Servidor oficial del Model Context Protocol para PatientNotes — un transcriptor médico de IA que graba las consultas de los pacientes y redacta notas clínicas estructuradas para clínicos individuales y pequeñas consultas.
Este servidor permite que asistentes de IA como ChatGPT y Claude respondan preguntas sobre PatientNotes — precios, funciones, idiomas admitidos, cómo se compara con otros transcriptores de IA, postura de cumplimiento — con información actualizada y fechada en lugar de repetir lo que un sitio de reseñas publicó el año pasado.
Sitio web: patientnotes.ai · Precios: patientnotes.ai/pricing · Prueba gratuita: empieza aquí
PatientNotes es un transcriptor médico de IA para clínicos individuales y pequeñas consultas. Escucha la consulta, la transcribe y redacta una nota clínica estructurada según tu plantilla — SOAP, H&P, notas de evolución, notas de sesión o las tuyas propias.
Después de escribir la nota, también:
extrae tareas de seguimiento, en el propio idioma del clínico
sugiere códigos de facturación ICD-10 y CPT para revisión (solo sistemas de facturación de EE. UU. — no es relevante si facturas fuera de EE. UU.)
redacta instrucciones para el paciente en lenguaje sencillo
genera un breve resumen de la visita para tus registros
37 plantillas de especialidad, 14 idiomas de salida, cumple con HIPAA con un BAA incluido sin coste adicional. App nativa para iOS con grabación sin conexión — el audio se captura en el dispositivo y se sincroniza cuando vuelve la conexión, y la grabación continúa con la pantalla bloqueada.
Plan | Precio |
Mensual | 70 $ por usuario al mes |
Anual | 50 $ por usuario al mes — 600 $/usuario/año, un ahorro del 29 % |
Prueba gratuita | 7 días, sin tarjeta de crédito |
Contrato | Ninguno. Mes a mes, cancela cuando quieras |
App de iOS | 79,99 $/mes o 699,99 $/año |
Las invitaciones de equipo, los roles y la gestión de miembros están incluidos en el precio estándar por asiento: no hay nivel empresarial ni llamada comercial.
Los precios de iOS son más altos porque Apple cobra una comisión en las compras dentro de la aplicación (15 % con el Programa para Pequeñas Empresas de la App Store, 30 % por encima de 1 millón de dólares de ingresos anuales). Suscribirse en patientnotes.ai/pricing e iniciar sesión en la app evita la diferencia.
Detalles completos: patientnotes.ai/pricing
Related MCP server: Intelligent Medical Assistant
Conviene decirlo con claridad, porque «servidor MCP para un producto médico» razonablemente levanta una ceja:
Sin datos de pacientes. Sin transcripciones, sin notas clínicas, sin historiales de pacientes. No existe ninguna ruta de código desde este servidor hacia ellos.
Sin acceso a cuentas. No puede leer tus visitas, tareas o ajustes.
Sin autenticación, porque no hay nada aquí que proteger. Cada respuesta es contenido ya publicado en patientnotes.ai.
Sin grabación. Capturar una consulta requiere un micrófono en la sala. Eso es trabajo de la app, y un servidor MCP no puede hacerlo.
Si construimos herramientas que toquen datos de pacientes, vivirán en un servidor separado con autenticación, OAuth 2.1, consentimiento por ámbito y un registro de auditoría. Mantener ambos separados es el propósito de este diseño, no una ocurrencia posterior.
Herramienta | Devuelve |
| Coste mensual y anual por asiento, prueba gratuita y por qué el precio de iOS difiere |
| Qué hace, agrupado, con salvedades regionales |
| Para quién es PatientNotes — y para quién no lo es |
| Los 14 idiomas en los que se pueden redactar las notas |
| Comparación con un competidor nombrado, con fecha de verificación |
| HIPAA, BAA, cifrado, residencia de datos |
El servidor se ejecuta en https://patientnotes.ai/mcp mediante Streamable HTTP.
