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Pangolinfo Review Mining MCP

Pangolinfo Review Mining MCP

Official
by Pangolin-spg
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

top_complaint_topics

Rank complaint themes from negative Amazon reviews for a single ASIN, showing counts, shares, and example review titles to trace top customer issues.

Instructions

Ranked complaint themes across negative reviews for one ASIN, with counts, shares, and example review titles for traceability.

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": "top_complaint_topics"
  }
}

Input Schema

NameRequiredDescriptionDefault
asinYes10-character Amazon ASIN (case-insensitive).
siteNoAmazon marketplace. Defaults to amz_us.amz_us
topNNoHow many top themes to return.
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

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries full behavioral burden. It helpfully discloses the return shape (counts, shares, example titles) and implicitly frames the tool as an analysis read, but says nothing about credit consumption, latency, or whether reviews are fetched live — the billing implication only exists in the pageCount schema field, not here.

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?

A single front-loaded sentence that packs scope, output contents and purpose with no filler. It is appropriately sized, though it could spend one more clause on cost or alternatives rather than pure brevity.

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 explain returns — it does name the fields but not their structure or whether totals are included. For a 4-parameter tool that bills credits per page, the omission of cost and data-freshness context leaves a real gap.

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 coverage is 100%, so asin, site, topN and pageCount are already documented in the schema, including the credit note. The description adds only 'one ASIN' and the negative-review scope, which is minimal value on top of the schema baseline.

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?

Names the resource precisely: ranked complaint themes from negative reviews for a single ASIN, plus the payload (counts, shares, example titles). It is specific enough to distinguish itself from siblings like review_sentiment_summary, though it never names the closest alternative (mine_complaints) to draw the line.

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?

The phrase 'across negative reviews' hints at the use case, but there is no explicit when-to-use, no when-not-to-use, and no mention of siblings such as mine_complaints or review_mining_report. The agent must infer routing from tool names alone.

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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