Bring Your AI
Server Details
No-data MCP handoff for local Claude Code to Codex harness moves. $49 lifetime.
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- Streamable HTTP
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Tool Definition Quality
Average 3.9/5 across 7 of 7 tools scored.
Each tool targets a distinct resource or action: installation, product listing, target listing, build preview, move preview, license quoting, and checkout. No two tools have overlapping purposes, and the descriptions clearly differentiate these functions.
All tool names follow a consistent snake_case verb_noun pattern (e.g., list_products, preview_build_setup, start_checkout). There are no mixed conventions, vague verbs, or unpredictable nomenclature, making tool selection straightforward.
Seven tools is well within the ideal range for a focused MCP server. Each tool serves a distinct and necessary function in the customer journey from discovery to purchase, with no redundancy or bloat.
The tool set comprehensively covers the server's stated purpose: installation guidance, product and target discovery, previews for both setup and migration, licensing quotes, and checkout. Since the server is designed to be no-data and hands-on work happens via the local CLI, there are no obvious missing operations.
Available Tools
7 toolsinstall_local_cliAInspect
Return no-data local install and MCP wiring commands. The remote server does not install anything and does not receive harness data.
| Name | Required | Description | Default |
|---|---|---|---|
| target_agent | No | Optional: claude-code or codex. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool only returns commands, does not install anything remotely, and does not receive harness data. This is valuable non-obvious behavior. It could go further by explicitly stating the tool does not execute the commands, but 'return commands' strongly implies that.
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 core purpose, then a clarifying negative. Every word earns its place; no fluff or redundancy.
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 simple command-return tool with no output schema and no annotations, the description covers the essential context: what it returns, that it's local-only, and that it avoids data sharing. It could explain what 'MCP wiring commands' means for users unfamiliar with the domain, but this is a minor gap given the tool's simplicity.
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% (the optional target_agent is fully described in the schema). According to the rubric, this sets a baseline of 3. The description does not add any additional parameter information, which is acceptable given the schema covers it completely.
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 tool's purpose: 'Return no-data local install and MCP wiring commands.' This is specific (verb+resource) and distinguishes it from siblings like list_products and start_checkout. The additional clarifying sentence about the remote server not installing anything reinforces the scope.
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 gives clear context for when to use the tool: for local install and MCP wiring, and explicitly states what it does NOT do (remote install, data transfer). However, it does not name an alternative tool for remote install scenarios, unlike the high-watermark example, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_productsAInspect
List agent-readable Bring Your AI products and supported payment modes. No harness data is accepted.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. The verb 'List' correctly implies a non-mutating read operation, and 'No harness data is accepted' clarifies an input constraint. However, it does not describe the return format or whether any authentication/rate limits apply, leaving some behavioral ambiguity.
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?
The description is only two sentences, front-loaded with the primary action and then a clarifying constraint. There is no fluff or redundant information; every word earns its place.
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 simple no-parameter tool, the description covers the key aspects: what is listed (products and payment modes) and an important constraint (no harness data). It does not explicitly state the return format, but the verb 'List' implies the output is a listing. Given the low complexity, this is nearly complete, though a tiny bit more detail about output would make it fully complete.
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 zero parameters, the baseline is 4. The description adds the note 'No harness data is accepted,' which reinforces the absence of parameters and clarifies that no input is expected. This adds a small semantic touch beyond the empty schema.
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 tool's purpose with a specific verb ('List') and resource ('agent-readable Bring Your AI products and supported payment modes'). It distinguishes itself from siblings like list_targets by focusing on products rather than targets, and the qualifier 'agent-readable' adds useful scope.
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 a clear use case: when you need to see available products and payment modes. However, it does not explicitly mention when NOT to use it or name alternative tools like list_targets, so the guidance is implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_targetsAInspect
List Bring Your AI target tools. No harness data is accepted or returned.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It adds a useful constraint: 'No harness data is accepted or returned,' clarifying the tool's scope. However, it doesn't explicitly state read-only behavior or return format, though 'List' strongly implies read-only.
