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Bernstein - Multi-agent orchestration

Bernstein - Multi-agent orchestration

by sipyourdrink-ltd
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

bernstein_task_capsule

Inspect a task capsule's journal and audit evidence to validate its state, creating the install audit key during verification if missing.

Instructions

Read a task capsule together with its local journal and audit evidence. With verify=true, verification may create the install audit key if it is absent. Host effects: reads files; writes files.

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": {
      "task_id": "<task_id>"
    },
    "name": "bernstein_task_capsule"
  }
}

Input Schema

NameRequiredDescriptionDefault
verifyNo
task_idYes
workdirNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.15.0
    • addedInput schema / description
      Added value: +"Read a task capsule together with its local journal and audit evidence. With verify=true, verification may create the install audit key if it is absent. Host effects: reads files; writes files."
  2. Addedv3.11.0

TDQS

A3.9/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden of disclosure. It explicitly mentions conditional side effects (verify=true may create install audit key) and host effects ('reads files; writes files'), which is unusually transparent for a tool named 'read'.

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?

Three short sentences, front-loaded with the primary purpose. The side-effect disclosure is compact and informative; no filler or redundant content.

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 not be described. The description covers the core action, conditional mutation, and host effects, but lacks usage context around workdir and when to prefer sibling status tools.

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 property descriptions are absent (0% coverage), so the description must compensate. It explains the verify parameter's conditional side effect, but task_id and workdir receive no semantic explanation beyond their names and schema types.

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?

Description states a specific action ('Read a task capsule...') with a clear resource (task capsule, local journal, audit evidence). It distinguishes from siblings like bernstein_run and bernstein_status by focusing on reading capsule contents rather than executing or checking status.

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 explicit guidance on when to use this tool versus alternatives such as bernstein_status or bernstein_run_status. The description implies a read/inspection use case but does not state exclusions or preferred alternatives.

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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