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pin_model_digest

Destructive

Pin a model's expected provenance digest to detect drift and flag when its integrity changes. Use it to enforce trusted model state in local LLM governance.

Instructions

[WRITE][risk=medium] Pin a model's expected provenance digest (drift detection).

Args: model: Model name. digest: The expected digest to pin (from list_models / model_details).

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>",
      "digest": "<digest>"
    },
    "name": "pin_model_digest"
  }
}

Input Schema

Name	Required	Description	Default
`model`	Yes
`digest`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.0Jul 29, 2026

TDQS

A3.5/5.0

Behavior3/5

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

The [WRITE][risk=medium] tag adds context beyond the annotations, and the digest-source pointer is genuinely helpful. But with destructiveHint=true declared, the description never says whether pinning overwrites an existing pinned digest or whether the change is reversible via undo_apply — meaningful gaps for a destructive write.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Very compact: one summary line plus two short arg lines, with the risk/write markers front-loaded. No wasted sentences.

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?

For a two-parameter destructive write with no output schema and no annotation-adjacent detail, the definition covers the essentials but omits overwrite semantics, reversibility, and any confirmation behavior — all relevant to a mutation tool.

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%, so the description must carry the load. It does define both parameters minimally ('model: Model name', 'digest: The expected digest to pin (from list_models / model_details)'), including the provenance of the digest value, but adds no format, validation, or constraint detail.

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: 'Pin a model's expected provenance digest', with the parenthetical '(drift detection)' explaining why. It is distinguishable from siblings like model_provenance or list_models, though it never explicitly contrasts itself with them.

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 description implies when to use it (drift detection) and usefully points to where the digest value comes from (list_models / model_details). However it gives no when-not-to-use guidance and does not mention the related undo_apply tool that could reverse a pin.

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