keepa-mcp

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool targets a clearly distinct data type: current product details, price time series, sales rank history, best-seller lists, keyword search, and deals. Even the two history tools are cleanly separated by price vs. rank.
Naming Consistency5/5
All tools follow a consistent get_* or search_* verb + noun pattern in snake_case (e.g., get_product, get_price_history, search_products). No mixed conventions or vague verbs.
Tool Count5/5
Six tools is well within the ideal 3-15 range and covers the core Keepa workflows without redundancy. The count feels deliberate, and each tool earns its place.
Completeness4/5
The surface covers the main read-only product intelligence needs: current data, historical prices and ranks, search, best sellers, and deals. Minor gaps like category tree lookup or offer-level detail exist, but workflows are not blocked.
Average 4.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 11 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?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds useful behavioral context: it returns an ordered list of ASINs, explains where to find category IDs, and gives common examples. No contradiction with annotations.
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 front-loaded with a clear purpose, then provides a link and examples. The Args and Examples sections add practical value, and the overall length is justified. Slightly longer than minimal, but each part earns its place.
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 simplicity (two parameters, no output schema), the description is complete: it explains what the tool returns (ordered list of ASINs), how to find category IDs, and provides common examples. No major missing context for an agent to select and invoke the tool.
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 both parameters thoroughly. The description marginally adds value with example category mappings, but these are also present in the schema's category_id description. The description does not add 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 clearly states 'Get the top-selling ASINs in a Keepa category' with a specific verb and resource. It also differentiates from siblings like search_products and get_deals by focusing on top-selling ASINs.
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 provides clear context for when to use the tool (when you need top-selling ASINs in a category), examples of use cases, and a link to find category IDs. It does not explicitly exclude alternatives like search_products or get_deals, but the context implies the tool is for best sellers.
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?
Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the agent knows it's a safe read. The description adds that the tool returns a decoded, downsampled time series plus a trend summary, and explains how to interpret rank changes (improving/worsening/stable). This goes beyond the safety profile to set expectations about 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?
The description is structured with a purpose statement, a brief explanation of BSR relevance, output summary, argument list, and examples. While longer than necessary, each section adds context and the format is easy to scan.
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?
The description covers the tool's purpose, output structure (time series + trend summary), parameter semantics, and provides two use-case examples. Without an output schema, it could be more explicit about the exact time series format (e.g., array of points), but given the simplicity and the annotations, it's adequately complete.
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?
All three parameters are documented in the schema (100% coverage), so the description's Args section is largely redundant. It does reinforce defaults and adds an example using range='6m', but doesn't provide substantial new meaning beyond the schema descriptions.
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 opens with 'Fetch historical sales rank data for an Amazon ASIN from Keepa', which clearly states a specific verb, resource, and scope. It distinguishes itself from siblings like get_price_history and get_best_sellers by focusing on BSR history, further clarified by examples.
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 includes concrete example queries ('Is demand trending up or down...') that signal when to invoke this tool. It doesn't explicitly name alternatives or exclusions, but the examples imply the tool is for demand/BSR history questions, not for price or current best-seller data.
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?
Annotations already declare readOnly, idempotent, and non-destructive behavior, so the description's additional context about returning products with significant price drops and filtering by rating is useful. It does not describe return structure, sorting, or edge cases, but given strong annotations, the bar is lower.
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 compact, front-loaded with purpose, and organized with an Args list and examples. Every sentence serves a purpose; no fluff or 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 the simple parameter set (all optional, no enums) and the lack of an output schema, the description covers the essential context: purpose, filters, defaults, pagination, and domain selection. It could mention return format or sorting, but is largely complete for practical use.
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 covers all four parameters with descriptions, but the tool description adds practical mapping examples ('Show me deals with at least 30% off' → min_price_drop_pct=30), which helps an agent translate natural language into parameter values. This goes beyond the schema's terse definitions.
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 opens with 'Fetch current Amazon price-drop deals from Keepa', clearly identifying the action and resource. It distinguishes itself from sibling tools like get_best_sellers and search_products by focusing specifically on price-drop deals with rating and drop-percentage filters.
