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Pangolinfo Review Mining MCP

Pangolinfo Review Mining MCP

Official
by Pangolin-spg
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

review_sentiment_summary

Summarizes Amazon review sentiment and rating trends for a single ASIN, showing rating distribution, positive/negative shares, and monthly trends to support product decisions.

Instructions

Rating distribution, average star, negative/critical/positive shares, and a monthly sentiment trend for one ASIN's reviews. Pure local computation over fetched reviews.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "asin": "<asin>"
    },
    "name": "review_sentiment_summary"
  }
}

Input Schema

NameRequiredDescriptionDefault
asinYes10-character Amazon ASIN (case-insensitive).
siteNoAmazon marketplace. Defaults to amz_us.amz_us
pageCountNoReview pages to fetch (~10 reviews per page). Each page bills credits on the Pangolinfo account — see billing notes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose one meaningful trait: 'pure local computation over fetched reviews,' telling the agent there is no external analysis call and that a fetch happens underneath. It does not cover cost/credit implications (left to the schema), caching, latency, or behavior on sparse review sets.

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?

Two sentences, zero waste. The metric list is front-loaded and the second sentence adds a distinct behavioral fact rather than restating the first.

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?

With no output schema, the description must characterize the return value, and it does so by enumerating the four metric families including the monthly trend. Minor gaps remain around trend granularity and empty/small-sample behavior, but nothing critical to calling it correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100%, including the ASIN pattern, the site enum default, and the pageCount billing note, so the schema already does the parameter work. The description adds no additional meaning about how pageCount affects the sentiment sample size, so baseline 3 applies.

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

Purpose4/5

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

The description names a specific resource (one ASIN's reviews) and enumerates the exact outputs: rating distribution, average star, negative/critical/positive shares, and a monthly sentiment trend. This makes the tool's purpose unambiguous, though it never explicitly contrasts itself with siblings like review_mining_report or top_complaint_topics.

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

Usage Guidelines2/5

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

There is no statement of when to use this tool versus get_product_reviews or mine_complaints, no prerequisites, and no exclusions. An agent must infer from the output list that this is the aggregate-sentiment choice, which is inference rather than guidance.

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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