GET /v1/models — resolve a live model id before inference. Optional id retrieves one public row (same object as that id in the list). Do not hardcode model ids.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Optional venice model id (bare or pzero/). Omit for the full list. |
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It communicates that the operation is a read-only GET, returns a list or a single public row, and exposes live model ids that can change over time. It doesn't describe the exact response shape or pagination, but for a simple retrieval tool the disclosed behavior is sufficient.
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 sentences, no filler. The primary purpose and HTTP verb are front-loaded, the optional parameter behavior is stated compactly, and the anti-hardcoding warning is a single clear directive. Every clause earns its place.
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 tool with one optional parameter and no output schema, the description covers everything an agent needs: when to use it, how the parameter changes behavior, what kind of object is returned, and the critical constraint against hardcoding. Nothing essential is missing.
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 provides 100% coverage for the single optional id parameter, so the baseline is 3. The description adds value by explaining the behavioral consequence of providing id ('retrieves one public row') and clarifying that the single-row object matches the object found in the list. This goes beyond the schema's bare parameter 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 opens with 'GET /v1/models — resolve a live model id before inference,' which names a specific verb (resolve), a specific resource (model id via the models endpoint), and its use case (before inference). This clearly distinguishes it from sibling tools like chat_completions or generate_image, which consume model ids rather than listing them.
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 phrase 'resolve a live model id before inference' tells the agent when to use this tool: ahead of inference calls, to obtain a current id. 'Do not hardcode model ids' adds a concrete usage rule. It does not explicitly name alternatives or state when not to use it, but the purpose is focused enough that this is only a minor gap.
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.
Several video tools are effectively duplicates: generate_video and video_queue both target POST /v1/video/queue, while get_generation_status and video_retrieve both call POST /v1/video/retrieve. The non-video tools are distinct, but these overlapping boundaries make it hard for an agent to choose the correct variant.
Tool names are uniformly snake_case and many follow a verb_noun pattern such as create_key, list_keys, and get_models. However, the video tools use an object-first video_* pattern, and names like agent_me, chat_completions, and funding_instructions break the dominant convention.
At 18 tools, the surface is on the heavy side, and the count is inflated by lower-level variants that duplicate agent-facing tools such as video_queue vs generate_video and video_retrieve vs get_generation_status. A leaner set could consolidate these while still covering account, key, model, image, and video workflows.
The set covers the account/key lifecycle, funding and price controls, model discovery, chat, image generation, and a full video quote/queue/status/retrieve/cleanup flow. It also provides request-trace recovery and capacity checks, so agents have no obvious dead ends for the stated domain.