Accede a múltiples APIs de LLM desde un solo lugar. Llama a ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, Mistral y al enrutador de inferencia de Hugging Face con selección inteligente de modelos, preferencias y registro de prompts.
Un servidor MCP (Protocolo de Contexto de Modelo) que proporciona acceso unificado a múltiples APIs de Modelos de Lenguaje Extensos para entornos de programación con IA como Cursor y Claude Desktop.
🌐 9 Proveedores de LLM – ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, Mistral, Hugging Face
🎯 Selección Inteligente de Modelos – Preferencias basadas en etiquetas (programación, negocios, razonamiento, matemáticas, creativo, general)
📊 Registro de Prompts – Rastrea todos los prompts con historial, estadísticas y análisis
💰 Optimización de Costes – Elige modelos insignia o más económicos según tus preferencias
⚡ Configuración Fácil – Instalación con un clic en Cursor o configuración manual sencilla
🔄 Llama a todos los LLMs – Obtén respuestas de todos los proveedores simultáneamente
Related MCP server: OpenRouter MCP Server
¿Listo para acceder a múltiples LLMs? Instálalo en segundos:
Instalar en Cursor (Recomendado):
O instálalo manualmente:
npm install -g cross-llm-mcp
# Or from source:
git clone https://github.com/JamesANZ/cross-llm-mcp.git
cd cross-llm-mcp && npm install && npm run buildcall-chatgpt – API de ChatGPT de OpenAI
call-claude – API de Claude de Anthropic
call-deepseek – API de DeepSeek
call-gemini – API de Gemini de Google
call-grok – API de Grok de xAI
call-kimi – API de Kimi de Moonshot AI
call-perplexity – API de Perplexity AI
call-mistral – API de Mistral AI
call-huggingface – Enrutador de Inferencia de Hugging Face (modelos del Hub compatibles con OpenAI)
call-all-llms – Llama a todos los LLMs con el mismo prompt
call-llm – Llama a un proveedor específico por su nombre
get-user-preferences – Obtén las preferencias actuales
set-user-preferences – Establece el modelo predeterminado, preferencia de coste y preferencias basadas en etiquetas
get-models-by-tag – Encuentra modelos por etiqueta (programación, negocios, razonamiento, matemáticas, creativo, general)
get-prompt-history – Visualiza el historial de prompts con filtros
get-prompt-stats – Obtén estadísticas sobre los registros de prompts
delete-prompt-entries – Elimina entradas de registro según criterios
clear-prompt-history – Borra todos los registros de prompts
Haz clic en el enlace de instalación de arriba o usa:
cursor://anysphere.cursor-deeplink/mcp/install?name=cross-llm-mcp&config=eyJjcm9zcy1sbG0tbWNwIjp7ImNvbW1hbmQiOiJucHgiLCJhcmdzIjpbIi15IiwiY3Jvc3MtbGxtLW1jcCJdfX0=Después de la instalación, añade tus claves de API en la configuración de Cursor (consulta la Configuración a continuación).
Requisitos: Node.js 18+ y npm
# Clone and build
git clone https://github.com/JamesANZ/cross-llm-mcp.git
cd cross-llm-mcp
npm install
npm run buildAñádelo a claude_desktop_config.json:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"cross-llm-mcp": {
"command": "node",
"args": ["/absolute/path/to/cross-llm-mcp/build/index.js"],
"env": {
"OPENAI_API_KEY": "your_openai_api_key_here",
"ANTHROPIC_API_KEY": "your_anthropic_api_key_here",
"DEEPSEEK_API_KEY": "your_deepseek_api_key_here",
"GEMINI_API_KEY": "your_gemini_api_key_here",
"XAI_API_KEY": "your_grok_api_key_here",
"KIMI_API_KEY": "your_kimi_api_key_here",
"PERPLEXITY_API_KEY": "your_perplexity_api_key_here",
"MISTRAL_API_KEY": "your_mistral_api_key_here",
"HF_TOKEN": "your_huggingface_token_here"
}
}
}
}Reinicia Claude Desktop después de la configuración.
