voc-amazon-reviews

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but `analyze_reviews` and `voc_full` can be confused since both produce a VOC report, differing only in fetching. `analyze_csv` overlaps conceptually with `analyze_reviews` but is differentiated by input type. Descriptions help clarify but some ambiguity remains.
Naming Consistency3/5
Names mostly follow verb_noun pattern (analyze_csv, fetch_reviews, render_dashboard) but `extract_listing_improvements` is longer and `voc_full` breaks the pattern entirely. Inconsistent but still readable.
Tool Count5/5
6 tools is well-scoped for the domain of Amazon review analysis. Each tool covers a distinct step (fetch, analyze, improve, render, external CSV analysis) without being too many or too few.
Completeness4/5
Core workflow (fetch, analyze, improve, visualize) is complete. Minor gaps: no tool for comparing multiple ASINs or batch processing, but this is acceptable for a focused toolkit.
Average 4.2/5 across 6 of 6 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the AI model (claude-opus-4-7), cost range, required API key, and output structure (title, bullets, etc.), which is good transparency. However, it does not mention latency or potential failure modes.
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?
The description is relatively concise, with two paragraphs that front-load the core value. Minor redundancy exists (e.g., 'grounded in actual customer language' and 'citing the pain point').
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?
Given the tool's complexity (AI model, cost, API key), the description covers what it does, input (implicitly ASIN/market/limit), output format, and prerequisites. It lacks detail on how to obtain the VOC report and does not provide an output schema, but the output structure is described in text.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has three parameters (asin, market, limit) with no descriptions (0% coverage). The description does not explain these parameters at all, so the agent gains no additional meaning beyond the schema.
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 clearly identifies the tool as a 'differentiator' that produces specific listing improvements from a VOC report, distinguishing it from raw data tools like 'analyze_reviews'. However, it does not explicitly contrast with each sibling tool, leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides context by contrasting with 'Data Dive style' and mentions API key and cost, implying usage when actionable suggestions are needed. But it lacks explicit 'when not to use' guidance or explicit alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explains the composite nature and returns shape, but does not disclose behavioral traits like read-only status, rate limits, error handling, or permissions. The limit constraint (max 1000) is mentioned, adding some transparency.
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?
The description is highly concise and well-structured: a single-sentence purpose, an explanatory note, and a clear args section. Every sentence adds value without redundancy, and the formatting is easy to scan.
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?
The description references the output shape of analyze_reviews, which assumes knowledge of that sibling tool. Without an output schema or further explanation of the AI analysis results, the description is somewhat incomplete. Additionally, no behavioral details (e.g., error handling, idempotency) are provided, leaving gaps for a composite tool.
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 description coverage is 0%, so the description compensates effectively. It specifies that asin is a 10-character ASIN, market is a market code or amazon.* domain, and limit is the number of reviews with default 100 and max 1000. This adds valuable meaning beyond the schema's titles and defaults.
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 clearly states it is a one-shot tool that fetches reviews and runs AI analysis, positioning it as the default for VOC report requests. It identifies the specific action (fetch + analyze) and resource (ASIN), differentiating from siblings like fetch_reviews and analyze_reviews by being a combined operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear usage context ('VOC report on this ASIN') and explains the internal composition, but does not explicitly state when not to use it or mention alternative sibling tools for separate fetch or analysis tasks. The guidance is adequate but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions that using this tool avoids re-paying the Shulex API, which is a cost-related behavior. However, it does not mention whether the tool is read-only, requires authentication, or any other side effects. Given it is an analysis tool, the description is moderately transparent.
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?
The description is concise and well-structured, with separate paragraphs for purpose, usage, arguments, and returns. It is front-loaded with the action. Could be slightly shorter, but no unnecessary filler.
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?
Given the complexity (custom input format) and lack of output schema, the description covers all return fields and provides usage context. It explains how reviews_json can be two different formats, which is crucial. It also mentions the relationship to fetch_reviews. Missing details on error handling or limitations, but overall adequate.
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?
The schema coverage is 0%, so the description must compensate. It does so by specifying that reviews_json accepts either a specific envelope format or a bare list, and explains asin as a 10-character identifier for the report header. This adds significant meaning beyond the schema.
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 explicitly states the tool runs AI analysis on reviews to produce a VOC report with specific components (sentiment, pain points, etc.). It distinguishes from sibling tools like fetch_reviews (which fetches reviews) by focusing on analysis of already-obtained reviews.
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?
The description clearly indicates when this tool is useful: after fetching reviews via fetch_reviews or a custom scraper, to get analysis without extra API cost. It does not explicitly state when not to use, but the context is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Describes output format and optional features (improvements card). However, lacks details on error handling, side effects (file writing), and default behavior when output_path is omitted. Acceptable but not comprehensive.
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?
Description is concise (~150 words) with a clear front-loaded purpose statement and a structured Args section. Every sentence contributes valuable information without redundancy.
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?
Given no annotations, no output schema, and 1 required param, the description covers the tool's function, inputs, and outputs adequately. It mentions return type {html, bytes, output_path}. Minor gap: does not clarify behavior when output_path is absent (presumably returns HTML string only).
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 description coverage is 0%, but the description's Args section explains each parameter's purpose and source (e.g., report from `analyze_reviews`, improvements from `extract_listing_improvements`). Adds significant meaning beyond the bare schema types.
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?
Description clearly states it renders a VOC report as a standalone HTML dashboard. Specifies output format (single-file HTML) and lists included sections (sentiment bar, panels, executive summary, optimization card). Distinguishes from sibling analysis tools by being a rendering step.
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?
Implicitly indicates usage after obtaining a report from sibling tools like `analyze_reviews`. Does not explicitly state when not to use, but the context is clear. Could be improved by stating prerequisites (e.g., 'Use after analysis tools').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, but description fully discloses behavior: returns raw review array plus metadata, and implies read-only fetch. Could mention rate limits or auth, but overall transparent.
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?
Concise yet complete, with clear sections for overview, usage context, args, and returns. Every sentence adds value.
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?
Covers all necessary aspects given no annotations or output schema: purpose, parameters, return format, and relationship with sibling tools. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but the description provides detailed semantics for all 3 parameters, including format, examples, defaults, and valid values.
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?
Clearly states 'Fetch raw Amazon reviews for an ASIN' with specific verb and resource, and distinguishes from siblings by emphasizing no analysis and returning raw data.
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?
Explicitly describes when to use (for raw data or prior to analyze_reviews) and when not to (if analysis is needed elsewhere), with alternatives named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description thoroughly discloses behavioral traits: supported file formats (csv, xls, xlsx), fuzzy column matching with examples, and return structure including meta.columns_detected. No contradictions.
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?
The description is well-structured with clear sections and front-loaded purpose. It could be slightly more concise (e.g., reduce redundancy in examples), but every sentence adds value.
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 moderate complexity, the description covers all essential aspects: supported sources, fuzzy matching, parameter details, and return shape reference. No output schema needed as return behavior is fully described in terms of a sibling tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description compensates fully by explaining each parameter in an 'Args' section: csv_path as local path or URL, product_name as optional header, market as optional tag with default. Adds meaning beyond schema titles.
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 explicitly states it analyzes any review CSV/Excel, not just Amazon, using a specific verb and resource. It distinguishes from siblings by mentioning bypassing the Shulex VOC API and supporting non-Amazon sources.
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 provides a clear 'Use this when:' section with three explicit conditions: product not on Amazon, have a reviews file from another source, want to bypass the API. This offers direct guidance on when to use and implies alternatives.
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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