Generate vector embeddings for text inputs using OpenAI-compatible models. Send a string or list of strings to convert them into numerical representations for semantic search, clustering, or similarity analysis.
Call Morpheus OpenAI-compatible embeddings (POST {base}/embeddings).
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | ||
| model | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It reveals the HTTP method and OpenAI compatibility, but omits side effects, authorization requirements, rate limits, or whether this is a read-only inference operation. For a tool with no annotations, this is too thin.
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 with no filler, front-loading the core operation and endpoint. Every word contributes information, making it easy to parse quickly.
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?
The output schema covers return shape, and the two required parameters are simple. However, the absence of annotations and lack of usage guidance leave gaps in safety and tool-selection context. It is minimally adequate for an agent already familiar with embeddings APIs, but not complete on its own.
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 description does not explain the parameters. The names 'Input' and 'Model' are somewhat intuitive, and 'OpenAI-compatible' hints at the expected shape, but the description adds almost no explicit meaning beyond what the bare schema already shows.
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 states a specific operation—calling Morpheus embeddings—and identifies the exact endpoint (POST {base}/embeddings). This distinguishes it from the sibling tools morpheus_chat and morpheus_list_models by resource. It does not elaborate on what an embedding is or returns, but the resource name is clear enough in the ML context.
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 gives no guidance on when to use this tool versus alternatives. It never mentions morpheus_chat or morpheus_list_models, nor does it describe conditions that would favor this tool. The only usage signal is implicit: 'embeddings' implies tasks needing vector representations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.