Establece variables de entorno para los proveedores de LLM que desees utilizar:
export OPENAI_API_KEY="your_openai_api_key"
export ANTHROPIC_API_KEY="your_anthropic_api_key"
export DEEPSEEK_API_KEY="your_deepseek_api_key"
export GEMINI_API_KEY="your_gemini_api_key"
export XAI_API_KEY="your_grok_api_key"
export KIMI_API_KEY="your_kimi_api_key"
export PERPLEXITY_API_KEY="your_perplexity_api_key"
export MISTRAL_API_KEY="your_mistral_api_key"
export HF_TOKEN="your_huggingface_token"
# Or: HUGGINGFACE_API_KEY (same as HF_TOKEN)
# Optional: DEFAULT_HUGGINGFACE_MODEL, HUGGINGFACE_INFERENCE_BASE_URL (default https://router.huggingface.co/v1)Anthropic: https://console.anthropic.com/
DeepSeek: https://platform.deepseek.com/
Google Gemini: https://makersuite.google.com/app/apikey
xAI Grok: https://console.x.ai/
Moonshot AI: https://platform.moonshot.ai/
Perplexity: https://www.perplexity.ai/hub
Mistral: https://console.mistral.ai/
Hugging Face: Crea un token de grano fino con acceso a Inference (serverless / Inference Providers) en https://huggingface.co/settings/tokens. Consulta Chat Completion para ver los modelos compatibles.
Este servidor llama al enrutador de inferencia alojado de Hugging Face; no descarga pesos ni ejecuta PyTorch/GGUF dentro de Node. Para ejecutar modelos en tu máquina, utiliza herramientas como Ollama, llama.cpp, Text Generation Inference o Inference Endpoints de Hugging Face, y luego apunta otros clientes a esos servicios si exponen una API.
Obtén una respuesta de OpenAI:
{
"tool": "call-chatgpt",
"arguments": {
"prompt": "Explain quantum computing in simple terms",
"temperature": 0.7,
"max_tokens": 500
}
}Obtén una respuesta de un modelo del Hub a través del enrutador de inferencia (model es el ID del repositorio del Hub, p. ej., Qwen/Qwen2.5-7B-Instruct):
{
"tool": "call-huggingface",
"arguments": {
"prompt": "Reply with exactly: ok",
"model": "Qwen/Qwen2.5-7B-Instruct",
"temperature": 0.3,
"max_tokens": 32
}
}Obtén respuestas de todos los proveedores:
{
"tool": "call-all-llms",
"arguments": {
"prompt": "Write a short poem about AI",
"temperature": 0.8
}
}Utiliza automáticamente el mejor modelo para cada tipo de tarea:
{
"tool": "set-user-preferences",
"arguments": {
"defaultModel": "gpt-4o",
"costPreference": "cheaper",
"tagPreferences": {
"coding": "deepseek-r1",
"general": "gpt-4o",
"business": "claude-3.5-sonnet-20241022",
"reasoning": "deepseek-r1",
"math": "deepseek-r1",
"creative": "gpt-4o"
}
}
}Visualiza tus registros de prompts:
{
"tool": "get-prompt-history",
"arguments": {
"provider": "chatgpt",
"limit": 10
}
}Los modelos están etiquetados según sus fortalezas:
coding: deepseek-r1, deepseek-coder, gpt-4o, claude-3.5-sonnet-20241022
business: claude-3-opus-20240229, gpt-4o, gemini-1.5-pro
reasoning: deepseek-r1, o1-preview, claude-3.5-sonnet-20241022
math: deepseek-r1, o1-preview, o1-mini
creative: gpt-4o, claude-3-opus-20240229, gemini-1.5-pro
general: gpt-4o-mini, claude-3-haiku-20240307, gemini-1.5-flash
Análisis Multi-Perspectiva – Obtén diferentes perspectivas de múltiples LLMs
Comparación de Modelos – Compara respuestas para entender fortalezas y debilidades
Optimización de Costes – Elige el modelo más rentable para cada tarea
Aseguramiento de Calidad – Referencia cruzada de respuestas de múltiples modelos
Selección Inteligente – Utiliza automáticamente el mejor modelo para programación, negocios, razonamiento, etc.
Análisis de Prompts – Rastrea el uso, costes y patrones con registro automático
Construido con: Node.js, TypeScript, MCP SDK
Dependencias: @modelcontextprotocol/sdk, superagent, zod
Plataformas: macOS, Windows, Linux
Almacenamiento de Preferencias:
Unix/macOS: ~/.cross-llm-mcp/preferences.json
Windows: %APPDATA%/cross-llm-mcp/preferences.json
Almacenamiento de Registro de Prompts:
Unix/macOS: ~/.cross-llm-mcp/prompts.json
Windows: %APPDATA%/cross-llm-mcp/prompts.json
⭐ Si este proyecto te ayuda, ¡por favor dale una estrella en GitHub! ⭐
¡Las contribuciones son bienvenidas! Por favor, abre un issue o envía un pull request.
Licencia MIT – consulta LICENSE.md para más detalles.
