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Glama

start_llamacpp

Launches a local llama-server instance using an absolute .gguf model path, restricted to 127.0.0.1:8080. Subsequent calls wait for approval.

Instructions

Start llama-server on this computer (127.0.0.1:8080 only) with an absolute .gguf path. Extra calls wait for Late Approve.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_pathYesAbsolute path to a .gguf file on this computer

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.5

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses meaningful traits: the server binds only to 127.0.0.1:8080, and extra calls wait for 'Late Approve'. However, the 'Late Approve' mechanism is left cryptic, and there is no mention of whether the call blocks, returns a status, or handles already-running servers.

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?

Two sentences, front-loaded with the action and constraints, followed by the notable queuing behavior. No filler or redundancy, though the unexplained 'Late Approve' term slightly reduces clarity.

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

Completeness3/5

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

The tool is simple (one required parameter), and the description covers the core constraints: localhost-only binding and the approval-wait behavior. Yet it leaves ambiguities such as what 'Late Approve' means, whether the call returns before the server is ready, and how errors like port conflicts are handled. Given no annotations and no output schema, a bit more context would improve completeness.

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 100%, so the schema already documents model_path as an absolute path to a .gguf file. The description's mention of 'absolute .gguf path' adds no meaningful semantic value beyond the schema, so the baseline of 3 applies.

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 uses a specific verb ('Start'), names the resource ('llama-server'), and adds location/port constraints ('127.0.0.1:8080 only') plus the required input type ('absolute .gguf path'). This clearly distinguishes it from sibling tools like start_ollama, start_vllm, and start_late_infer.

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

Usage Guidelines3/5

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

The context is clear: use this to start llama-server locally with a .gguf file. However, it does not explicitly state when to prefer this over sibling start_* tools, mention alternatives, or explain when not to use it. Usage guidance is therefore implied rather than explicit.

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