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?
The description is two short sentences, front-loaded with the action and resource. The second sentence adds a significant constraint without redundancy. Every word earns its place.
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 tool's simplicity (zero parameters, no output schema), the description is mostly complete: it states the purpose and a key behavioral limitation. However, it doesn't describe the return value's format or structure, which could be useful since there is no output schema.
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?
The tool has zero parameters, so the baseline is 4. There is no parameter information to add beyond the schema, which is fully described as empty. The description doesn't need to compensate for missing parameter details.
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 uses a specific verb ('List') and a specific resource ('Bring Your AI target tools'), clearly distinguishing it from sibling tools like list_products. It is unambiguous about what the tool does.
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?
There is no explicit guidance on when to use this tool versus alternatives such as list_products. The sentence 'No harness data is accepted or returned' is a constraint, not usage guidance. The intended use is only implied by the name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
preview_build_setupAInspect
Free no-data preview for building a user's first setup in any of the 13 supported tools. Does not accept GitHub handles, generated memories, mappings, or file content.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | Target tool id, e.g. claude-code, codex, cursor. Use list_targets for the full matrix. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the preview is 'no-data' and 'free', and lists excluded input types (GitHub handles, generated memories, mappings, file content). However, it does not describe side effects, return format, or safety profile (e.g., read-only nature), leaving gaps for the agent.
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 purpose. The second sentence lists constraints succinctly without fluff. Every word contributes to clarity.
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 simple one-parameter tool, the description covers main purpose and constraints, but lacks details on what the preview returns or how it differs from preview_move. Since there is no output schema, the agent is left without expectations for the result. It is adequate but not fully complete.
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?
The schema already covers the 'to' parameter with examples. The description adds value by mentioning '13 supported tools' and directing the agent to 'Use list_targets for the full matrix', which enriches the parameter semantics beyond the schema.
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 tool's function: a free preview for building a user's first setup across 13 supported tools. It distinguishes itself from sibling preview_move by specifying 'first setup', though it does not explicitly compare with that sibling.
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 implies usage for previewing a first setup and mentions what inputs are not accepted, but does not explicitly state when to use this tool over alternatives like preview_move or start_checkout. The 'does not accept' clause gives some constraint guidance but not a clear when-to-use vs when-not-to-use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
preview_moveAInspect
Free no-data preview for moving a harness between any two of the 13 supported tools (Claude Code, Cursor, Codex, OpenClaw, Aider, Continue, Cline, Goose, Zed, Roo Code, ChatGPT, Claude.ai, Copilot). Returns feasibility copy only. Does not accept or return mappings, file paths, generated content, or validation notes.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | Target tool id, e.g. codex, cursor, claude-code, copilot. Use list_targets for the full matrix. | |
| from | Yes | Source tool id, e.g. claude-code, cursor, codex, openclaw, aider, continue, cline, goose, zed, roo, chatgpt, claude-ai. Use list_targets for the full matrix. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It transparently states it 'Returns feasibility copy only' and 'Does not accept or return mappings, file paths, generated content, or validation notes', which clarifies its limited behavior. It also says 'no-data', implying no data is transmitted, but does not mention side effects or authentication, so it is not a perfect 5.
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?
The description is front-loaded with the core purpose ('Free no-data preview for moving a harness') and remains concise across three sentences. Every sentence adds value: the tool list, the return type, and the explicit exclusions. There is no fluff.
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 simple two-parameter tool with no output schema and no annotations, the description is quite complete: it explains what it does, what it returns (feasibility copy), and what it does not handle. It could be slightly more explicit about the nature of 'feasibility copy', but overall it is sufficient for an agent to decide on usage.
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% with both parameters (from, to) described, so the baseline is 3. The description lists the supported tool names, but this repeats the examples already present in the schema descriptions. It adds no additional parameter semantics beyond what the schema provides, so no score above baseline.