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 gives clear context for when to use the tool (to find products with significant price drops) and provides concrete examples of user queries. However, it does not explicitly compare with alternatives or state when not to use this tool, so it falls just short of full guidance.
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?
Annotations already indicate a read-only, idempotent operation, but the description adds valuable context: it details the return fields, explains the 'N/A' sentinel, notes USD currency, and describes error handling for missing API key and invalid ASINs. This goes beyond the annotation baseline.
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 (Returns, Prices, Args, Examples, Error handling) and is front-loaded with the primary purpose. It is slightly long due to repeating schema info for the 'Args' section, but every section serves a useful role, so it earns a 4.
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?
Despite having no output schema, the description thoroughly enumerates the return fields, clarifies value semantics, and covers error scenarios. For a simple 2-param tool, this is complete enough for an agent to correctly select and invoke the tool.
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% for both asin and domain, so the schema already provides parameter meaning. The description repeats the asin argument and domain default without adding new semantic detail beyond a couple of examples, which is marginal value. Baseline 3 applies.
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 clearly states the tool fetches current product details for a given Amazon ASIN, naming the specific resource (Keepa product data) and scope (current snapshot). It distinguishes itself from sibling tools like get_price_history and get_best_sellers by focusing on current details and listing the exact returned fields.
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?
Examples provide concrete use cases ('What is the current price...') and imply when to use this tool, but it does not explicitly contrast with alternatives like get_price_history for historical data. The description gives clear contextual guidance without formal exclusions, so it earns a 4 rather than 5.
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?
Annotations already indicate read-only and idempotent behavior. The description adds valuable behavioral details: downsampling to at most 60 data points, cents-to-USD conversion, and decoding Keepa time to ISO dates. This goes beyond annotation coverage.
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 well-structured and front-loaded: main purpose, return data, unit/time conventions, args, and examples. Every section earns its place with no redundancy or fluff.
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?
Despite having no output schema, the description explains what data points are returned and the format. Combined with strong annotations (read-only, idempotent, non-destructive), it gives a complete picture for a 3-parameter read 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 covers all parameters (100% coverage), so baseline is 3. The description adds concrete examples for range values and clarifies domain default, which helps the agent map natural language to parameter 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?
The description states the specific action ('Fetch historical price data') on a specific resource ('Amazon ASIN from Keepa') and clearly lists the returned data series. It distinguishes itself from siblings like get_product (product info) and get_sales_rank_history (sales rank), though not explicitly.
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?
Provides clear context through examples and parameter descriptions, showing when to use (e.g., 'Show price history for B08N5WRWNW over the past year'). Does not mention alternative tools or exclusions, so it's not a 5, but it's more than implied usage.
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?
The description adds value beyond annotations by stating that the return is a list of ASINs and that full details require a separate call to get_product. This clarifies the tool's scope. Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description supplements with return format and next-step guidance, justifying a 4.
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 well-structured, beginning with a clear one-sentence purpose, followed by return behavior and a pointer to a sibling tool. It includes an Args section and Examples, but each element contributes meaningfully to usage understanding. No redundant filler, making it both concise and informative.
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?
For a search tool with no output schema, the description adequately explains the return value (list of ASINs) and how to consume it. It also covers all parameters, provides examples, and notes the optional nature of category_id and domain. The description is self-contained and complete for the agent to select and invoke the tool correctly.
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%, but the description goes beyond schema by providing concrete examples that map natural language requests to parameters (e.g., "yoga mat" -> keyword). This helps the agent understand how to infer values from user intents, adding semantic value over the schema's field descriptions.
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 clearly states it searches for Amazon products on Keepa by keyword, returns a list of matching ASINs, and distinguishes itself from get_product by explicitly directing users to use get_product for full details. This is a specific verb+resource combination that differentiates it from sibling tools.
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 provides clear context by telling users to call get_product on any returned ASIN for full details, giving a direct follow-up action. It also gives usage examples, but it does not explicitly state when not to use the tool or compare against alternatives like get_best_sellers. Thus it has clear context but no exclusions.
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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