Retrieve running-model identities and engine server details for a configured inference target across vLLM, SGLang, or TGI, so you can verify served models and endpoints.
[READ] Running-model identity + engine server info (engine-agnostic).
vLLM / SGLang report served ids from /v1/models; TGI's single model id comes from /info.
Args: target: Inference target name from config; omit for the default.
| Name | Required | Description | Default |
|---|---|---|---|
| target | No |
Changes observed during successful MCP inspections.
v0.6.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
readOnlyHint=true already declares the safety profile, and the description adds genuine context beyond it: it is engine-agnostic and explains that served ids come from /v1/models on vLLM/SGLang versus /info on TGI. No contradictions; it does not cover return format or pagination, but for a single-target read tool the added backend detail is meaningful.
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?
Front-loads the read tag and core purpose, then spends one sentence on engine-specific sourcing and one short line on the arg. No padding, though the engine-by-engine sentence is arguably more detail than needed for a simple inventory call.
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 one-parameter, no-output-schema read tool this is nearly complete: purpose, safety signal, engine-agnostic behavior, and the target parameter are all covered. Only the shape of the returned inventory is left implicit, which is acceptable for a simple identity lookup.
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%, so the description carries the burden, and it does so well: 'target: Inference target name from config; omit for the default' explains both the meaning of the value and the null/default behavior. This compensates for the bare anyOf string/null schema.
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 names a specific resource (running-model identity + engine server info) and explicitly tags it as a read operation, so an agent knows what it returns. It distinguishes itself from the many sibling engine/metrics tools only implicitly via 'identity ... engine-agnostic' and never names a sibling it is not, such as model_info or engine_health.
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?
Usage is implied by the '[READ]' tag and the nature of an inventory call, and it explains how vLLM/SGLang vs TGI report ids, but there is no explicit 'use when' framing or alternative. With so many adjacent tools (engine_health, model_list, model_info), the lack of routing guidance is a real gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.