Si encuentras útil este proyecto, considera apoyarlo:
⚡ Lightning Network
lnbc1pjhhsqepp5mjgwnvg0z53shm22hfe9us289lnaqkwv8rn2s0rtekg5vvj56xnqdqqcqzzsxqyz5vqsp5gu6vh9hyp94c7t3tkpqrp2r059t4vrw7ps78a4n0a2u52678c7yq9qyyssq7zcferywka50wcy75skjfrdrk930cuyx24rg55cwfuzxs49rc9c53mpz6zug5y2544pt8y9jflnq0ltlha26ed846jh0y7n4gm8jd3qqaautqa₿ Bitcoin: bc1ptzvr93pn959xq4et6sqzpfnkk2args22ewv5u2th4ps7hshfaqrshe0xtp
Ξ Ethereum/EVM: 0x42ea529282DDE0AA87B42d9E83316eb23FE62c3f
Decode a Lightning invoice
| Name | Required | Description | Default |
|---|---|---|---|
| invoice | Yes | BOLT11 Lightning invoice |
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 action 'decode' but doesn't explain what decoding entails (e.g., extracting payment details, checking validity, or returning structured data). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and output.
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, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly. No unnecessary words or redundant information are included.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., decoded fields like amount, timestamp, or destination) or potential errors (e.g., invalid invoice format). For a decoding tool with no structured output documentation, this leaves the agent guessing about results.
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 the parameter 'invoice' documented as a 'BOLT11 Lightning invoice'. The description doesn't add any meaning beyond this, such as format examples or validation rules. Since the schema already provides adequate coverage, the baseline score of 3 is appropriate.
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 verb 'decode' and the resource 'Lightning invoice', making the purpose immediately understandable. It doesn't differentiate from siblings like 'decode_tx' or 'pay_invoice', but the core action is specific enough to understand what the tool does.
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 like 'decode_tx' or 'validate_address'. It doesn't mention prerequisites, such as needing a valid BOLT11 invoice, or clarify that this is for decoding rather than processing payments (which 'pay_invoice' handles).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Decode a Bitcoin transaction
| Name | Required | Description | Default |
|---|---|---|---|
| rawHex | Yes | Transaction hex |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It doesn't disclose whether this is a read-only operation, if it requires network access, potential rate limits, error conditions, or what the decoded output looks like. The description is functional but lacks context about how the tool behaves.
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, efficient sentence with zero wasted words. It's front-loaded with the core purpose and 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is inadequate. It doesn't explain what 'decode' entails (e.g., parsing inputs/outputs, scripts), the format of the result, or error handling. Given the complexity of Bitcoin transactions and lack of structured context, more completeness is needed.
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 schema description coverage is 100%, with the parameter 'rawHex' fully documented in the schema as 'Transaction hex'. The description adds no additional meaning beyond this, so it meets the baseline for high schema coverage without compensating value.
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 verb ('decode') and resource ('a Bitcoin transaction'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_transaction' or 'decode_invoice', which prevents 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?
No guidance is provided about when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing raw hex data), exclusions, or comparisons to sibling tools like 'get_transaction' (which might retrieve transaction details differently).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a new Bitcoin key pair and address
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states what the tool does without behavioral details. It doesn't disclose if this requires network connectivity, has rate limits, stores keys securely, or what format the output takes (e.g., public/private keys, address type). This leaves significant gaps for agent understanding.
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, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core action and resource, making it highly concise and well-structured.
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 complexity of generating cryptographic keys and the lack of annotations and output schema, the description is incomplete. It doesn't explain return values (e.g., key formats), security implications, or error conditions, leaving the agent with insufficient context for safe and effective use.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately avoids discussing parameters, focusing on the tool's purpose. A baseline of 4 is applied as it compensates adequately for the lack of parameters.
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 action ('Generate') and the resource ('a new Bitcoin key pair and address'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'validate_address' or 'pay_invoice', which prevents 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 alternatives like 'validate_address' for checking existing addresses or 'pay_invoice' for transactions. It lacks context about prerequisites, such as needing Bitcoin network access or when key generation is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the latest block
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action without details on permissions, rate limits, response format, or potential side effects. For a tool with zero annotation coverage, this is insufficient to inform the agent adequately.
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 extremely concise with a single sentence, 'Get the latest block', which is front-loaded and wastes no words. It efficiently conveys the core purpose without unnecessary elaboration.
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 lack of annotations and output schema, the description is incomplete. It does not explain what the tool returns (e.g., block data structure, error handling) or provide context for its use among siblings. For a tool with no structured support, more descriptive content is needed.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not add parameter information, which is appropriate here, but it could have clarified the lack of parameters explicitly. Baseline is 4 due to the absence of parameters.
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 'Get the latest block' clearly states the action (get) and resource (latest block), making the purpose understandable. However, it lacks specificity about what a 'block' refers to in this context (e.g., blockchain block, data block) and does not differentiate from sibling tools like 'get_transaction', leaving room for ambiguity.
