Queue a local model/runtime request. This never auto-downloads by public request.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| reason | No | requested by agent swarm | |
| runtime | No | ollama | |
| agent_id | No | ||
| expected_size_gb | 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 only mentions that it never auto-downloads, but omits details on queue behavior, permissions, or error handling. The description does not reveal important behavioral traits.
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 brief (two sentences) and front-loaded with the core purpose. However, it is under-specified for the tool's complexity, and additional parameter context would be valuable.
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 5 parameters with 0% schema description coverage and no annotations, the description is insufficient. Even though an output schema exists, the description fails to explain what the tool does in enough detail for correct invocation.
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%, and the tool has 5 parameters. The description adds no meaning beyond the parameter titles, failing to explain fields like reason, runtime, agent_id, or expected_size_gb.
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 it queues a local model/runtime request and explicitly notes it never auto-downloads by public request. This distinguishes it from sibling tools like ddg_run_paid_model or ddg_list_models.
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 explicit guidance on when to use this tool vs alternatives. It implies it's for requesting local models that aren't auto-downloaded, but does not clarify prerequisites or contrast with tools like ddg_list_local_runtime_options.
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.
