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ShadovvSinger

Ollama_MCP_Guidance

post_generate_embeddings

Generate numerical embeddings for text using a specified model. Returns JSON-formatted embeddings data or error details.

Instructions

Generate embeddings for the given text using Ollama API.

Args:
    model (str): Model name to use for embeddings generation.
           Example: "nomic-embed-text"
    text (List[str]): List of texts to generate embeddings for.
           Example: ["Text 1"] or ["Text 1", "Text 2"]

Returns:
    str: JSON-formatted string containing:
        - Embeddings data and metadata if successful
        - Error details if the request fails or response format is invalid

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
textYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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 it uses the Ollama API, takes a model and text, and returns JSON with success or error. However, it does not disclose whether the operation is read-only or has side effects, nor does it discuss rate limits or authentication. This is adequate but not comprehensive.

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

Conciseness4/5

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

The description is well-structured with Args and Returns sections, and examples. It is fairly concise, though the return format description is slightly verbose. Every sentence serves a purpose.

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

Completeness4/5

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

Given 2 required parameters, no annotations, and an output schema (though not shown), the description covers the essential inputs, expected output format, and error handling. It is complete enough for an AI agent to use the tool correctly.

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

Parameters5/5

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

The schema description coverage is 0%, so the description must compensate. It does so excellently: for 'model', it gives a type, example, and format; for 'text', it specifies it is a list of strings with examples. This adds significant meaning beyond the bare schema.

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 'Generate embeddings for the given text using Ollama API.' This is a specific verb-resource pair, and with sibling tools like simple_chat and get_ollama_list, it is clearly distinguishable as the embeddings tool.

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 a clear context for using the tool (to generate embeddings) but does not explicitly state when not to use it or mention alternatives. However, the sibling tool names imply distinct purposes, so it's reasonably clear.

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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