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relayvault_commit_checkpoint

Save a deterministic, zero-trust task checkpoint before switching AI models or hitting rate limits, with secrets scrubbed into local vault tokens for safe handoff.

Instructions

Save a deterministic, zero-trust task checkpoint before switching AI models or hitting a rate limit. Automatically scrubs API keys, credentials, emails, and private IPs into local <RV_VAULT:...> tokens.

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": {
      "workspace_id": "<workspace_id>",
      "source_model": "<source_model>",
      "objective": "<objective>",
      "locked_decisions": "<locked_decisions>",
      "failed_approaches": "<failed_approaches>",
      "artifacts": "<artifacts>",
      "next_steps": "<next_steps>"
    },
    "name": "relayvault_commit_checkpoint"
  }
}

Input Schema

NameRequiredDescriptionDefault
branchNomain
artifactsYes
objectiveYes
next_stepsYes
parent_hashNo
source_modelYes
workspace_idYes
locked_decisionsYes
failed_approachesYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the significant behavioral trait that secrets are scrubbed into <RV_VAULT:...> tokens. However, it says nothing about whether commit is idempotent, how parent_hash chaining works, whether a commit can overwrite or fork history via branch, or what permissions are needed. The scrubbing disclosure is valuable but leaves the mutation's side effects largely unexplained.

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?

Two sentences, no filler, with the action and the trigger condition front-loaded before the scrubbing detail. Every clause carries information an agent needs.

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?

An output schema exists, so return values need not be described. But for a 9-parameter, 7-required mutation with nested objects and no annotations, two sentences is thin: the description never explains the parameters or the checkpoint's consistency model. An agent could call it, but likely with malformed argument semantics.

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 description coverage is 0% across 9 parameters, so the description must compensate and it does not — none of workspace_id, branch, parent_hash, source_model, locked_decisions, failed_approaches, artifacts, or next_steps is explained. The critical chaining semantics of parent_hash and the expected shape of the nested artifacts object are left entirely undocumented. Only a vague hint that vault tokens appear somewhere in the payload is implied.

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 — 'Save a deterministic, zero-trust task checkpoint' — which an agent can easily distinguish from relayvault_checkout_handoff and relayvault_list_history. It stops short of naming the siblings or explicitly contrasting the operations, so differentiation relies on the reader's inference.

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

Usage Guidelines4/5

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

Gives clear timing guidance: use it 'before switching AI models or hitting a rate limit.' That is a concrete trigger condition. It offers no when-not guidance, no mention of the sibling tools as alternatives, and no prerequisites such as whether a workspace must exist first.

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