Reserve a paid AI consultation with Akshay Shetty; returns a Stripe payment link.
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Average 4.5/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one lists available slots, the other reserves a slot and provides a payment link. There is no ambiguity.
Both tools follow a consistent verb_noun pattern (list_consultation_slots and request_consultation), making the purpose clear at a glance.
With only 2 tools, the surface is minimal, but it fits the narrow domain of booking a single consultation service. Slightly under but reasonable.
The tools cover listing and reserving slots, but lack cancellation, status checking, or manual payment handling beyond the link. Notable but not critical gaps for this domain.
list_consultation_slotsAInspectList 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 | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides all behavioral context: slots are 60-minute, paid, with a specific person, and returns ISO-8601 UTC times. It does not specify if the list changes frequently or if there are limits, but it is adequate.
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 essential information. No redundant words, and 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?
Given no parameters, no output schema, and a simple list operation, the description is complete. It explains the purpose, prerequisites (who, price, timezone), and output format, enabling correct invocation and follow-up.
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 naturally covers the parameter aspect. It adds meaning beyond the empty schema by specifying the slot characteristics.
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 specifies the exact action (list), the resource (60-minute paid AI-consultation slots with Akshay Shetty), and key details (price, timezone, output format). It clearly distinguishes from the sibling tool 'request_consultation'.
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 the tool should be used before 'request_consultation' by noting that it returns start times to pass to that tool. However, it lacks explicit guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_consultationAInspectReserve 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully covers behavioral traits: nothing is charged automatically, payment is required via the returned checkout_url, and unpaid holds expire in ~30 minutes. This is comprehensive for a booking 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 concise sentences with no fluff. It front-loads the action and key constraints, making it easy to scan.
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 6-parameter tool with no output schema, the description covers the critical workflow: payment link, expiration, and confirmation requirement. The return value (checkout_url) is mentioned, and the behavioral details are sufficient.
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 baseline is 3. The description adds context for 'tier' (different prices and types) and 'slot_start' (must be from list_consultation_slots), but does not elaborate on other parameters. It meets the baseline adequately.
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 reserves a consultation slot and provides a Stripe payment link, with explicit distinction from sibling 'list_consultation_slots'. The verb 'reserve' and resource 'consultation slot' are precise.
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 the workflow: choose a slot from list_consultation_slots, then book via this tool. It clarifies that booking is confirmed only after payment and that unpaid holds expire. It could explicitly state when not to use, but the context is adequate.
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.
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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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The server is experiencing an outage
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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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