Check if a brand name is free across domains, GitHub, npm and PyPI, and suggest available names.
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Average 4.1/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one generates candidate names and the other checks availability across multiple platforms. There is no overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern (check_name, generate_names) using snake_case, making them predictable and easy to understand.
With only 2 tools, the server is minimal but well-scoped for the domain of generating and checking names. While additional features like domain history or registration could be added, the current count is reasonable for a focused utility.
The tool surface covers the complete workflow: generate candidate names pre-checked for domains, then fully check favorites across namespaces. No obvious gaps exist for the stated purpose of finding available brand names.
check_nameCheck brand-name availability across domains + namespacesACheck whether one or more candidate names are free to use as a brand — across domains and the GitHub / npm / PyPI namespaces — in a single call.
| Name | Required | Description | Default |
|---|---|---|---|
| tlds | No | ||
| names | Yes | ||
| surfaces | No |
| Name | Required | Description |
|---|---|---|
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate safe, idempotent, read-only behavior. The description adds that it checks across domains (tlds) and multiple namespaces (surfaces) in 'a single call', providing useful operational context beyond the annotations.
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?
One sentence, 24 words, front-loaded with the action. Every word adds value with no redundancy or filler.
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?
Given the presence of an output schema and annotations, the description is complete enough for an agent to understand the tool's purpose and basic behavior. It covers the main parameters and scope, though it could benefit from mentioning output format summary or order of checks.
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?
With 0% schema description coverage, the description must add meaning. It mentions 'domains' (mapping to tlds) and 'GitHub / npm / PyPI' (mapping to surfaces), and names to check. However, it does not explicitly describe each parameter or their constraints, only implying their roles.
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 clearly states the verb 'Check' and the resource 'brand-name availability across domains and namespaces'. It distinguishes itself from the sibling 'generate_names' by focusing on checking availability rather than generating names.
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?
The description implies use when you need to check names for brand availability, but does not explicitly state when not to use it or mention alternatives beyond the sibling name. No when/when-not or context for exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_namesGenerate candidate names, pre-checked for domain availabilityAGenerate candidate names from a description, each pre-checked for domain availability. Then call check_name on the favorites for the full cross-namespace picture.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| useHacks | No | ||
| description | Yes |
| Name | Required | Description |
|---|---|---|
| query | Yes | |
| aiUsed | Yes | |
| candidates | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive behavior. The description adds valuable context about domain availability pre-checking and follow-up recommendation, without contradictions.
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, front-loaded with the main action, no unnecessary words. Every sentence adds value.
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?
While purpose and guidelines are clear, the description lacks detail on parameters and does not reference the output schema. For a tool with 3 parameters and 0% schema coverage, this is insufficient.
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 0%, so description must compensate for parameters. It only mentions 'description' implicitly, but does not explain 'count' or 'useHacks', leaving ambiguity for correct invocation.
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 clearly states 'Generate candidate names from a description' specifying the verb and resource. It also distinguishes from the sibling 'check_name' by noting the domain pre-check and directing to use check_name for full cross-namespace details.
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 tells when to use the tool (generate candidate names from a description) and when to use the sibling (call check_name for full picture). Provides clear guidance on workflow.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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