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SentiSift MCP

Name: SentiSift MCP
Author: pickelfintech

by pickelfintech

Overview Schema Source Related Servers Discussions Admin

Python

Remote

Servidor MCP de SentiSift

Servidor oficial del Model Context Protocol (MCP) para SentiSift. Permite que los hosts de IA compatibles con MCP (Claude Desktop, Cursor, VS Code, Continue y otros) llamen a la API de moderación de comentarios e inteligencia de SentiSift como una herramienta nativa.

Qué permite esto

Una vez instalado, tu asistente de IA puede:

Analizar comentarios directamente en la conversación: "Pasa estos 50 comentarios por SentiSift y muéstrame los bots y la distribución de sentimiento."
Comprobar el estado de tu cuenta: "¿Cuál es mi saldo en SentiSift y cuándo se renueva mi suscripción?"
Recuperar resultados históricos: "Muéstrame el historial de sentimiento del artículo de ayer en example.com/breaking-news."
Autodiagnosticar el estado del servicio: "¿Está lista la API de SentiSift o los modelos aún se están cargando?"

No se requiere código. El asistente llama a las herramientas a través de MCP e informa de los resultados.

Related MCP server: Semanticscholar Universal MCP Server

Herramientas expuestas

Herramienta	Descripción
`analyze_comments`	Envía un lote de comentarios junto con la URL de un artículo, recibe indicadores de bot/spam, etiquetas de sentimiento, indicadores de contenido comercial, resumen de moderación y (en Pro/Enterprise) Inteligencia + comentarios de Influence intercalados.
`get_balance`	Saldo actual de comentarios, nivel, indicadores de funciones, subvenciones activas, estado de la suscripción, estadísticas de Influence.
`get_health`	Si la API de SentiSift está lista o aún cargando modelos.
`get_article_results`	Comentarios puntuados históricos para la URL de un artículo (Pro/Enterprise conservan el historial completo; Free/Starter solo devuelven el estado del búfer).

Instalación

Necesitas Python 3.10+ y una clave de API de SentiSift. Claves gratuitas (1.000 comentarios, sin tarjeta de crédito) en sentisift.com/pricing.

Recomendado: `uvx` (sin instalación, siempre la última versión)

uv ejecuta aplicaciones de Python publicadas sin instalación persistente. Esta es la ruta sin instalación.

# One-time: install uv (mac/linux)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Verify it works (Ctrl-C after a few seconds; the server is meant to be
# launched by your AI host, not run in a terminal).
SENTISIFT_API_KEY=sk_sentisift_... uvx sentisift-mcp

Alternativa: `pip install`

pip install sentisift-mcp
sentisift-mcp   # run manually to verify

Configuración del host

Cada host de IA tiene su propio archivo de configuración. Elige el tuyo:

Claude Desktop

Edita el archivo de configuración:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Añade la entrada de SentiSift a mcpServers:

{
  "mcpServers": {
    "sentisift": {
      "command": "uvx",
      "args": ["sentisift-mcp"],
      "env": {
        "SENTISIFT_API_KEY": "sk_sentisift_your_key_here"
      }
    }
  }
}

Reinicia Claude Desktop. Deberías ver un nuevo indicador de herramienta en la esquina inferior izquierda de un nuevo chat. Prueba: "Use SentiSift to check my balance."

Cursor

Cursor admite MCP a través del panel Settings → MCP Servers. Añade:

Name: sentisift
Command: uvx
Arguments: sentisift-mcp
Environment variables: SENTISIFT_API_KEY=sk_sentisift_your_key_here

O edita ~/.cursor/mcp.json directamente con la misma estructura que el ejemplo de Claude Desktop anterior.

VS Code (con la extensión MCP)

Edita tu settings.json de usuario o de espacio de trabajo:

{
  "mcp.servers": {
    "sentisift": {
      "command": "uvx",
      "args": ["sentisift-mcp"],
      "env": {
        "SENTISIFT_API_KEY": "sk_sentisift_your_key_here"
      }
    }
  }
}

Continue.dev

En ~/.continue/config.json:

{
  "mcpServers": [
    {
      "name": "sentisift",
      "command": "uvx",
      "args": ["sentisift-mcp"],
      "env": {
        "SENTISIFT_API_KEY": "sk_sentisift_your_key_here"
      }
    }
  ]
}

Cualquier otro host compatible con MCP

El servidor habla MCP a través de stdio. Apunta tu host a uvx sentisift-mcp (o sentisift-mcp si se instaló mediante pip) y establece SENTISIFT_API_KEY en su entorno.

Cómo hablar con el asistente

Una vez configurado, funcionan las instrucciones en lenguaje natural. Algunos ejemplos:

"Check my SentiSift balance."

"Run these 20 comments through SentiSift for the article at https://example.com/breaking-story. Here are the comments:" (pégalos)

"Is the SentiSift API ready right now, or is it still loading?"

"Fetch the processed comments for https://example.com/article-42 from SentiSift."

El asistente elige la herramienta correcta, pasa los argumentos correctos y resume los resultados. Si una llamada falla, la respuesta de error incluye un enlace profundo a la sección de documentación relevante para que el asistente pueda autocorregirse.

