Senden Sie Anfragen an OpenAI, MistralAI, Anthropic, xAI, Google AI, DeepSeek, Alibaba und Inception mithilfe des MCP-Protokolls über ein Tool oder vordefinierte Eingabeaufforderungen. Anbieter-API-Schlüssel erforderlich
Der Server implementiert ein Tool:
unichat : Senden Sie eine Anfrage an unichat
Nimmt "Nachrichten" als erforderliche String-Argumente an
Gibt eine Antwort zurück
code_review
Überprüfen Sie den Code auf bewährte Methoden, potenzielle Probleme und Verbesserungen
Argumente:
code (Zeichenfolge, erforderlich): Der zu überprüfende Code"
document_code
Generieren Sie Dokumentation für Code, einschließlich Docstrings und Kommentaren
Argumente:
code (Zeichenfolge, erforderlich): Der zu kommentierende Code"
explain_code
Erklären Sie im Detail, wie ein Codeteil funktioniert
Argumente:
code (Zeichenfolge, erforderlich): Der zu erklärende Code"
code_rework
Wenden Sie die gewünschten Änderungen am bereitgestellten Code an
Argumente:
changes (Zeichenfolge, optional): Die anzuwendenden Änderungen"
code (Zeichenfolge, erforderlich): Der zu überarbeitende Code"
Related MCP server: MCP AI Gateway
Unter MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json Unter Windows: %APPDATA%/Claude/claude_desktop_config.json
Unterstützte Modelle:
Eine Liste der aktuell unterstützten Modelle, die als
"SELECTED_UNICHAT_MODEL"verwendet werden können, finden Sie hier . Bitte fügen Sie den entsprechenden API-Schlüssel des Anbieters als"YOUR_UNICHAT_API_KEY"hinzu.
Beispiel:
"env": {
"UNICHAT_MODEL": "gpt-4o-mini",
"UNICHAT_API_KEY": "YOUR_OPENAI_API_KEY"
}Konfiguration von Entwicklungs-/unveröffentlichten Servern
"mcpServers": {
"unichat-mcp-server": {
"command": "uv",
"args": [
"--directory",
"{{your source code local directory}}/unichat-mcp-server",
"run",
"unichat-mcp-server"
],
"env": {
"UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
"UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
}
}
}Konfiguration veröffentlichter Server
"mcpServers": {
"unichat-mcp-server": {
"command": "uvx",
"args": [
"unichat-mcp-server"
],
"env": {
"UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
"UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
}
}
}So installieren Sie Unichat für Claude Desktop automatisch über Smithery :
npx -y @smithery/cli install unichat-mcp-server --client claudeSo bereiten Sie das Paket für die Verteilung vor:
Ältere Builds entfernen:
rm -rf distAbhängigkeiten synchronisieren und Sperrdatei aktualisieren:
uv syncErstellen Sie Paketverteilungen:
uv buildDadurch werden Quell- und Wheel-Distributionen im Verzeichnis dist/ erstellt.
Auf PyPI veröffentlichen:
uv publish --token {{YOUR_PYPI_API_TOKEN}}Da MCP-Server über stdio laufen, kann das Debuggen eine Herausforderung darstellen. Für ein optimales Debugging empfehlen wir dringend die Verwendung des MCP Inspector .
Sie können den MCP Inspector über npm mit diesem Befehl starten:
npx @modelcontextprotocol/inspector uv --directory {{your source code local directory}}/unichat-mcp-server run unichat-mcp-serverBeim Start zeigt der Inspector eine URL an, auf die Sie in Ihrem Browser zugreifen können, um mit dem Debuggen zu beginnen.
Chat with an assistant. Example tool use message: Ask the unichat to review and evaluate your proposal.
| Name | Required | Description | Default |
|---|---|---|---|
| messages | Yes | Array of exactly two messages: first a system message defining the task, then a user message with the specific query |
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 nothing about behavioral traits like whether this is a read-only operation, if it requires authentication, rate limits, or what kind of responses to expect. The example hints at evaluation tasks but doesn't disclose operational characteristics.
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 brief but includes an example that adds some value. However, the formatting with extra whitespace is awkward, and the example could be integrated more cleanly. It's not excessively verbose, but the structure could be improved for better front-loading of 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?
For a chat tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the assistant does, what domains it covers, what format responses take, or any limitations. The example provides minimal context but doesn't compensate for the lack of structured information about this interactive 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%, so the schema fully documents the single parameter (messages array with exactly two messages). The description adds no parameter information beyond what's in the schema, not even mentioning the two-message requirement. Baseline 3 is appropriate when schema does all the work.
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 'Chat with an assistant' which indicates the basic function, but it's vague about what this assistant does or what domain it operates in. The example tool use message adds some context about reviewing proposals, but doesn't make the purpose specific or distinguish it from other chat tools. It's not tautological but lacks clear differentiation.
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 explicit guidance on when to use this tool versus alternatives is provided. The example suggests it can be used for reviewing proposals, but there's no mention of prerequisites, limitations, or when not to use it. With no sibling tools, the bar is lower, but still lacks basic usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
unichatScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'unichat' has a clear and distinct purpose of chatting with an assistant.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'unichat' follows a simple, readable pattern without any conflicting conventions.
A single tool is too few for most server purposes, as it severely limits functionality and scope. While it might be appropriate for a minimal chat interface, it feels thin and lacks the depth expected for a typical MCP server, which usually requires multiple tools to handle different operations or resources.
For a chat assistant domain, the single tool 'unichat' covers the core action of chatting, but there are notable gaps. It lacks operations for managing chat history, configuring settings, or handling multiple sessions, which are common in chat systems. However, the basic functionality is present, allowing agents to perform the primary task.
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