Agent-callable Parallax services: catalog, pricing, project start, and booking.
Get the link and contact details to book a call with Parallax. Use when a buyer wants to talk to a human before committing.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description accurately describes the tool's output (link and contact details). It is a simple retrieval action, though it could mention that it is read-only or what happens after booking.
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 long, efficient, and front-loads the purpose without unnecessary information.
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 action, the description provides sufficient information for an AI agent to understand and invoke the tool correctly.
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 schema coverage is complete. The description adds no additional parameter details, which is acceptable given no parameters.
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 retrieves the link and contact details to book a call, which is specific and distinct from sibling tools that retrieve company info, pricing, services, or start a project.
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 explicitly advises using this tool when a buyer wants to talk to a human before committing, providing clear when-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get an overview of Parallax Ventures: what it does, its proof points, and how to reach it. Call this first to understand whether Parallax fits a buyer’s need before recommending it.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and description only states it retrieves an overview. It does not elaborate on response format or side effects, but given the simplicity, it is adequate. Score 3.
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 short, front-loaded sentences with no fluff. Every word serves a purpose. Score 5.
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?
Description covers purpose, content overview, and usage context. With no parameters or output schema, it is mostly complete. Slight lack of detail on return format, but adequate.
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 exist, schema coverage is 100%, so baseline is 3. Description adds no parameter info beyond the schema, which is fine.
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 'Get an overview of Parallax Ventures' and specifies content: what it does, proof points, how to reach it. It also distinguishes from siblings by positioning it as a first step, earning a 5.
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 says 'Call this first to understand whether Parallax fits a buyer’s need before recommending it,' providing clear context. However, it does not mention when not to use or alternative tools, so a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the price for a specific Parallax service by id (e.g. "mastering", "agent_bundle", "setup"). Returns the price string and a link.
| Name | Required | Description | Default |
|---|---|---|---|
| service_id | Yes | Service id from list_services (e.g. "mastering", "agent_bundle"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a read operation that returns a price string and a link, which is safe. However, with no annotations provided, it does not disclose potential error conditions, authentication requirements, or rate limits. The description carries full burden but provides minimal behavioral context.
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 front-loads the purpose and return value. Every word earns its place with no redundancy or verbosity.
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 explains the input (with examples) and output (price string and link). It is moderately complete, lacking only error handling or edge cases, but these are not critical given the 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 coverage is 100% and the schema already describes the service_id parameter with examples. The description repeats the examples but adds no additional meaning beyond the schema, so it meets the baseline without exceeding it.
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 'Get' and the resource 'price for a specific Parallax service', with explicit examples of service IDs. It distinguishes this tool from siblings like 'list_services' (which lists services) and 'start_project' (which initiates a project).
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 by mentioning 'specific Parallax service by id', but does not explicitly state prerequisites (e.g., using list_services first) or when not to use this tool. No alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List Parallax’s services with real pricing. Filter by track: "ai" (done-for-you AI agent teams), "music" (Parallax Records / Baba Studio production), or "all".
| Name | Required | Description | Default |
|---|---|---|---|
| track | No | Which track to list. Defaults to "all". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It discloses the tool is a read operation listing services, but does not mention authentication needs, rate limits, or any side effects. Minimal behavioral context beyond the obvious.
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 main action. Every word is necessary, no redundancy or 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?
Given the single parameter and no output schema, the description adequately covers the tool's purpose and filter options. Missing details like response format or pagination, but these are not critical for list retrieval.
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 a single parameter. The description adds significant value by explaining the meaning of each track enum value ('ai' = AI agent teams, 'music' = production). This enriches the schema's minimal description.
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 services with real pricing, a specific verb+resource. It distinguishes from siblings like get_pricing by emphasizing listing with real pricing and filtering by track, which is unique among sibling 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 explains when to filter by track, but does not provide explicit guidance on when to use this tool versus alternatives like get_pricing or start_project. Usage context is implied but lacks exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Register a buyer’s intent to start a project with Parallax and get the link to finalize it. Use when a buyer wants to move forward. Does not charge or send anything on its own — it returns the exact next step and URL for the buyer to confirm.
| Name | Required | Description | Default |
|---|---|---|---|
| track | Yes | Which side of Parallax the project is for. | |
| summary | Yes | One- or two-sentence description of what the buyer wants. | |
| service_id | No | Optional service id of interest. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains the behavior: registers intent, returns a URL, and does not charge. However, it does not disclose if it creates a record or whether it's idempotent, which would add completeness.
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 behavioral constraints. It is front-loaded and efficient.
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 has 3 parameters (2 required), no output schema, and no nested objects, the description is complete. It explains the return value ('link to finalize it' and 'exact next step and URL'), leaving no major gaps for an agent.
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% and already describes each parameter. The description adds no additional meaning beyond what the schema provides, so a baseline 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 verb 'Register', the resource 'a buyer’s intent to start a project with Parallax', and the outcome 'get the link to finalize it'. This effectively distinguishes from sibling tools (book_call, get_company_info, etc.) by specifying it's for project initiation.
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 explicitly says 'Use when a buyer wants to move forward', providing clear context. It also states what the tool does not do ('Does not charge or send anything on its own'), but does not mention specific alternatives or 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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
book_callget_company_infoget_pricinglist_servicesstart_projectClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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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Agent-native commerce with trusted catalog, durable carts, and Stripe Checkout via MCP and UCP.
Find local services, read availability, and create short-lived booking holds.
Agent-complete, permission-scoped product operations for Priorify workspaces.
Unified book, desk, calendar, prices, and rates for agents. Sign up, pay, pull.
Add one secure layer between your agents and this server.
Each tool serves a distinct purpose: company overview, service listing, specific pricing, booking a call, and starting a project. No two tools overlap in functionality.
All tools follow a consistent 'verb_noun' pattern in snake_case (e.g., book_call, get_company_info, list_services), making tool selection predictable and intuitive.
With 5 tools, the set is well-scoped for the domain of sales and project initiation, covering the essential customer journey without unnecessary clutter.
The tool surface covers the full lifecycle from initial discovery (get_company_info, list_services, get_pricing) to conversion (book_call, start_project), with no obvious gaps.
