SentiSift MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| SENTISIFT_API_KEY | Yes | Your SentiSift API key, available from sentisift.com/pricing |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| 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. |
| get_balanceA | Return the current SentiSift balance, tier, usage counters, and subscription state. Fields of interest:
Call this when the user asks about their balance, upcoming renewals, or whether a feature is available on their tier. |
| 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. |
| 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:
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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
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.
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.
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.
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.