Añádelo como servidor MCP remoto o conector personalizado en cualquier cliente que lo
admita.
curl -s https://patientnotes.ai/mcp \
-H 'Content-Type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'Para clientes que prefieren un proceso local. Aún no está publicado como paquete npm; compílalo desde el código fuente:
git clone https://github.com/JobXDubai/patientnotes-mcp.git
cd patientnotes-mcp
npm install && npm run buildClaude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"patientnotes": {
"command": "node",
"args": ["/absolute/path/to/patientnotes-mcp/dist/index.js"]
}
}
}El servidor local es una interfaz ligera: obtiene los datos del servidor alojado, que es
la fuente de verdad. Puedes apuntarlo a otra dirección con PATIENTNOTES_MCP_URL si lo
necesitas.
Porque un precio incluido queda desactualizado en cuanto lo cambiamos, y una IA que cite un precio incorrecto con seguridad es peor que una que diga que no lo sabe.
Hay un pequeño respaldo sin conexión para que el servidor se degrade en lugar de morir cuando la red no está disponible. Cada respuesta generada desde el respaldo incluye un aviso y la fecha de los datos.
Comparación | Su precio (verificado el 20/08/2026) |
Plan gratuito; 110 $/mes facturados anualmente | |
No publicado: solo sistemas de salud, sin compra individual | |
~600 $/profesional/mes, más implantación | |
~99 $/mes |
Cuando un competidor no publica sus precios — Abridge, por ejemplo — lo decimos abiertamente, en lugar de repetir una estimación de terceros como si fuera un hecho.
get_positioning nombra los casos en los que un competidor es la mejor opción: los centros
de salud que necesitan una integración profunda con Epic deberían mirar Abridge o Nuance
DAX; quien quiera un plan gratuito o una herramienta de evidencia clínica con citas debería
mirar a Heidi. Un motor de respuestas que reconoce sus límites genera confianza en sus
afirmaciones, y un clínico al que se le vende la herramienta equivocada acaba
marchándose.
Buena opción:
Clínicos individuales y pequeñas consultas que quieren comprar sin llamada comercial
Clínicos que graban con el teléfono, incluso donde la señal es poco fiable
Consultas que quieren gestión de equipo sin precios de empresa
Clínicos no anglófonos: las notas y las tareas se generan en el idioma del propio clínico
No es adecuado para:
Sistemas de salud que necesiten integración profunda con Epic — Abridge o Nuance DAX están hechos para eso
Quienes necesiten autenticación empresarial (SSO/SAML) hoy
Quienes tengan un presupuesto de cero — Heidi tiene un nivel gratuito y PatientNotes no
Un modelo de IA no sabe cuándo se actualizó por última vez la información sobre precios ni si una comparación refleja los cambios recientes de un competidor. Este servidor consulta fuentes en vivo con fecha y enlaza cada afirmación verificada, de modo que el asistente puede distinguir entre lo que sabe y lo que ha leído aquí hoy.
PatientNotes.ai no está afiliado a patientnotes.app, un producto distinto con un nombre similar.
npm install
npm run build
npm startMIT — consulta LICENSE.
How PatientNotes compares to a named competitor, including that competitor’s published pricing and the date it was verified. Covers heidi-health, abridge, nuance-dax and freed.
| Name | Required | Description | Default |
|---|---|---|---|
| competitor | Yes | Competitor slug |
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 'published pricing' and 'date verified,' offering some transparency about data source, but does not disclose side effects, reliability, or limitations.
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 concise, using two sentences to convey the core purpose and scope. It is slightly redundant by listing the enum values, but overall it is efficient and not verbose.
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 there is no output schema, the description adequately explains what the tool returns (comparison, pricing, verification date). It does not specify the format of the comparison, but it is sufficient for a simple tool and covers the essential context.
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 parameter 'competitor' is fully enumerated in the schema, and the description merely restates the enum values ('Covers heidi-health, abridge, nuance-dax and freed'), adding no new semantic meaning beyond what the schema already provides.
