How do I use junglescout-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@junglescout-mcp Search volume and PPC bid estimates for 'bamboo cutting board'" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

junglescout-mcp

Q: Which integrations are available for this server?

Provides tools for Amazon product research including keyword search volume, ASIN keyword analysis, product database queries, sales estimates, and share of voice using the Jungle Scout API.

by purahmanian

Overview Schema Source Related Servers Score Discussions Admin

TypeScript

Remote

junglescout-mcp

MCP server for the Jungle Scout Cobalt/Developer API: keyword search volume, ASIN keyword analysis, product database queries, sales estimates, and share of voice.

npm

Unofficial: This project is not affiliated with, endorsed by, or sponsored by Jungle Scout. "Jungle Scout" is a trademark of Jungle Scout Group LLC. All rights belong to their respective owners.

What it does

Tool	Description	Example prompt
`keyword_search_volume`	Exact-match and broad-match volume for up to 100 keywords, plus 30-day trend, quarterly trend, and PPC bid estimates	"What is the search volume for 'yoga mat' and 'foam roller' on Amazon?"
`keywords_by_asin`	Which keywords drive traffic to a specific Amazon listing	"What keywords is ASIN B07XJ8C8F5 ranking for?"
`product_database_query`	Find product opportunities filtered by category, title keywords, price, minimum revenue, and max reviews	"Find Sports and Outdoors products under $40 with at least $5k monthly revenue and fewer than 300 reviews"
`sales_estimates`	Estimated monthly units sold and revenue for an ASIN	"How many units per month does B07XJ8C8F5 sell?"
`share_of_voice`	Brand share of organic and sponsored results for a keyword	"Which brands dominate the 'protein powder' keyword on Amazon?"

Related MCP server: Influencers Club MCP Server

Quick start

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "junglescout": {
      "command": "npx",
      "args": ["-y", "junglescout-mcp"],
      "env": {
        "JUNGLESCOUT_API_KEY": "your-api-key-here",
        "JUNGLESCOUT_KEY_NAME": "your-key-name-here"
      }
    }
  }
}

Restart Claude Desktop after saving.

Claude Code

claude mcp add junglescout \
  -e JUNGLESCOUT_API_KEY=your-api-key-here \
  -e JUNGLESCOUT_KEY_NAME=your-key-name-here \
  -- npx -y junglescout-mcp

OpenAI Codex CLI

Add to ~/.codex/config.toml:

[mcp_servers.junglescout]
command = "npx"
args = ["-y", "junglescout-mcp"]

[mcp_servers.junglescout.env]
JUNGLESCOUT_API_KEY = "your-api-key-here"
JUNGLESCOUT_KEY_NAME = "your-key-name-here"

Getting an API key

The Jungle Scout Developer API requires an active Jungle Scout subscription that includes API access.

Once you have a plan with API access:

Log in to Jungle Scout and navigate to Settings > Manage API Keys.
Create a new API key. You will receive both a Key Name and an API Key.
Set the environment variables:

export JUNGLESCOUT_API_KEY="your-api-key-here"
export JUNGLESCOUT_KEY_NAME="your-key-name-here"

The server will start without credentials and tools will return setup instructions until both variables are configured.

Example conversations

Keyword research for a new product:

"Give me the Amazon US search volume and PPC bid estimates for 'bamboo cutting board', 'wooden cutting board', and 'plastic cutting board'. Which has the best volume-to-competition ratio?"

The model calls keyword_search_volume with all three terms, compares volumes and ease-of-ranking scores, and summarizes which keyword is the best entry point.

Competitor keyword gap analysis:

"I am competing with ASIN B07XJ8C8F5. What are the top 20 keywords driving traffic to that listing?"

The model calls keywords_by_asin and returns the ranking keywords sorted by relevancy, including organic and sponsored positions.

Product opportunity discovery:

"Find me product opportunities in the Kitchen category priced between $20 and $60 with at least $8,000 monthly revenue and fewer than 150 reviews."

The model calls product_database_query with the filters and returns a ranked list of products with revenue, BSR, and listing quality scores.

Development

# Install dependencies
npm install

# Run tests (no live API calls)
npm test

# Build
npm run build

# Run locally (credentials required for live data)
JUNGLESCOUT_API_KEY=xxx JUNGLESCOUT_KEY_NAME=yyy node dist/index.js

Authentication

The Jungle Scout API uses a custom authorization format:

Authorization: <KEY_NAME>:<API_KEY>
X-API-Type: junglescout
Content-Type: application/vnd.api+json
Accept: application/vnd.junglescout.v1+json

The Accept header selects the API version and must be the application/vnd.junglescout.v1+json media type. Sending a different Accept value causes the API to return HTTP 404.

All marketplace parameters default to us. Other supported values include ca, uk, de, fr, it, es, jp, au, in, and more.

