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Local Model MCP

Local Model MCP

by Doughboyfressh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

preview_weight_edits

Read-only

Simulate weight edits on a local model to preview statistics and receive a token bound to the modified weights, assets, and edits before applying changes.

Instructions

Simulate edits and return statistics and a token bound to weights, assets, and edits.

value means scalar for scale/add/set, or standard deviation for noise. low_rank applies W += alpha * (left @ right). Ranges use exclusive stops; omit for the whole tensor. Tensor asset shapes must match exactly. This reads each affected shard into CPU memory.

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": {
      "request": "<request>"
    },
    "name": "preview_weight_edits"
  }
}

Input Schema

NameRequiredDescriptionDefault
requestYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavior beyond that: it reads each affected shard into CPU memory (a cost/latency warning) and returns a token bound to weights, assets, and edits. It stops short of saying whether the token expires or is invalidated by intervening changes, which is the key behavioral question for a preview tool.

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?

Front-loaded with the outcome (simulate, return statistics and token), then dense semantic notes in short sentences. Every line carries information; the only nit is that the operation-specific notes read as a fragment list without headings.

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?

An output schema exists, so return values need no prose, and the description covers the non-obvious computation semantics an agent cannot derive from the schema. Remaining gaps are token lifetime and the preview-then-apply handoff, which matter for a 128-edit nested request but are secondary to correctness of the edit spec.

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 0% and the nested WeightEdit object has roughly a dozen fields, so the description must carry the load. It does explain the two hardest semantics well: what "value" means per operation and the low_rank formula W += alpha * (left @ right), plus exclusive-stop ranges and exact shape matching. However, seed, minimum/maximum, source_tensor, asset, and the 128-edit cap are left entirely to the bare schema.

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?

"Simulate edits and return statistics and a token" names a specific verb (simulate) and specific outputs, making clear this is a dry-run counterpart to a write operation. It is distinguishable from apply_weight_edits by the word "simulate" alone, though it never names that sibling explicitly.

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 word "simulate" plus "return ... a token" implies the intended workflow (preview first, then commit with the token), but the description never states when to use this versus apply_weight_edits, nor that the token is required for the commit step. Usage is inferable rather than stated.

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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