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llama-mcp-server

llama-mcp-server

by ahays248
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

llama_lora_set

Adjust the scale factors of LoRA adapters to control their influence on model outputs, with zero scale disabling the adapter.

Instructions

Set LoRA adapter scales

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": {
      "adapters": "<adapters>"
    },
    "name": "llama_lora_set"
  }
}

Input Schema

NameRequiredDescriptionDefault
adaptersYesAdapters to update with new scale values

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

C2.9/5.0
Behavior1/5

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

With no annotations, the description bears full burden but only says 'Set LoRA adapter scales'. It does not disclose behavioral traits such as whether scales are updated in-memory, if previous scales are overwritten, or any side effects.

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 a single sentence, very concise with no unnecessary words. However, it could be more informative without sacrificing brevity.

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 tool mutates state (sets scales) and has a nested parameter, the description is incomplete. It does not mention return values, success indicators, or model state requirements. For a mutation tool, more context is needed.

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 coverage is 100% and the descriptions in the schema ('Adapter ID', 'Scale factor (0 to disable)') are clear. The description adds no extra meaning beyond what the schema provides, meeting the baseline for high coverage.

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 action 'Set' and the resource 'LoRA adapter scales'. It succinctly indicates what the tool does, and among sibling tools like llama_lora_list, it differentiates by focusing on setting rather than listing.

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 is provided on when to use this tool versus alternatives. It does not mention prerequisites (e.g., model must be loaded) or scenarios where adjusting scales is appropriate.

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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