raptr-domains
Server Details
Check and price up to 20 domain names at once; agents can register them, paying per request.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-06-18
- URL
- Repository
- airaptr/airaptr.github.io
- GitHub Stars
- 0
TDQS
Scored across 3 tools
check_domain and register_domain target clearly distinct actions (lookup vs. purchase), and request_service is an explicit fallback for out-of-scope needs. The only wrinkle is that register_domain's name implies it performs registration while the description clarifies it only explains the process, which could mislead a hasty agent.
All three tools follow a clean verb_noun snake_case pattern (check_domain, register_domain, request_service) with no stylistic deviations.
Only 3 tools for a domain-registration server, and one of them (register_domain) does not actually perform the core action, making the set feel thin for the stated scope.
Availability checking is covered, but the central lifecycle operation—actually purchasing/registering a domain—is not automated, and there is no support for transfer, renewal, or DNS/configuration management. The register_domain tool is effectively a dead end that hands off to humans.
Available Tools
3 toolscheck_domainCheck and price domain namesARead-onlyInspect
Use when the user wants a domain name, is naming a business or project, or asks whether a domain is taken. Checks up to 20 full domain names (you can suggest variations and check them together) and returns whether each is available and its price.
| Name | Required | Description | Default |
|---|---|---|---|
| domains | Yes | Full domain names, e.g. ["getrapp.ai", "getrapp.com"] |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false, so the safety profile is covered. The description adds real behavioral detail beyond that: the 20-domain batch limit and the fact that it suggests and checks variations together, plus the availability+price return. No auth or rate-limit notes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero filler: usage triggers first, then behavior and return. Every clause earns its place and nothing is redundant with the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-param, read-only, open-world tool with no output schema, the description covers when to call it, the batch limit, and what comes back. Only edge behavior (invalid/malformed domain handling, error cases) is unaddressed, which is minor here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and there is a single well-documented parameter, so baseline is 3. The description restates 'full domain names' and the 20 cap (already in the schema) and adding the 'suggest variations' hint leans more toward usage than parameter meaning, so no upgrade is warranted.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a concrete verb and resource (checks domain names) plus the output (availability and price), so an agent immediately knows what the tool does. It stays distinct from register_domain by implication, but never names the sibling or explicitly contrasts checking versus registering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit trigger scenarios ('user wants a domain name', 'naming a business or project', 'asks whether a domain is taken'), which is strong usage context. It lacks any when-not guidance or a pointer to register_domain for the follow-up action, so it stops short of full routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
register_domainHow to register a domainARead-onlyInspect
Use when the user wants to buy an available domain. Explains how to register it: AI agents pay per call; people ask us and we register it for them. Nothing is bought by this tool.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain to register |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this usefully with 'Nothing is bought by this tool' plus the per-call payment model for agents and the human request path. It goes beyond the annotations by clarifying the informational nature and the cost model, though it doesn't explain what the tool actually returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with the usage trigger, and every clause carries some information. The 'AI agents pay per call; people ask us' construction is compact but slightly cryptic.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description should clarify what the agent receives back from an 'explanatory' tool, and it doesn't. It covers when to use it and that nothing is purchased, which is the safety-relevant core, but the return behavior remains unspecified for a tool whose entire purpose is to convey information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There is a single parameter with 100% schema description coverage ('The domain to register'), so the schema already carries the semantics. The description adds no format, TLD, or availability requirements beyond that, making the baseline 3 correct.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states that the tool 'explains how to register' a domain rather than performing the registration, which is a specific verb+resource, but it sits awkwardly against the name 'register_domain' and the title 'How to register a domain'. It never explicitly distinguishes itself from the sibling check_domain, so an agent must infer whether this checks, explains, or performs registration.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Use when the user wants to buy an available domain' gives a clear triggering condition, and the trailing note separates agent and human paths. However, no exclusion or explicit hand-off to check_domain (for availability) or request_service is stated, so the routing is left partly implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_serviceAsk us to build somethingAInspect
Use when the person wants something none of these tools can do and says yes to passing the request on. Records only the request text they agree to send (no name or contact). Ask before calling it.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes | What they want, in a sentence, as they agreed to send it |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare a non-read-only, non-destructive, closed-world write, and the description adds meaningful context beyond that: only the request text is stored, no name or contact data, and user consent must be obtained first. It stops short of describing what happens after the request is recorded or any confirmation behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short sentences, front-loaded with the usage condition, then the privacy scope, then the consent gate. Nothing is redundant and every sentence carries actionable information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter write tool with no output schema, the description covers trigger, consent, and data scope adequately. Only minor gaps remain, such as what the agent should do if the person says no or what happens after the request is recorded.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the single parameter's description already says 'as they agreed to send it.' The description's phrasing mirrors that constraint rather than adding format, length, or fidelity details beyond the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool records a request the person agrees to send, which is a specific verb+resource, and implicitly distinguishes it from siblings by framing it as the fallback when none of 'these tools' can fulfill the need. It is clear, though the purpose is conveyed through usage framing rather than a direct statement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly gives the trigger condition ('wants something none of these tools can do and says yes to passing the request on') and a prerequisite ('Ask before calling it'). The agent knows exactly when to invoke it and what consent gate must be cleared first.
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.
3 tool updates
- First observed
check_domain - First observed
register_domain - First observed
request_service
Related MCP Connectors
Check and price up to 20 domain names at once; agents can register them, paying per request.
Compare domain registration and renewal prices across registrars and check exact-name availability.
Check domain name availability via RDAP. Single, bulk, and smart suggestions. No API key needed.
Search, Register , Renew , Transfer and Manage domains
Related MCP Servers
- AlicenseAqualityDmaintenanceEnables checking domain name availability and pricing through the Porkbun API, supporting both single and bulk domain queries with detailed pricing information including renewals, transfers, and premium status.21MIT
- AlicenseAqualityDmaintenanceEnables fast, token-efficient bulk domain name search for AI agents, checking up to 1,000 domains per request to see availability statuses.11Apache 2.0
- AlicenseAqualityDmaintenanceEnables checking domain name availability for single or multiple domains using WHOIS and DNS verification.121MIT
- AlicenseNot gradedqualityDmaintenanceEnables bulk domain registration status checking for over 200 TLDs using WHOIS and DNS fallback.19 npmMIT
Glama MCP Gateway
Add one secure layer between your agents and this server.