Return the AI-agent radar surfaces DDG is targeting and their current go-live gates.
| Name | Required | Description | Default |
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No arguments | |||
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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, and the description only states the return value. It does not disclose whether the operation is read-only, requires any special permissions, or has any side effects. This is a minimal disclosure for a 0-parameter 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 a single sentence that immediately conveys what the tool returns. It is front-loaded and contains no extraneous 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 no parameters and an output schema that likely documents the return structure, the description provides adequate context about the return values (radar surfaces and go-live gates). However, the terms 'radar surfaces' and 'go-live gates' may require domain knowledge, but with the output schema present, the description is complete enough.
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 0 parameters and schema coverage is 100% (trivially). The description does not need to add parameter information. According to the guidelines, 0 parameters yields a baseline of 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 'Return' and the specific resource 'AI-agent radar surfaces DDG is targeting' including the aspect of 'current go-live gates'. It distinguishes from sibling tools like ddg_agent_status which focuses on agent status, making the purpose unambiguous.
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 does not provide any guidance on when to use this tool versus alternatives such as ddg_agent_status or ddg_checkout_conformance. There is no mention of context, prerequisites, 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.
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.
