Return the payment challenge for a supported DDG protected route without executing backend compute.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | /v1/model/chat-completions | |
| agent_id | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool does not execute backend compute, which indicates it is non-destructive but lacks details on authentication requirements, rate limits, or consequences of misuse.
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 sentence of 15 words, making it concise and front-loaded. It earns its place by providing essential purpose information, though it could add parameter context without significant bloat.
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?
The tool has 2 optional parameters and an output schema. The description adequately conveys the core action but omits context about the output schema or how the payment challenge is used. It is minimally viable but could be more 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 description coverage is 0%, but the description does not explain the parameters (path and agent_id). With 0% coverage, the description should compensate by describing parameter roles, but it does not, leaving the agent with only schema defaults.
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 a payment challenge without executing backend compute, specifying the verb 'return' and resource 'payment challenge'. It distinguishes from sibling tools that execute actions (e.g., ddg_submit_order), but could be clearer about what a 'payment challenge' entails.
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 guidance on when to use this tool versus alternatives like ddg_submit_order or ddg_run_paid_model. The description implies it's for quoting without execution, but does not explicitly state use cases, when-not-to, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose, ranging from status checks to order management to payment processing. Despite the large number, descriptions make them easy to differentiate, with no obvious overlap.
All tools share the 'ddg_' prefix, but naming patterns vary: some use verb_noun (e.g., ddg_list_models) while others use noun_noun (e.g., ddg_agent_status). This mix reduces consistency, though readability remains acceptable.
With 25 tools, the count is at the high end but scales to cover diverse aspects of payable services (status, orders, payments, models, x402). Minor consolidation could be possible, but most tools earn their place.
The tool surface covers core workflows like order lifecycle, payment, and service discovery. Minor gaps (e.g., no cancellation or refund tools) exist but do not severely hinder typical agent interactions.