Built by

Built by Puya Ventures LLC. I build custom MCP servers and AI integrations for product, e-commerce, and data teams. Get in touch: purahmanian@gmail.com | Portfolio: puyarahmanian.com

Part of the Product-Research MCP Suite: keepa-mcp · google-trends-mcp · junglescout-mcp

Privacy

This server runs entirely on your machine. It collects no telemetry and stores no data. The only network calls it makes are to the Jungle Scout API (developer.junglescout.com), sending your API credentials and the keywords or ASINs you ask about. Credentials are read from the JUNGLESCOUT_API_KEY and JUNGLESCOUT_KEY_NAME environment variables and never written to disk or sent anywhere except Jungle Scout. See Jungle Scout's privacy policy: https://www.junglescout.com/privacy/

License

MIT. See LICENSE.

Available Tools

5 tools

keywords_by_asinA

Read-only

Discover which keywords drive traffic to a specific Amazon listing (by ASIN).

ParametersJSON Schema

Name	Required	Description	Default
`asin`	Yes	Amazon ASIN. Example: B07XJ8C8F5
`page_size`	No	Number of keywords to return (1-100). Defaults to 25.
`marketplace`	No	Amazon marketplace code. Defaults to 'us'.	us

TDQS

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is clear. The description adds that keywords 'drive traffic' which is behavioral context, but does not explain other traits like pagination, rate limits, or response format. Bar is lowered by annotations, so a 3 is appropriate.

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 is concise and immediately conveys the tool's purpose. Every word earns its place, but it could be slightly more informative without being verbose.

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

Completeness2/5

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

Given the absence of an output schema, the description should explain what the tool returns (e.g., list of keywords with metrics, format, array objects). It only says 'discover which keywords drive traffic' without specifying the output structure, leaving the agent with incomplete context about the response.

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 the schema already documents all three parameters with descriptions for asin, page_size, and marketplace. The description adds no additional meaning beyond the schema, meeting the baseline for high coverage.

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 discovers keywords that drive traffic to a specific Amazon listing via ASIN. It uses a specific verb ('discover') and resource ('keywords') and implies non-overlap with siblings like 'keyword_search_volume' which focuses on volume rather than traffic drivers.

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 implies use for analyzing which keywords bring traffic to an ASIN but provides no explicit guidance on when to use versus alternatives like 'product_database_query' or 'sales_estimates'. The context signal of sibling tools is present but the description itself lacks differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

keyword_search_volumeA

Read-only

Look up exact-match and broad-match search volume for one or more Amazon keywords.

ParametersJSON Schema

Name	Required	Description	Default
`keywords`	Yes	Keywords to look up exact and broad monthly search volume for. Each keyword is queried individually (up to 10 per call are processed). Example: ["yoga mat", "resistance bands"]
`marketplace`	No	Amazon marketplace code. Defaults to 'us'.	us

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating a safe read operation. The description adds value by specifying exact-match and broad-match volume, and the schema description notes that up to 10 keywords per call are processed. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is both concise and front-loaded with the purpose. Every word contributes meaning with no redundancy. It is appropriately sized for the tool's simplicity.

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 simple lookup tool with complete annotations and schema coverage, the description adequately covers what the tool does, what it returns (exact and broad volume), and the input constraints (keywords, marketplace). No output schema is needed as the return values are implied.

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 burden on the description is low. The description only says 'one or more Amazon keywords,' while the schema already explains each parameter in detail (e.g., keyword array with examples, marketplace enum). The description adds minimal extra 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 the tool's purpose: looking up exact-match and broad-match search volume for Amazon keywords. The verb 'look up' and the resource 'search volume' are specific, and the tool is distinct from siblings like keywords_by_asin and sales_estimates.

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 no explicit guidance on when to use this tool versus alternatives. Siblings are listed but not compared. The description mentions match types but does not address when not to use or prerequisites. Basic context is implied but lacks exclusionary criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

product_database_queryA

Read-only

Search the Jungle Scout product database for Amazon product opportunities.

ParametersJSON Schema

Name	Required	Description	Default
`category`	No	Amazon category name to filter by. Example: 'Sports & Outdoors'
`max_price`	No	Maximum price in USD. Example: 50
`min_price`	No	Minimum price in USD. Example: 15
`page_size`	No	Number of results to return (1-100). Defaults to 25.
`marketplace`	No	Amazon marketplace code. Defaults to 'us'.	us
`max_reviews`	No	Maximum number of reviews (to find low-competition products). Example: 200
`include_keywords`	No	Only include products whose title matches these keywords (or ASINs). Example: ['tonneau cover']
`min_monthly_revenue`	No	Minimum estimated monthly revenue in USD. Example: 5000

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already include readOnlyHint=true and openWorldHint=true. The description adds no behavioral context beyond confirming a read operation, but it does not contradict annotations.

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?

A single sentence front-loads the core purpose with zero wasted words.