Privacidad

El servidor envía una cabecera User-Agent que lo identifica como sentisift-mcp/<version> para que podamos distinguir el tráfico MCP del tráfico SDK directo en nuestros registros de uso. No hay otra telemetría.
El servidor se ejecuta localmente en tu máquina. Solo realiza llamadas a api.sentisift.com.
Tu clave de API permanece en tu máquina (en la configuración de entorno del host). Nunca se registra en el disco por parte de este servidor.

Fuente

Fuente y seguimiento de problemas: gitlab.com/pickel-fintech/sentisift-sdks.

Enlaces

Documentación de la API de SentiSift
Especificación OpenAPI
SDK de Python (este servidor MCP lo envuelve)
SDK de Node
Registro de cambios
Soporte

Licencia

MIT — ver LICENSE. El servicio de API de SentiSift que llama este cliente se rige por separado por sentisift.com/terms.html; el uso de la API requiere una clave de API de sentisift.com/pricing.

Available Tools

4 tools

analyze_commentsA

Submit a batch of comments to SentiSift for moderation and analysis.

Returns a response with status='buffered' (accepted, not yet analyzed, nothing charged) or status='processed' (full analysis returned).

Processed responses include per-comment sentiment labels (Toxic, Negative, Neutral, Positive, Saccharine), bot/spam flags (the response 'comments' array has those removed), composite scores, and on Professional/Enterprise tiers, crowd-level 'intelligence' (discussion_themes, omega_ratio, sentiment_balance) plus interleaved Influence comments marked with is_influence=true.

Comments are buffered per article until a processing threshold is reached, then all accumulated comments are analyzed together. You are billed only when processing occurs.

Batch size caps: 50 comments per call on the Free tier, 2000 on paid tiers.

ParametersJSON Schema

Name	Required	Description
`article_url`	Yes	Full URL of the article the comments belong to (e.g. 'https://example.com/article/42'). Used to group comments and accumulate Intelligence. URLs are normalized server-side.
`comments`	Yes	Array of comment objects. Each must have 'text' (str), 'author' (str), and 'time' (ISO 8601 str). Optional fields: 'likes' (int), 'dislikes' (int), 'is_reply' (bool). If real author/time are unknown, synthesize stable placeholders (e.g. author='anonymous-1', time='2026-04-18T10:00:00').
`article_text`	No	Full article body. STRONGLY RECOMMENDED on the first call for each article (we cache it and use it for contextual analysis and Influence generation). Skip on later calls for the same article URL.
`title`	No	Article title, for the article profile.
`tone`	No	Brand voice for Influence comments when applicable (e.g. 'professional and measured', 'warm and community-oriented'). Pro/Enterprise tiers only.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses behavioral traits such as buffering comments per article, batch processing, billing only on processing, and response statuses. Since annotations are absent, the description carries full burden. It covers mutation aspects and pagination/caps but could mention rate limits or idempotency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat lengthy but front-loaded with the core action. It could be more concise by grouping status explanation and tier details. However, each sentence adds necessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 parameters, output schema present, buffering, billing, tier distinctions), the description is complete: it covers input, behavior, response structure, and usage caveats. No significant gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the article_url groups comments, comments array structure with required and optional fields, and recommendations for article_text. It also explains tone and title usage. This justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool submits comments for moderation and analysis, specifies the two possible response statuses, and details what processed responses include. It distinguishes from siblings by focusing on comment submission vs. retrieving results or checking balance.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use this tool (submitting comments for analysis), provides batch size caps per tier, recommends including article_text on first call, and implies alternative tools (get_article_results) for fetching results. It gives clear context on buffering and billing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_article_resultsA

Fetch already-processed, scored comments for an article URL.

Does NOT trigger new processing and does NOT generate Influence comments (Influence only appears in analyze_comments responses).

Tier behavior:

Free/Starter: returns the buffered state only (processed comments are delivered inline via analyze_comments).
Professional/Enterprise: returns the full scored history accumulated across batches.

Use this when the user asks for historical sentiment of an article already submitted previously, or to check how a discussion has evolved since the last analyze_comments call.

ParametersJSON Schema

Name	Required	Description	Default
`article_url`	Yes	Full URL of the article to retrieve processed results for.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so description must carry full burden. It discloses read-only nature (does NOT trigger new processing), tier-dependent result scope, and that Influence scores are absent. However, it does not mention response format, pagination, or data freshness beyond 'buffered state'.

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?

Well structured: one-line summary, then negation, then tier behavior, then usage guidance. Every sentence adds unique value. No redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given single parameter with 100% schema coverage, output schema exists (so return values need not be explained), and tool is a simple retrieval, the description is complete. Could mention rate limits or idempotency for extra safety, but not necessary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (single param fully described). Description adds value by clarifying the parameter's role: 'Full URL of the article to retrieve processed results for' is redundant with schema. However, no additional semantic guidance needed as schema already clear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Verb 'fetch' plus specific resource 'already-processed, scored comments for an article URL'. Contrasts clearly with sibling analyze_comments by explaining what it does NOT do (no new processing, no Influence scores). Distinguishes from get_balance and get_health by focus on comments.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'when the user asks for historical sentiment of an article already submitted previously, or to check how a discussion has evolved since the last analyze_comments call.' Also explains when NOT to use: when Influence scores are needed (use analyze_comments). Tier behavior clarifies expectations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_balanceA

Return the current SentiSift balance, tier, usage counters, and subscription state.