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. It does not mention any prerequisites, context, or comparisons to sibling tools such as 'get_transaction' or 'decode_tx', leaving the agent without usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get transaction details
| Name | Required | Description | Default |
|---|---|---|---|
| txid | Yes | Transaction ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the basic action without disclosing behavioral traits such as whether this is a read-only operation, error handling, rate limits, or authentication needs. It mentions 'details' but doesn't specify what those include.
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 extremely concise with 'Get transaction details'—a single, front-loaded sentence that efficiently conveys the core purpose without unnecessary words. However, it may be overly terse for a tool with no annotations or output schema.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what transaction details are returned, error conditions, or how it differs from sibling tools. For a tool with one parameter but no structured context, more information is needed.
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 input schema has 100% description coverage, clearly documenting the 'txid' parameter. The description adds no additional meaning beyond the schema, so it meets the baseline of 3 for adequate but not enhanced parameter semantics.
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 'Get transaction details' states the basic action (get) and resource (transaction details), but it's vague about what specific details are retrieved and doesn't differentiate from sibling tools like 'decode_tx' or 'get_latest_block'. It provides minimal but adequate purpose information.
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 like 'decode_tx' (which might decode transaction data) or 'get_latest_block' (which retrieves block information). The description lacks context about prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Pay a Lightning invoice
| Name | Required | Description | Default |
|---|---|---|---|
| invoice | Yes | BOLT11 Lightning invoice |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states 'Pay a Lightning invoice' which implies a financial transaction, but doesn't clarify if this is irreversible, requires authentication, has rate limits, or what happens on success/failure. This is inadequate for a payment tool with zero 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 a single, efficient sentence with zero wasted words. It's appropriately sized for a simple tool with one parameter and gets straight to the point without unnecessary elaboration.
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 payment tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after payment (success confirmation, error handling), doesn't mention security implications, and provides minimal behavioral context despite the tool's financial nature.
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 input schema has 100% description coverage, with the 'invoice' parameter documented as 'BOLT11 Lightning invoice'. The description doesn't add any additional meaning beyond what the schema provides, such as format examples or validation requirements, so it meets the baseline for high schema coverage.
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 action ('Pay') and target resource ('a Lightning invoice'), making the purpose immediately understandable. It doesn't differentiate from siblings like 'decode_invoice' or 'get_transaction', but it's specific enough to understand what the tool does.
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 like 'decode_invoice' or 'validate_address'. It doesn't mention prerequisites, such as requiring a valid invoice or sufficient balance, leaving the agent to infer usage context from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate a Bitcoin address
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | The Bitcoin address to validate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Validate' implies a read-only check, but the description doesn't specify what validation entails (format, checksum, network type), whether it requires network connectivity, what happens with invalid inputs, or what the output format will be. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple validation tool and is perfectly front-loaded.
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 validation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what constitutes validation, what the tool returns (success/failure, validation details, error messages), or how it differs from related sibling tools. The agent would lack critical context to use this tool effectively.
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 schema has 100% description coverage, with the single parameter 'address' clearly documented as 'The Bitcoin address to validate'. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.
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 verb ('validate') and resource ('Bitcoin address'), making the purpose immediately understandable. However, it doesn't differentiate this validation tool from potential sibling tools that might also validate addresses in different contexts or with different criteria.
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. It doesn't mention whether this is for address format validation, network compatibility checking, or other specific validation contexts, nor does it reference any sibling tools that might serve related purposes.
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.
decode_invoicedecode_txgenerate_keyget_latest_blockget_transactionpay_invoicevalidate_addressScored across 7 tools
Each tool has a clearly distinct purpose targeting specific resources and actions in the Bitcoin/Lightning domain. For example, decode_invoice and pay_invoice handle Lightning payments, while decode_tx and get_transaction handle Bitcoin transactions, with no overlapping functionality that would cause confusion.
All tool names follow a consistent verb_noun pattern using snake_case, such as decode_invoice, generate_key, and validate_address. This uniformity makes the tool set predictable and easy to understand for agents.
With 7 tools, the server is well-scoped for its purpose of Bitcoin and Lightning operations. Each tool serves a specific, necessary function without redundancy, making the count appropriate for the domain's core needs.
The tool set covers key operations like decoding, generating, validating, and paying, but there are minor gaps such as creating invoices or managing wallet balances. However, agents can still perform essential workflows with the provided tools.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
Real-time chat hub for AI agents — Claude Code, Cursor, Cline, Codex over MCP or REST.
MCP server for building and testing AI agents with multi-model experimentation and insights.
