booking
Server Details
Reserve a paid AI consultation with Akshay Shetty; returns a Stripe payment link.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Managed credentials
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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 4.4/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one lists available slots, the other requests a reservation. No overlap or confusion possible.
Both tools use the consistent verb_noun pattern: list_consultation_slots and request_consultation, making the action and target clear.
With only 2 tools, the set feels thin for a booking system. While it covers listing and requesting, additional tools for cancellation or status checking would make it more robust.
Missing essential operations like canceling a booking or checking booking status. The workflow has a dead end after requesting, as the agent cannot manage the booking further.
Available Tools
2 toolslist_consultation_slotsAInspect
List available 60-minute paid AI-consultation slots with Akshay Shetty (EUR 500, timezone Europe/Tallinn). Returns ISO-8601 UTC start times to pass to request_consultation.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses the return format (ISO-8601 UTC start times) and basic slot characteristics. However, it does not mention potential behaviors like authentication requirements, rate limits, or what happens when no slots are available.
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, well-structured sentence that front-loads the core action and key details. 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 simplicity (no parameters, no output schema), the description covers the essential information: what the tool lists, the slot details, and the output format. It could mention whether the list is exhaustive or paginated, but for this tool it is largely 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?
There are no parameters, so the description needs no additional parameter info. The baseline score for zero parameters is 4, and the description does not detract from that.
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 lists available 60-minute paid AI-consultation slots with specific details (provider, price, timezone). It also explains the output format and its intended use with the sibling tool, making the purpose unmistakable.
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 implicitly guides usage by stating the output is to be passed to request_consultation, indicating a two-step workflow. It provides context about the slots but lacks explicit when-to-use or when-not-to-use guidance relative to the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_consultationAInspect
Reserve a consultation slot and get a secure Stripe payment link. The booking is only CONFIRMED once the human completes the EUR 500 payment at the returned checkout_url — nothing is charged automatically and an unpaid hold expires in ~30 minutes.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Full name of the person booking | |
| tier | No | Consultation type: 'strategy' = AI strategy & sovereign automation (EUR 500); 'coaching' = life/personality/health coaching (EUR 200). Defaults to strategy. | |
| Yes | Contact email (confirmation is sent here) | ||
| notes | No | Optional: what you'd like to discuss | |
| client_tz | No | Optional IANA timezone of the person, e.g. Europe/London | |
| slot_start | Yes | An ISO-8601 UTC slot_start from list_consultation_slots, e.g. 2026-07-01T09:00:00Z |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses that the booking is confirmed only upon payment, no automatic charge, and a 30-minute expiry for unpaid holds. This is comprehensive.
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, first sentence conveys the core action, second adds critical behavioral detail. No redundant wording.
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?
Though lacking an output schema, the description mentions the returned checkout_url and confirmation via email. Given the tool's complexity and payment flow, this is quite 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?
Schema coverage is 100%, so parameters are well-documented. The description adds value by noting default tier and price, and linking slot_start to `list_consultation_slots`. Baseline 3, extra context justifies 4.
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 'Reserve' and the resource 'consultation slot', and explains the payment link generation. It distinguishes itself from the sibling tool 'list_consultation_slots' by focusing on the booking action.
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 after `list_consultation_slots` and details the payment flow and expiration. It does not explicitly state when not to use it, but the context is clear.
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
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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
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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