tools
Each tool targets a distinct resource or action: check_status is for API health, while get_posts, get_talks, and get_videos retrieve different content types. No overlap.
All tool names follow a consistent verb_noun pattern using underscores (check_status, get_posts, get_talks, get_videos), making them predictable.
With 4 tools, the set is well-scoped for a contributions-focused MCP, covering essential retrieval operations without being too sparse or bloated.
The tool surface provides read operations for all contribution types and a health check, but lacks write or update operations, which may be acceptable for a read-only server but leaves minor gaps.
Maintenance
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