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sepehr071

technolife-mcp

by sepehr071
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Product reviews

tl_reviews
Read-onlyIdempotent

Read product reviews to check quality before recommending: average rating, star distribution, AI pros/cons summary, and review text. Sort by lowest_rated to surface complaints.

Instructions

Read customer reviews of a product: average 0-5, star distribution, AI summary with pros/cons, and the reviews.

Use as a quality check before recommending; sort=lowest_rated surfaces complaints. Review text is cut at 400 characters unless full_text=true. The AI summary (null for few reviews) comes on page 0 only. The rating sorts rank the newest 500 reviews.

Input Schema

NameRequiredDescriptionDefault
pageNoZero-based page number.
sortNoOrder of the reviews.newest
limitNoReviews per page.
full_textNoReturn review and reply text uncut (default cuts at 400 characters).
product_codeYesProduct code, e.g. 'TLP-60492' (bare number works).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed8 schema fields changedv0.3.0
    • addedInput schema / properties / full_text
      Added value: +{
+  "default": false,
+  "description": "Return review and reply text uncut (default cuts at 400 characters).",
+  "type": "boolean"
+}
    • removedInput schema / properties / limit / title
      Removed value: -"Limit"
    • removedInput schema / properties / page / title
      Removed value: -"Page"
    • changedInput schema / properties / product_code / description
      Previous value: -"Product code from a search or list, e.g. 'TLP-60492' (the bare number works too)."New value: +"Product code, e.g. 'TLP-60492' (bare number works)."
    • removedInput schema / properties / product_code / title
      Removed value: -"Product Code"
    • removedInput schema / properties / sort / title
      Removed value: -"Sort"
    • removedInput schema / title
      Removed value: -"tl_reviewsArguments"
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "title": "tl_reviewsDictOutput",
-  "type": "object"
-}New value: +null
  2. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover safety (readOnlyHint, idempotentHint, destructiveHint=false), and the description adds genuinely non-obvious behavior: text truncated at 400 chars unless full_text=true, AI summary is null for few reviews and appears only on page 0, and rating sorts cover only the newest 500 reviews. It does not mention pagination limits or response shape, but the caveats it does give are substantive.

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?

Three short sentences with the primary purpose front-loaded and caveats trailing. Every sentence carries new information; no filler or restated field names. Slightly dense but well ordered.

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?

With no output schema, the description carries the return-value burden well: it names the average, star distribution, AI summary, and review list, and explains the truncation, page-0-only summary, and sorting scope. An agent knows what it will get and the main edge cases before calling.

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 the baseline is 3, but the description adds meaning the schema lacks: the effect of full_text (uncut vs 400-char cutoff), the purpose of sort=lowest_rated, and the 500-review scope of the rating sorts. The page parameter's role in gating the AI summary is also clarified.

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 verb and resource ('Read customer reviews of a product') and enumerates the payload: average rating, star distribution, AI pros/cons summary, and the reviews themselves. The product scope implicitly separates it from the sibling tl_shop_reviews, but that sibling is never named, so differentiation requires inference.

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

Usage Guidelines4/5

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

It gives explicit usage context: 'Use as a quality check before recommending; sort=lowest_rated surfaces complaints.' That is a clear when-to-use with a concrete tactic. It stops short of naming alternatives or stating when not to use it (e.g., versus shop-level reviews).

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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