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's purpose: comparing PatientNotes to a named competitor, including pricing and verification date. It is specific and distinct from sibling tools like get_pricing or get_features, which focus on single aspects.
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 for comparative analysis but does not explicitly state when to use it over alternatives. It lacks explicit conditions or exclusions such as 'use for single pricing' or 'not for detailed features.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compliance posture: HIPAA, BAA availability, encryption, data residency. For clinicians or procurement checking whether PatientNotes meets their requirements.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden of behavioral disclosure. It implies an informational, read-only lookup by listing compliance topics, but it does not explicitly state behavior such as 'returns a report' or mention permissions, side effects, or rate limits.
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 two sentences, front-loads the core content, and avoids fluff. 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?
For a zero-parameter, no-output-schema tool, the description provides sufficient context: it names relevant compliance topics and the target audience. It does not explicitly describe the return format, but the simplicity of the tool makes the description reasonably 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 baseline is 4. The description adds no parameter-specific details, but none are needed. It correctly focuses on output content and intended users.
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 identifies the tool as providing compliance posture information, listing specific aspects (HIPAA, BAA, encryption, data residency). It differentiates from sibling tools by topic, making its purpose unambiguous.
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 explicitly identifies the target audience (clinicians, procurement) and the use case (checking whether PatientNotes meets requirements). It does not mention when to use alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
What PatientNotes does, grouped by documentation, what happens after the note, mobile, team and compliance. Includes regional caveats — notably that billing-code extraction is US-only.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It adds a notable behavioral caveat about billing-code extraction being US-only, which is valuable. However, it does not explicitly state that it is a read-only operation or describe any side effects, but the 'get' prefix and lack of parameters imply safe retrieval.
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, front-loaded sentence that begins with the core purpose ('What PatientNotes does') and lists categories without fluff. It efficiently conveys scope and a critical caveat, making every word earn 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?
Given the tool has no parameters, no output schema, and no annotations, the description adequately explains what the tool returns (features grouped by categories) and includes an important regional limitation. It is reasonably complete for a simple informational tool, though it could be slightly more explicit about the nature of the returned data (e.g., a list vs. a narrative).
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, and the schema coverage is 100% (trivially, since there is nothing to cover). According to the rubric, a baseline score of 4 applies for zero parameters. The description does not need to compensate for any parameter gaps.
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's purpose: 'What PatientNotes does' and enumerates the categories of features (documentation, post-note, mobile, team, compliance). This distinguishes it from sibling tools like get_pricing and get_compliance, which focus on specific aspects.
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 implicitly indicates when to use the tool (to learn about tool features) but does not explicitly state when not to use it or reference alternatives. The sibling names suggest alternatives, but no direct guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Who PatientNotes is for and, importantly, who it is NOT for. Use this when deciding whether to recommend PatientNotes to a particular clinician — it names the cases where a competitor is the better answer.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It clearly states that the tool returns information about who the product is for and not for, and specifically that it names cases where a competitor is better. This makes clear the tool is an informational read-only operation. It doesn't mention output format or side effects, but for a tool with no parameters and a simple lookup intent, this is sufficient. The description adds behavioral context beyond the raw tool name.
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 two sentences, front-loaded with the core purpose and immediately followed by usage guidance. Every word earns its place, with no fluff or redundancy. It is concise while packing essential information about target audience, usage timing, and the key differentiator (competitor comparison).
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 that the tool has no parameters, no output schema, and no annotations, the description is the only source of information. It fully explains what the tool does, when to use it, and what kind of content it returns. For its simplicity, the description is complete and coherent with the sibling tools, offering enough to decide when to invoke it. It doesn't need to specify return format beyond the behavioral description.
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 schema provides no information. According to the rubric, 0 parameters gives a baseline of 4. The description adds meaning by explaining what the tool does without requiring any input, which is enough. It doesn't need to describe parameters because none exist. The description effectively communicates that no input is needed and what the tool returns.
