hyperd-mcpofficial
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licenseA
qualityC
maintenancePre-trade DeFi intelligence for AI agents. 20 paid x402 endpoints, USDC on Base.
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MIT
KDP Niche Research
Server Details
Kindle niche intelligence (demand/competition/BSR/revenue) gated by x402 USDC on Base.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
2 toolslist_nichesAInspect
List available KDP niche keywords ordered by demand score.
Use this to discover what Kindle publishing niches are available for
detailed research. Returns keywords ordered by demand score (highest first).
This endpoint is free — no payment required.
Args:
limit: Number of results to return (1-100, default 20)
offset: Pagination offset (default 0)
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
A4.6/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses ordering (highest first), the free nature, and pagination parameters, which gives the agent confidence that this is a safe read operation. However, it doesn't discuss potential rate limits or data freshness, so it's not fully 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?
Well-structured with a clear summary sentence, usage hint, free note, and an Args list. Each part serves a purpose and is front-loaded.
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 list tool with an output schema, the description covers the core purpose, usage context, parameters, and an additional note about pricing. It is complete enough for an agent to invoke correctly.
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 no descriptions for parameters, and the description compensates by fully explaining limit (including range and default) and offset (default). This exceeds what the schema provides.
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 the tool lists available KDP niche keywords ordered by demand score. The mention of 'available for detailed research' distinguishes it from the sibling research_niche tool by framing it as a discovery 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?
Provides clear context on when to use it ('to discover what Kindle publishing niches are available'), but doesn't explicitly name the alternative research_niche or state when not to use. This limits it to a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
research_nicheAInspect
Get detailed KDP niche intelligence for a specific keyword.
Returns demand score, competition score, Amazon BSR range, estimated
monthly revenue, review threshold, average book pricing, and data
freshness for the given Kindle publishing niche.
Pricing tiers (x402 USDC on Base network):
- $0.03 per query for cached/pre-seeded keywords
- $0.10 per query for live on-demand research (new keywords)
Use the free `list_niches` tool first to see available keywords.
Payment options:
1. Set the KDP_X_PAYMENT environment variable on the server for auto-pay.
2. Pass a valid x402 payment header via the x_payment argument.
3. If neither is set, the tool returns structured 402 payment instructions
that an x402-capable agent can use to construct and retry payment.
Args:
keyword: The KDP niche keyword to research (e.g. "romance novels", "keto cookbook")
x_payment: Optional base64-encoded x402 payment header. Takes precedence
over the KDP_X_PAYMENT environment variable.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | ||
| x_payment | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
A5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden and does so exceptionally. It discloses payment gating, the precedence of the x_payment argument over the environment variable, and the explicit fallback behavior of returning structured 402 payment instructions. This is transparent about how the tool behaves under different conditions.
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-organized with clear sections: an overview, pricing tiers in bullet points, payment options numbered, and args with descriptions. Every sentence adds necessary information, and the formatting makes it easy for an agent to parse.
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 has an output schema (so return values need no explanation), the description covers everything else: prerequisite usage (list_niches), pricing, payment methods, fallback behavior, and data freshness. It is complete for an agent to select and invoke this tool correctly.
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 description coverage is 0%, so the description must compensate, and it does. It explains `keyword` with examples ('romance novels', 'keto cookbook') and clarifies `x_payment` as a base64-encoded x402 header with precedence semantics. Both parameters are fully documented beyond the bare 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 opens with 'Get detailed KDP niche intelligence for a specific keyword,' which is a specific verb+resource+scope. It also lists concrete data points returned (demand score, competition score, BSR range, etc.), clearly distinguishing it from the sibling list_niches tool.
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 instructs to 'Use the free `list_niches` tool first to see available keywords,' naming the alternative and providing a workflow. It also details pricing tiers and payment options, giving the agent clear conditional guidance for when to auto-pay, pass a header, or expect a 402 response.
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.
2 tool updates
- First observed
list_niches - First observed
research_niche
Frequently Asked Questions
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io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
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Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
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For server owners:
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Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
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The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
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TDQS
A4.6/5.0
Disambiguation5/5
The two tools are completely distinct: list_niches provides an overview of available keywords, while research_niche dives into detailed metrics for a specific keyword. There is no overlap in purpose or output, making selection unambiguous.
Naming Consistency5/5
Both tools follow a consistent verb_noun convention with lowercase and underscores: list_niches and research_niche. The pattern is predictable and matches the server's research-focused domain.
Tool Count3/5
With only two tools, the server feels thin for a complete research workflow, though it covers the essential 'discover then investigate' flow. The count is borderline but not entirely inadequate given the focused purpose.
Completeness4/5
The core workflow of listing niches and researching a specific niche is covered with no dead ends. Minor gaps include lack of bulk research or filtering options, but agents can manually iterate over list results to achieve similar outcomes.