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llama-mcp-server

llama-mcp-server

by ahays248
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

llama_embed

Generate vector embeddings from text for semantic search, clustering, and similarity comparisons using local llama.cpp.

Instructions

Generate embeddings for text

Tool call template

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": {
      "content": "<content>"
    },
    "name": "llama_embed"
  }
}

Input Schema

NameRequiredDescriptionDefault
contentYesText to embed

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as model used, input length limits, or output format. The description is too minimal to inform the agent about important behaviors.

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 very concise at two sentences, with no wasted words. However, it lacks structure or additional details that would improve its utility.

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 absence of output schema and annotations, the description should provide more context about the embedding generation, such as embedding dimensions or model specifics. It is incomplete for an agent to use effectively.

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?

Schema coverage is 100% for the single parameter 'content', and its description is 'Text to embed'. The tool description adds no additional meaning beyond what the schema already provides, so it falls short of the baseline of 3.

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

Purpose4/5

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

The description 'Generate embeddings for text' clearly states the verb (generate) and resource (embeddings). It distinguishes from sibling tools like llama_chat or llama_complete, which are for different tasks. However, it could be more specific about the type or purpose of embeddings.

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?

No guidance on when to use this tool versus alternatives. The agent must infer that embeddings are used for similarity search or classification, but no explicit context is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

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