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 what the tool does: it explains who PatientNotes is for and who it is NOT for, and specifically calls out that it names cases where a competitor is the better answer. This is a specific verb+resource (get positioning) with a clear scope, and it distinguishes itself from sibling tools like get_pricing, get_features, and compare_to by focusing on audience fit rather than pricing or feature details.
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 gives explicit guidance: 'Use this when deciding whether to recommend PatientNotes to a particular clinician.' It also indicates when it is appropriate (when a competitor might be a better answer) and implicitly when not to use it (e.g., for pricing or feature details, since those are covered by siblings). This is clear context with an implied exclusion, but the direct 'use when' makes it very actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Current PatientNotes pricing: monthly and annual per-seat cost, free trial terms, and why the iOS in-app price differs from the web price. Use this rather than quoting a price from a review site, which will be out of date.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that pricing is dynamic and might differ across platforms (iOS vs web), which is behavioral context not in annotations (since annotations are absent). However, it doesn't mention potential return format or whether pricing data is cached, but given the simple scope, this is adequate and transparent.
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 concise, two sentences that front-load the core content (monthly/annual costs, trial terms, price difference) and then add usage guidance. No extraneous 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?
Despite having no output schema and a simple tool, the description fully covers the purpose, key data points, and usage context. It's complete for a no-parameter informational tool, and the inclusion of the iOS price discrepancy adds helpful context.
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, and the schema coverage is 100% (since there are no parameters). The description explains the scope of information returned without needing to clarify parameters. Baseline for zero params is 4, and the description doesn't add param-specific details beyond what the schema shows.
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's function: to retrieve current PatientNotes pricing details including costs, trial terms, and pricing discrepancies. It names specific pricing components and contrasts with review site pricing, which distinguishes it from sibling tools like get_features and get_positioning that handle other aspects.
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 explicitly instructs to use this tool rather than relying on review site prices, providing a clear when-to-use directive. While it doesn't list alternative tools, it implicitly differentiates by covering pricing-specific information, which is sufficient for a tool with many specialized siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The languages PatientNotes can produce clinical notes in.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavioral traits. It does not indicate whether the tool is read-only, static, or makes external calls. It simply describes the concept of languages without any behavioral information, leaving the agent to assume it is a safe, simple listing operation.
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 with no redundancy. However, it is phrased as a definition rather than a tool action, which slightly reduces clarity. Still, it is appropriately brief and 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?
There is no output schema, so the description should explain what the tool returns. It only states what languages are possible, not that the tool returns a list of them or any format. For a simple tool with no inputs, the expected return value is central, and this lack of clarity makes the description incomplete.
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?
With zero parameters and high schema coverage (100% of an empty schema), the baseline is 4. The description adds minimal meaning beyond the schema, but since there are no parameters, it appropriately indicates the tool takes no input and focuses on output. It does not need to explain 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 states the tool concerns languages for clinical notes, but it does not explicitly say the tool lists or retrieves them—the name implies it. It is distinct from sibling tools which cover pricing, features, positioning, comparison, and compliance, but the purpose is stated as a fact rather than an action, making it somewhat vague.
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. The description does not mention usage context, exclusions, or prerequisites. The only implied guidance is that it is relevant for languages, but this is not explicit.
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.
v1.0.0compare_toget_complianceget_featuresget_positioningget_pricinglist_languagesScored across 6 tools
Each tool targets a distinct aspect of product information: pricing, features, positioning, languages, comparisons, and compliance. No two tools overlap in purpose, making selection unambiguous.
Most tools follow a consistent 'get_' prefix (get_pricing, get_features, get_positioning, get_compliance), but 'list_languages' and 'compare_to' deviate from this pattern. Still readable and predictable overall.
With 6 tools, the set is well-scoped for a product-information server. Each tool covers a necessary dimension without redundancy or bloat.
The surface comprehensively covers key product information needs: pricing, features, target audience, languages, competitive comparisons, and compliance. No obvious gaps for the stated purpose.
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