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

Completeness2/5

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

With 8 optional parameters and no output schema, the description fails to explain the return format, result structure, or how 'product opportunities' are defined, leaving key usage information missing.

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 8 parameters have descriptions in the input schema (100% coverage), so the tool description does not need to add parameter details. It adds no additional meaning beyond the schema, earning a baseline 3.

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 verb 'Search' and the specific resource 'Jungle Scout product database' for finding 'Amazon product opportunities', which effectively distinguishes it from sibling tools focused on keywords or sales estimates.

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 implies usage for product opportunity discovery but provides no explicit guidance on when to use or avoid this tool compared to siblings like keyword_search_volume or sales_estimates.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

sales_estimatesA

Read-only

Get Jungle Scout estimated monthly sales units and revenue for a specific ASIN.

ParametersJSON Schema

Name	Required	Description	Default
`asin`	Yes	Amazon ASIN. Example: B07XJ8C8F5
`end_date`	No	End date YYYY-MM-DD (must be before today). Defaults to yesterday.
`start_date`	No	Start date YYYY-MM-DD. Defaults to 30 days ago.
`marketplace`	No	Amazon marketplace code. Defaults to 'us'.	us

TDQS

A3.8/5.0

Behavior3/5

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

Annotations declare readOnlyHint true and openWorldHint true, which are consistent. The description adds minimal behavioral context beyond the purpose, but does not contradict annotations.

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?

Single sentence of 13 words that is front-loaded with the action and object. No wasted words.

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?

With no output schema, the description adequately states what is returned (estimated monthly sales units and revenue). Parameters are well-described in the schema, so overall the tool is sufficiently documented for its simplicity.

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 description adds little value over the schema. It mentions 'a specific ASIN' which is already in the schema, but does not further clarify parameter behavior.

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 specifies the exact verb 'Get' and the resource 'Jungle Scout estimated monthly sales units and revenue for a specific ASIN', clearly distinguishing it from sibling tools like keyword_search_volume or product_database_query.

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 implies use when sales estimates for an ASIN are needed, but provides no explicit when-to-use or when-not-to-use guidance, nor any mention of alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

share_of_voiceB

Read-only

Analyze brand share of voice for a given Amazon search keyword.

ParametersJSON Schema

Name	Required	Description	Default
`keyword`	Yes	Search keyword to analyze brand share for. Example: 'protein powder'
`marketplace`	No	Amazon marketplace code. Defaults to 'us'.	us

TDQS

B3.2/5.0

Behavior2/5

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

Annotations include readOnlyHint=true and openWorldHint=true, but the description adds minimal behavioral context beyond the purpose. It does not disclose output format, accuracy limitations, or data scope (e.g., how 'share of voice' is computed), which would help the agent anticipate results.

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 a single, front-loaded sentence with no superfluous words. It efficiently communicates the core purpose without redundancy.

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

Completeness2/5

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

Despite low complexity (2 parameters, no output schema), the description lacks essential context. It does not explain what 'share of voice' means, how it is calculated, or what the output structure is, leaving important gaps for agent understanding.

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?

The input schema has 100% description coverage, with clear parameter descriptions (e.g., example for 'keyword', default/enum for 'marketplace'). The tool description does not add additional meaning beyond what the schema provides, meeting the baseline without enhancement.

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's purpose: 'Analyze brand share of voice for a given Amazon search keyword.' It uses a specific verb ('Analyze') and distinct resource ('brand share of voice'), differentiating it from sibling tools like 'keyword_search_volume' and 'sales_estimates'.

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 description provides no guidance on when to use this tool versus alternatives. It does not suggest when to avoid using it or mention any prerequisites, leaving the agent to infer usage from the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

5 tool updatesv0.1.1Jul 30, 2026
- First observedkeyword_search_volume
- First observedkeywords_by_asin
- First observedproduct_database_query
- First observedsales_estimates
- First observedshare_of_voice

Install Server

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TDQS

A3.8/5.0

Scored 2026-07-30 13:30 across 5 tools

Disambiguation5/5

Each tool targets a distinct aspect of Amazon product research: keyword volume, keyword-to-ASIN mapping, product database search, sales estimates, and share of voice. No overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern and clearly indicate their function (e.g., 'keyword_search_volume', 'sales_estimates'). No mixing of conventions.

Tool Count5/5

With 5 tools, the server is well-scoped for Amazon product research. Each tool serves a necessary purpose without redundancy.

Completeness4/5

The tool set covers the core workflow: keyword research, product discovery, sales data, and brand analysis. Minor gaps like historical trends or competitor tracking exist but are not essential.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

Need Help?

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What it does

Quick start

Claude Desktop

Claude Code

OpenAI Codex CLI

Getting an API key

Example conversations

Development

Authentication

Built by

Privacy

License

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Influencers Club MCP Serverofficial

Latest Blog Posts

MCP directory API