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 tool provides a 'Free no-data preview for moving a harness between any two of the 13 supported tools', using a specific verb ('preview') and resource ('moving a harness'). It lists the exact tools, distinguishing it from sibling tools like preview_build_setup, which focuses on build setup.
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 gives clear context: it is for previewing a move without data, and it explicitly states what it does not accept or return (mappings, file paths, etc.), which serves as a 'when-not' guideline. However, it does not name alternative tools for related tasks (e.g., list_targets for the full matrix), falling short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
quote_lifetime_licenseAInspect
Quote a Bring Your AI lifetime license in USD. Human checkout uses Stripe Payment Links; agent checkout can settle a Link-issued Stripe shared payment token.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | Optional BCP 47 locale. | |
| currency | No | Optional requested currency. Only USD is currently supported. | |
| product_id | No | Optional product id. Defaults to bringyour_founder_lifetime. | |
| buyer_country | No | Optional ISO country code. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosure. It adds useful context about Stripe Payment Links and token settlement, but it does not clarify whether the operation is read-only, creates any state, or details the response format. This is a moderate level of transparency.
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?
The description is two sentences, with the first sentence stating the purpose and the second adding relevant checkout context. Every word earns its place; there is no filler or redundancy.
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?
There is no output schema, so the description must explain return values, but it does not state what the quote returns (price, link, token, etc.). It also omits any prerequisites or follow-up steps. While the tool is simple, the missing output information is a significant gap.
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?
The schema has 100% parameter description coverage, so the baseline is 3. The description adds no additional meaning beyond the schema's own parameter descriptions, such as locale, currency, or product_id.
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 tool's purpose: 'Quote a Bring Your AI lifetime license in USD.' This uses a specific verb and resource, and the mention of checkout methods distinguishes it from sibling tools like start_checkout.
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 provides clear context by explaining the difference between human checkout (Stripe Payment Links) and agent checkout (shared payment token), implying when this quote tool is needed. However, it does not explicitly name alternatives or state exclusions, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
start_checkoutAInspect
Start checkout. With payment_mode=stripe_spt plus shared_payment_granted_token and buyer_email, settles a Stripe PaymentIntent and returns the signed license without opening a browser.
| Name | Required | Description | Default |
|---|---|---|---|
| No | Alias for buyer_email. | ||
| product_id | No | Optional product id. Defaults to bringyour_founder_lifetime. | |
| buyer_email | No | Email bound to the issued license. Required for stripe_spt settlement. | |
| payment_mode | No | stripe_payment_link, stripe_link, link, stripe_checkout, stripe_acp, stripe_spt, or x402. | |
| shared_payment_granted_token | No | Link-issued Stripe shared payment token. Required for stripe_spt settlement. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the key behavior for the stripe_spt path: it settles a PaymentIntent, returns a signed license, and does not open a browser. However, it does not explain side effects for other payment modes, permissions, idempotency, failure behavior, or whether browser redirection occurs for non-SPT modes, which is significant for a payment tool.
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?
The description is two sentences with no filler. The first sentence names the action, and the second provides a specific mode detail. Every sentence earns its place and the most important clarifying behavior is front-loaded.
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?
Despite full schema coverage, the description is incomplete for a checkout tool with multiple payment modes. It only explains the stripe_spt special case and does not describe behavior for other modes, generic return values, error conditions, or whether a browser is normally opened. There is no output schema or annotations to compensate.
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%, so the baseline is 3. The description adds semantic value by specifying that payment_mode=stripe_spt, shared_payment_granted_token, and buyer_email must be used together to settle without a browser, which clarifies parameter relationships beyond individual schema descriptions.
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 identifies the tool as initiating checkout and provides a specific behavioral detail: with certain parameters it settles a Stripe PaymentIntent and returns a signed license. It does not explicitly contrast with sibling tools, though its function is distinct from quote/listing/preview tools.
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 when to use the tool (to start checkout) and gives a clear special-case usage for stripe_spt, but it provides no explicit guidance on when to choose this tool over alternatives or when not to use it. No exclusions or alternative tool references are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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