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SentiSift MCP

Name: SentiSift MCP
Author: pickelfintech

by pickelfintech

Overview Schema Source Related Servers Discussions Admin

Python

Remote

SentiSift MCP-Server

Offizieller Model Context Protocol (MCP)-Server für SentiSift. Ermöglicht MCP-kompatiblen KI-Hosts (Claude Desktop, Cursor, VS Code, Continue und andere), die SentiSift-API für Kommentarmoderation und Intelligenz als natives Tool aufzurufen.

Was dies ermöglicht

Nach der Installation kann Ihr KI-Assistent:

Kommentare direkt im Gespräch analysieren: „Lass diese 50 Kommentare durch SentiSift laufen und zeige mir die Bots und die Stimmungsverteilung.“
Ihren Kontostatus prüfen: „Wie hoch ist mein SentiSift-Guthaben und wann verlängert sich mein Abonnement?“
Historische Ergebnisse abrufen: „Zeige mir den Stimmungsverlauf für den gestrigen Artikel auf example.com/breaking-news.“
Den Servicestatus selbst diagnostizieren: „Ist die SentiSift-API bereit oder laden die Modelle noch?“

Kein Programmieren erforderlich. Der Assistent ruft die Tools über MCP auf und berichtet die Ergebnisse.

Related MCP server: Semanticscholar Universal MCP Server

Verfügbare Tools

Tool	Beschreibung
`analyze_comments`	Übermittelt eine Reihe von Kommentaren sowie eine Artikel-URL und erhält Bot-/Spam-Markierungen, Stimmungs-Labels, Markierungen für kommerzielle Inhalte, eine Moderationszusammenfassung und (bei Pro/Enterprise) Intelligence + eingefügte Influence-Kommentare.
`get_balance`	Aktuelles Kommentar-Guthaben, Tarif, Feature-Flags, aktive Zuschüsse, Abonnementstatus, Influence-Statistiken.
`get_health`	Gibt an, ob die SentiSift-API bereit ist oder noch Modelle lädt.
`get_article_results`	Historische bewertete Kommentare für eine Artikel-URL (Pro/Enterprise behalten den vollständigen Verlauf; Free/Starter geben nur den Pufferstatus zurück).

Installation

Sie benötigen Python 3.10+ und einen SentiSift-API-Schlüssel. Kostenlose Schlüssel (1.000 Kommentare, keine Kreditkarte) finden Sie unter sentisift.com/pricing.

Empfohlen: `uvx` (keine Installation, immer aktuell)

uv führt veröffentlichte Python-Apps ohne dauerhafte Installation aus. Dies ist der installationsfreie Weg.

# 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

Alternative: `pip install`

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

Host-Konfiguration

Jeder KI-Host hat seine eigene Konfigurationsdatei. Wählen Sie Ihre aus:

Claude Desktop

Bearbeiten Sie die Konfigurationsdatei:

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

Fügen Sie den SentiSift-Eintrag zu mcpServers hinzu:

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

Starten Sie Claude Desktop neu. Sie sollten in der unteren linken Ecke eines neuen Chats eine neue Tool-Anzeige sehen. Versuchen Sie: „Use SentiSift to check my balance.“

Cursor

Cursor unterstützt MCP über das Panel Settings → MCP Servers. Fügen Sie hinzu:

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

Oder bearbeiten Sie ~/.cursor/mcp.json direkt mit der gleichen Struktur wie im obigen Claude Desktop-Beispiel.

VS Code (mit der MCP-Erweiterung)

Bearbeiten Sie Ihre Benutzer- oder Arbeitsbereichs-settings.json:

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

Continue.dev

In ~/.continue/config.json:

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

Jeder andere MCP-kompatible Host

Der Server spricht MCP über stdio. Verweisen Sie Ihren Host auf uvx sentisift-mcp (oder sentisift-mcp, falls über pip installiert) und setzen Sie SENTISIFT_API_KEY in dessen Umgebung.

Wie man mit dem Assistenten spricht

Sobald die Konfiguration abgeschlossen ist, funktionieren natürlichsprachliche Prompts. Einige Beispiele:

„Check my SentiSift balance.“

„Run these 20 comments through SentiSift for the article at https://example.com/breaking-story. Here are the comments:“ (fügen Sie diese ein)

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

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

Der Assistent wählt das richtige Tool, übergibt die richtigen Argumente und fasst die Ergebnisse zusammen. Wenn ein Aufruf fehlschlägt, enthält die Fehlerantwort einen Deep-Link zum entsprechenden Dokumentationsabschnitt, damit der Assistent sich selbst korrigieren kann.

Datenschutz

Der Server sendet einen User-Agent-Header, der ihn als sentisift-mcp/<version> identifiziert, damit wir MCP-Datenverkehr in unseren Nutzungsprotokollen von direktem SDK-Datenverkehr unterscheiden können. Keine weitere Telemetrie.
Der Server läuft lokal auf Ihrem Computer. Er ruft nur api.sentisift.com auf.
Ihr API-Schlüssel bleibt auf Ihrem Computer (in der Umgebungskonfiguration des Hosts). Er wird von diesem Server niemals auf die Festplatte geschrieben.

Quelle

Quelle und Issue-Tracker: gitlab.com/pickel-fintech/sentisift-sdks.

Lizenz

MIT — siehe LICENSE. Der SentiSift-API-Dienst, den dieser Client aufruft, wird separat durch sentisift.com/terms.html geregelt; die Nutzung der API erfordert einen API-Schlüssel von 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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SentiSift MCP-Server

Was dies ermöglicht

Verfügbare Tools

Installation

Empfohlen: uvx (keine Installation, immer aktuell)

Alternative: pip install

Host-Konfiguration

Claude Desktop

Cursor

VS Code (mit der MCP-Erweiterung)

Continue.dev

Jeder andere MCP-kompatible Host

Wie man mit dem Assistenten spricht

Datenschutz

Quelle

Links

Lizenz

Available Tools

Output Schema

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Empfohlen: `uvx` (keine Installation, immer aktuell)

Alternative: `pip install`