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Pangolinfo Review Mining MCP

Pangolinfo Review Mining MCP

Official
by Pangolin-spg
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

mine_complaints

Extracts complaint signals from 1–3 star Amazon reviews for one ASIN, tags each with human-readable themes, and optionally filters by helpful votes to surface actionable product issues.

Instructions

Extract complaint signals from negative (critical, 1-3 star) reviews for one ASIN. Tags each complaint review with human-readable themes (quality_defect, battery_power, connectivity, comfort_fit, sound_quality, shipping_delivery, returns_refunds, value_price, authenticity, app_software) using a transparent English keyword heuristic — not a classifier. Optionally filter to complaints with at least minHelpful helpful votes.

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": "mine_complaints"
  }
}

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.
minHelpfulNoOnly keep complaints with at least this many helpful votes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does add real value by disclosing the method — a 'transparent English keyword heuristic — not a classifier' — which signals precision limits an agent should know about. But it is silent on whether it fetches reviews itself (the pageCount/credit implication lives only in the schema), on rate limits, on what happens on zero matches, and on the shape of the response.

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?

Front-loaded with the core action and scope, then the tagging mechanism, then the optional filter; no filler sentences. The ten-item theme list is long but carries informational content an agent may need to interpret results, so it mostly earns its space.

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

Completeness3/5

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

There is no output schema, so the description must carry the return picture; it says each complaint review is tagged with themes, which is the essential shape but skips whether scores, keyword matches, or the review text/URL are included. Adequate to call correctly, thin on what comes back.

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%, so the schema already documents asin, site, pageCount and minHelpful. The description reinforces the 'negative = critical, 1-3 star' scoping and the minHelpful semantics but adds no format or edge-case detail beyond the schema. Baseline 3 fits.

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?

Specific verb and resource: 'extract complaint signals from negative (1-3 star) reviews for one ASIN', plus an explicit enumeration of the ten theme tags. It never names a sibling tool (review_mining_report, top_complaint_topics, review_sentiment_summary), so the boundary is inferable rather than stated.

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?

Usage context is implied (you want complaint themes rather than raw reviews or a sentiment score) but never spelled out. There is no explicit when-to-use/when-not and no routing to the obvious alternatives, so the agent must infer the choice from sibling names alone. Only the optional minHelpful filter is described, which is parameter guidance, not tool selection 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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