Fields of interest:

comment_balance: remaining comments across all active grants (FIFO consumed)
tier: 'free', 'starter', 'professional', or 'enterprise'
features: which capabilities the current key unlocks (moderate, intelligence, influence)
comment_grants: active allocations with expiry dates
subscription: billing state ('active', 'cancelled', 'past_due') or null for free-tier keys
influence_stats: Influence generation history (if feature enabled)

Call this when the user asks about their balance, upcoming renewals, or whether a feature is available on their tier.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.7/5.0

Behavior4/5

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 details the returned fields (balance, tier, features, etc.) and their semantics (e.g., 'FIFO consumed', 'or null for free-tier keys'), which is sufficient for a read-only query. However, it does not mention side effects or edge cases like rate limits or caching, which would be desirable.

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 well-structured with a lead sentence and bulleted details for fields. It is informative but slightly longer than necessary; the field list could be more concise. Overall, it's clear and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that the tool has zero parameters and an output schema likely defines the structure, the description provides sufficient context for an agent to understand what is returned and when to use it. No gaps are apparent for this use case.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no parameters, and the description provides rich semantics about what each field in the output means. Since schema coverage is 100% (0 params), the description adds value by explaining the return values beyond schema constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a clear verb 'Return' and specifies the resource 'SentiSift balance, tier, usage counters, and subscription state'. It also lists sibling tools like 'analyze_comments' and 'get_health', and this tool's purpose is distinct from them, as it focuses on account details.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to call this tool: 'when the user asks about their balance, upcoming renewals, or whether a feature is available on their tier'. This provides clear context and implies when not to use it (e.g., for other queries), though no alternative tools are named for those cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_healthA

Check if the SentiSift service is ready to handle requests.

Returns {'status': 'ready'} when models are loaded and requests will be served normally, or {'status': 'loading', 'progress': 0.4} during a cold start (usually 10-60 seconds after a restart).

Call this before a large batch analysis if response latency matters, or when diagnosing 'service appears slow' complaints. The analyze_comments tool handles transient loading states automatically with retries, so this is typically informational.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.8/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes return values for both ready and loading states, including progress field. No annotations provided, so description carries full burden. Could mention potential rate limits or side effects, but as a read-only health check, this is sufficient.

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?

Concise, front-loaded with purpose, followed by a line explaining return format, and a final line with usage guidelines. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters, no annotations, and an output schema existing, the description fully covers the tool's behavior and use cases. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters in schema; description adds no param info needed. Score baseline 4 for zero params, plus no extra detail required, so 5 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool checks if the SentiSift service is ready. Differentiates from siblings like analyze_comments, which handles transient states automatically.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly describes when to use: before large batch analysis if latency matters, or for diagnosing slowness. Notes analyze_comments handles loading automatically, so this is informational.

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 updatesApr 19, 2026
- First observedanalyze_comments
- First observedget_article_results
- First observedget_balance
- First observedget_health

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.7/5.0

Scored 2026-09-09 18:53 across 4 tools

Disambiguation5/5

Each tool addresses a distinct concern: submission/analysis, balance/account state, service health, and historical results. There is no overlap between analyze_comments and get_article_results because the former is for new submissions while the latter retrieves past processed data. Even get_balance and get_health are clearly separated by purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (analyze_comments, get_balance, get_health, get_article_results). The verbs ('analyze', 'get') and nouns ('comments', 'balance', 'health', 'article_results') are uniformly descriptive and predictable. This makes it easy for an agent to infer functionality from the name alone.

Tool Count5/5

With only 4 tools, the server is tightly scoped to its core functionality: analyzing comments, checking balance, checking health, and retrieving results. This is not too thin for a specialized moderation service; each tool covers a necessary operation without redundancy. The small count reduces ambiguity and helps agents avoid over-selection.

Completeness4/5

The key operations for the stated purpose (sentiment analysis and moderation) are covered: submit and get processed results, fetch historical results, check account status, and verify service readiness. The only minor gap is the lack of a tool to manage or modify existing submissions (e.g., reprocess or delete), but that is not a typical requirement for this domain. Overall, agents can complete primary workflows without dead ends.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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Servidor MCP de SentiSift

Qué permite esto

Herramientas expuestas

Instalación

Recomendado: uvx (sin instalación, siempre la última versión)

Alternativa: pip install

Configuración del host

Claude Desktop

Cursor

VS Code (con la extensión MCP)

Continue.dev

Cualquier otro host compatible con MCP

Cómo hablar con el asistente

Privacidad

Fuente

Enlaces

Licencia

Available Tools

Output Schema

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Recomendado: `uvx` (sin instalación, siempre la última versión)

Alternativa: `pip install`