Skip to main content
Glama
Sign In
Local Model MCP

Local Model MCP

by Doughboyfressh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

read_tensor

Read-only

Inspect tensor statistics and read a bounded flat sample using per-axis [start, stop] ranges, so you can examine checkpoint tensors locally before training or editing.

Instructions

Read tensor statistics and a bounded flat sample. ranges are [start, stop] per axis.

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": {
      "model": "<model>",
      "tensor": "<tensor>"
    },
    "name": "read_tensor"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
modelYes
rangesNo
tensorYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds that results are bounded and that ranges are axis-wise [start, stop] intervals, but says nothing about sample ordering, truncation behavior, or errors for out-of-range axes.

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 tight sentences with the core behavior front-loaded and the range format immediately after. The second sentence is clipped but not wasteful.

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?

Since an output schema exists, return values needn't be described. However, for a 4-parameter tool with zero schema descriptions, the definition leaves key inputs like `limit` and `model` format unexplained.

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 0%, so the description must carry the load, yet it only clarifies `ranges` syntax. `model`, `tensor`, and especially `limit` (default 64, max 1024) get no explanation of meaning or interaction with ranges.

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?

States a specific verb and resource ("Read tensor") plus what comes back: statistics and a bounded flat sample. That distinguishes it from the mutation/job siblings, though no alternative is named.

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 versus inspecting a model or dataset, and no prerequisites such as how `model` is resolved (name vs path). The only implied usage cue is "bounded sample".

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

Deploy Server

Other Tools