Turn text into a 768-dimension embedding vector for search, clustering or similarity.
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"text": "<text>"
},
"name": "embed"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to embed (max 4000 characters) |
Changes observed during successful MCP inspections.
Input schema / properties / textAdded value: +{
+ "description": "Text to embed (max 4000 characters)",
+ "type": "string"
+}Input schema / requiredAdded value: +[
+ "text"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full burden, and it does disclose the key behavioral fact an agent needs: the output is a 768-dimension vector. It does not say whether embedding is deterministic, which model produces it, whether the 4000-character cap silently truncates or errors, or that it is a side-effect-free computation.
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?
A single front-loaded sentence with no filler; the verb, output shape, and intended uses all land in one pass.
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?
There is no output schema, and the description compensates by naming the return format (768-dimension vector). For a single-parameter, side-effect-free utility that is nearly sufficient, though the absence of any note on truncation, model choice, or non-text handling leaves a small gap.
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?
Only one parameter exists and schema description coverage is 100%, with the schema already stating 'Text to embed (max 4000 characters)'. The description adds no additional meaning about the input beyond what the schema provides, so the baseline of 3 applies.
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?
States a specific verb+resource pair ('Turn text into a ... embedding vector') and pins down the output shape as 768-dimension, which an agent can use immediately. It does not explicitly distinguish itself from text-similarity siblings like word_similarity or analyze_sentiment, so it stops short of a 5.
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 'for search, clustering or similarity' implies the downstream use cases but never says when to pick this tool over a sibling (e.g. word_similarity, compare, search) or what inputs are inappropriate. Usage is inferable rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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