Bring Your AI
Server Details
No-data MCP handoff for local Claude Code to Codex harness moves. $49 lifetime.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.7/5 across 7 of 7 tools scored. Lowest: 3/5.
Each tool addresses a distinct function: installation, product listing, target listing, setup preview, move preview, licensing, and checkout. There is no overlap or ambiguity.
All tools follow a consistent verb_noun pattern in snake_case (e.g., install_local_cli, list_products, start_checkout). No mixing of styles or vague verbs.
Seven tools cover the essential operations for the server's domain (product listing, previews, licensing, checkout). The count is well-scoped without being too sparse or excessive.
The tool set covers the full workflow: installation, discovery (products/targets), previews (setup/move), pricing, and payment. There are no obvious missing operations for the stated purpose.
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, the description adequately discloses that it returns commands (no-data) and that the remote server is not affected. However, it does not detail side effects or parameter behavior beyond what is implied.
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 concise sentences efficiently state purpose and key behavioral constraint with no unnecessary words.
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—one optional parameter, no output schema—the description fully covers its purpose and behavior.
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 parameter descriptions. The tool description adds no extra meaning beyond the schema, so baseline score of 3 is appropriate.
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 returns local install and MCP wiring commands, distinguishing it from siblings like 'list_targets' and 'preview_build_setup' by focusing on installation and wiring.
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?
No explicit guidance on when to use this tool versus alternatives; only implicit context that the remote server does nothing, but lacks when-not or alternative tool mentions.
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?
No annotations are provided, so the description carries the burden. It states a read operation ('list') and adds a key behavioral constraint: 'No harness data is accepted.' This clarifies that the tool is non-destructive and restricts input, but lacks details on rate limits, data freshness, or return 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?
The description is a single, clear sentence that efficiently conveys the tool's purpose and a key constraint. No extraneous words; every part 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 (no parameters, no output schema, no nested objects), the description provides sufficient context: it lists products and payment modes and explicitly excludes harness data. However, it could briefly mention the expected return format or that it is safe for repeated calls.
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 baseline is 4 per rubric. The description adds no parameter info, but none is needed since the schema already covers 100% of the empty parameters. The description's mention of 'agent-readable' products provides context 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 identifies the tool as listing 'Bring Your AI products and supported payment modes'. It uses a specific verb ('List') and resource. However, it does not explicitly differentiate from sibling tools like 'list_targets', which might list different entities, but the negative constraint 'No harness data is accepted' helps clarify 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 provides no guidance on when to use this tool versus alternatives, such as when to list products versus targets or other operations. There is no mention of prerequisites, authentication, or context for invocation.
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?
Discloses that no harness data is accepted or returned, but lacks other behavioral details like listing scope or side effects.
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 concise sentences with no redundancy, efficiently conveying purpose and constraint.
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 zero-parameter, no-output schema tool, the description fully covers necessary context.
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?
No parameters, baseline 4; description adds a clarifying constraint ('no harness data') beyond 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?
Description clearly states it lists Bring Your AI target tools, with specific mention of no harness data, distinguishing it from potential siblings.
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?
Implicitly suggests when not to use (if harness data needed) but no explicit alternatives or when-to-use guidance.
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 the full burden of behavioral disclosure. It reveals the tool is 'no-data' and 'free,' implying no destructive side effects, but does not describe what happens if unacceptable inputs are passed, nor the response format or error handling.
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, front-loading the core purpose in the first sentence and constraints in the second. Every word is necessary and no redundancy exists.
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 tool with a single parameter and no output schema, the description adequately covers purpose and constraints. It lacks information about return value or preview behavior, but given low complexity, it is mostly 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 covers both the description and examples for the 'to' parameter (target tool id), including a reference to 'list_targets' for available options. The description adds context about '13 supported tools' but does not clarify other aspects like format or expected tool IDs beyond what the schema already provides.
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 is for a 'free no-data preview for building a user's first setup in any of the 13 supported tools,' specifying the action (preview), resource (first setup), and scope (13 tools). It distinguishes itself from siblings like 'preview_move' by focusing on initial setup rather than moving.
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 explains when to use the tool (for first setup preview) and explicitly lists four types of inputs it does not accept, advising against sending GitHub handles, memories, etc. However, it does not mention alternative tools for those inputs or provide explicit when-not-to-use guidance beyond the exclusion list.
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?
Despite no annotations, the description transparently states it is a no-data preview, returns only feasibility copy, and does not accept mappings or file paths. This adequately communicates the tool's non-destructive, read-only 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?
The description is two sentences, front-loaded with the key purpose and constraints. Every sentence provides necessary information without 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?
Given the simplicity of the tool (2 required parameters, no output schema), the description covers purpose, return type, and exclusions. It could mention error handling or response format but is largely complete for its complexity.
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%, but the description adds value by naming all 13 supported tools and emphasizing the 'from' and 'to' parameters, clarifying their roles beyond the schema's examples.
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 specifies a 'free no-data preview for moving a harness', listing all 13 supported tools. It distinguishes itself from siblings like preview_build_setup by focusing on moves and not accepting mappings or file paths.
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 usage for checking feasibility without data, but does not explicitly state when to use or avoid this tool compared to alternatives like list_targets or preview_build_setup.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
quote_lifetime_licenseBInspect
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?
No annotations exist, so the description carries the burden. It adds useful context about checkout methods (Stripe Payment Links for humans, Link-issued token for agents). However, it does not disclose side effects, idempotency, or what the tool returns (e.g., a price or payment link), leaving behavioral gaps.
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 wasted words. The first sentence states the core purpose, and the second adds context on checkout methods. It is concise but could benefit from a slightly more structured approach.
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 4 optional parameters, no output schema, and no annotations, the description is incomplete. It does not explain what the tool returns (e.g., a price, a payment link, a quote ID) or clarify that it is strictly a quoting step before purchase. The mention of 'checkout' may confuse agents into thinking it handles payment.
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 schema fully describes all four optional parameters. The description adds no additional parameter meaning beyond what the schema provides, making a score of 3 appropriate as a 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 verb 'Quote' and the resource 'Bring Your AI lifetime license in USD', indicating a pricing/quotation function. However, it does not explicitly differentiate from sibling tools like 'start_checkout' or 'list_products', leaving room for ambiguity in the overall workflow.
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 mentions two checkout methods (human vs. agent), providing some usage context. It does not explain when to use this tool versus alternatives, such as whether it should be called before 'start_checkout' or how it relates to listing products.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
start_checkoutBInspect
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?
With no annotations, the description must fully disclose behavior. It describes one specific case (stripe_spt) but lacks details on other payment modes, side effects, or safety considerations 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?
Single sentence, front-loaded with main action. Concise but could be more structured to cover all modes.
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?
No output schema or annotations; description only covers one payment mode and does not explain return values for other cases or general behavior. Incomplete for a 5-parameter payment tool.
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?
Adds value beyond schema by explaining the stripe_spt parameter combination, aliasing email to buyer_email, and defaulting product_id. Schema coverage is 100%, so baseline 3, but description provides extra context.
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 'Start checkout' as the verb+resource, and provides a specific scenario. However, it does not differentiate from sibling tools, though none are directly similar.
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?
No explicit guidance on when to use this tool vs alternatives, no prerequisites or when-not-to-use 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
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
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
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
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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