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Telnyx MCP Server

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by team-telnyx

create_embeddings

Generate vector embeddings from files in a storage bucket to enable semantic search and AI applications. Specify chunking parameters and embedding models for document processing.

Instructions

Embed a bucket that containe files.

Args:
    bucket_name: Required. Bucket Name. The bucket must exist (string)
    document_chunk_size: Optional. Document Chunk Size (integer)
    document_chunk_overlap_size: Optional. Document Chunk Overlap Size (integer)
    embedding_model: Optional. Supported models (thenlper/gte-large,
    intfloat/multilingual-e5-large, sentence-transformers/all-mpnet-base-v2)
    to vectorize and embed documents.
    loader: Optional. (default, intercom) (string)

Agent should prefer only rely on required fields unless user explicitly
provides values for optional fields.

Returns:
    Dict[str, Any]: Response data containing the embeddings

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions that the bucket must exist (a prerequisite) and lists optional parameters with some details (e.g., supported models), but it doesn't disclose critical behavioral traits such as whether this is a read-only or destructive operation, authentication needs, rate limits, or what happens during processing (e.g., does it modify the bucket?). The return type is mentioned but without specifics on format or errors.

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

Conciseness3/5

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

The description is structured with sections for Args and Returns, which helps organization, but it's moderately verbose with redundant information (e.g., repeating 'Optional' and 'Required'). Sentences like 'Agent should prefer only rely on required fields unless user explicitly provides values for optional fields' could be more concise. Overall, it's front-loaded with the main purpose but includes some unnecessary wording.

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?

Given the complexity (embedding operation with multiple parameters), no annotations, low schema coverage (0%), and no output schema, the description is incomplete. It provides basic parameter info and return type but lacks details on behavior, error handling, output structure, or how it integrates with sibling tools. For a tool with such gaps in structured data, the description should do more to fill in context.

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

Parameters2/5

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

The input schema has 0% description coverage with only one parameter 'request' of type object, which is poorly documented. The description lists 5 parameters (bucket_name, document_chunk_size, document_chunk_overlap_size, embedding_model, loader) that aren't reflected in the schema, adding some semantics like required/optional status and examples for embedding_model. However, this creates a contradiction with the schema, and the description doesn't fully compensate for the schema's lack of coverage, leaving parameter details incomplete.

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

Purpose3/5

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

The description states the tool 'Embed a bucket that containe files' which indicates the general purpose of embedding files from a bucket. However, it's somewhat vague about what 'embed' specifically means (e.g., generating vector embeddings for AI/ML use) and doesn't clearly differentiate from sibling tools like 'embed_url' or 'list_embedded_buckets'. The verb 'embed' is clear but the resource 'bucket that containe files' is imprecise.

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

Usage Guidelines2/5

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

The description provides minimal guidance: it mentions that the agent should prefer required fields unless optional ones are explicitly provided, but this is generic advice rather than specific usage context. It doesn't explain when to use this tool versus alternatives like 'embed_url' or how it relates to other cloud storage tools. No explicit when/when-not scenarios or prerequisites are given.

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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