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hedging8563

TokenLab MCP Server

by hedging8563
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JavaScript
Remote

Create multimodal embeddings

create_multimodal_embedding

Create multimodal embeddings from text and images for vector-based similarity search and retrieval.

Instructions

Create multimodal embeddings Creates embeddings for multimodal input items. Text input is generally available; image input may require feature enablement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYes
modelYesModel to use for multimodal embeddings
dimensionsNoOptional embedding dimensionality when supported by the selected model

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changedv0.6.4
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
    • removedInput schema / properties / dimensions / maximum
      Removed value: -9007199254740991
    • changedInput schema / properties / input / oneOf
      Previous value: -[
-  {
-    "additionalProperties": false,
-    "description": "A multimodal embedding input item. Text input is generally available; image input may require feature enablement.",
-    "properties": {
-      "image_base64": {
-        "description": "Base64-encoded image content",
-        "type": "string"
-      },
-      "image_mime_type": {
-        "description": "MIME type for image_base64",
-        "type": "string"
-      },
-      "image_url": {
-        "description": "Public image URL to embed",
-        "type": "string"
-      },
-      "text": {
-        "description": "Text to embed",
-        "type": "string"
-      }
-    },
-    "type": "object"
-  },
-  {
-    "items": {
-      "additionalProperties": false,
-      "description": "A multimodal embedding input item. Text input is generally available; image input may require feature enablement.",
-      "properties": {
-        "image_base64": {
-          "description": "Base64-encoded image content",
-          "type": "string"
-        },
-        "image_mime_type": {
-          "description": "MIME type for image_base64",
-          "type": "string"
-        },
-        "image_url": {
-          "description": "Public image URL to embed",
-          "type": "string"
-        },
-        "text": {
-          "description": "Text to embed",
-          "type": "string"
-        }
-      },
-      "type": "object"
-    },
-    "minItems": 1,
-    "type": "array"
-  }
-]New value: +[
+  {
+    "additionalProperties": false,
+    "description": "A multimodal embedding input item. Text input is generally available; image input may require feature enablement.",
+    "properties": {
+      "image_base64": {
+        "description": "Base64-encoded image content",
+        "type": "string"
+      },
+      "image_mime_type": {
+        "description": "MIME type for image_base64",
+        "type": "string"
+      },
+      "image_url": {
+        "description": "Public image URL to embed",
+        "type": "string"
+      },
+      "text": {
+        "description": "Text to embed",
+        "type": "string"
+      }
+    },
+    "type": "object"
+  },
+  {
+    "items": {
+      "description": "A multimodal embedding input item. Text input is generally available; image input may require feature enablement.",
+      "type": "object"
+    },
+    "minItems": 1,
+    "type": "array"
+  }
+]
  2. Addedv0.3.1

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate this is a write operation that is not read-only, not idempotent, and not destructive. The description adds the availability nuance for images, but does not disclose other behaviors such as rate limits, storage of embeddings, or return format. Given annotations, the description provides marginal additional transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences with no redundant or filler content. The first sentence states the purpose, the second adds a critical availability detail. Every word serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description lacks essential context: there is no mention of return values (e.g., embedding vectors or IDs), error handling, or usage limitations. Since there is no output schema, the description should explain what the tool returns. The tool is moderately complex with 3 parameters, and the description fails to cover these aspects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 67%, which is below the 80% threshold, so the description should compensate. The description adds the availability nuance for text vs image input, but does not elaborate on other parameter constraints or formats. The schema already describes most parameter details, so the description provides minimal added meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool creates embeddings for multimodal input items, which distinguishes it from the sibling tool create_embedding (presumably text-only). The verb 'create' and resource 'multimodal embeddings' are specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context on when to use text vs image input, noting potential feature enablement requirements for images. However, it lacks explicit comparisons to sibling tools like create_embedding or guidance on when not to use this tool.

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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