Find DDG agent-callable services matching a natural-language NEED.
Returns the best-matching services (service_id, path, price, description) so an
agent planner can pick the right one and then invoke it with ddg_call(service, args).
This is the entry point to all DDG services — search first, then call.
| Name | Required | Description | Default |
|---|---|---|---|
| need | Yes | ||
| top_k | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It discloses the return shape and workflow: returns best-matching services with service_id, path, price, description, and is meant to be followed by ddg_call. It does not discuss edge cases like empty results or ranking guarantees, but for a low-risk search tool the disclosed behavior 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?
Three concise sentences with no redundancy. The purpose is front-loaded, followed by the return value and the recommended workflow. Every sentence contributes useful 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 the tool's simplicity and the presence of an output schema, the description covers the essential search, pick, and call flow. It could be more complete by explicitly naming ddg_list_services as the alternative for browsing all services and by explaining top_k, but it is sufficient for an agent to 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?
The schema has 0% description coverage, so the description must compensate. It does clarify that "need" is a natural-language query, but it never explains top_k or how it affects results. The parameter name and default value make top_k's likely meaning inferable, but the description itself leaves it unaddressed.
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 uses a specific verb and resource: "Find DDG agent-callable services matching a natural-language NEED." It clearly distinguishes this search tool from the later invocation step ddg_call and from related sibling tools like ddg_list_services by emphasizing matching rather than listing.
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?
It gives clear usage context: "This is the entry point to all DDG services — search first, then call." This tells the agent to use it before ddg_call and explains the overall workflow. It does not explicitly mention when to prefer ddg_list_services or other alternatives, so it is not a full 5.
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.