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pickelfintech

SentiSift MCP

by pickelfintech
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

analyze_comments

Submit article comments for moderation and sentiment analysis. Obtain toxicity labels, spam flags, composite scores, and crowd-level intelligence including discussion themes and sentiment balance. Supports buffered processing per article URL.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
article_urlYesFull 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.
commentsYesArray 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_textNoFull 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.
titleNoArticle title, for the article profile.
toneNoBrand voice for Influence comments when applicable (e.g. 'professional and measured', 'warm and community-oriented'). Pro/Enterprise tiers only.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

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.

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