sentarion-mcp

dispatch_with_dependencies

Run tasks in dependency-ordered waves, fanning out to parallel agents and fanning in results, with cost estimation and policy governance.

Instructions

Run tasks in dependency-ordered waves via Algernon (fan-out/fan-in).

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": {
      "tasks_json": "<tasks_json>"
    },
    "name": "dispatch_with_dependencies"
  }
}

Input Schema

Name	Required	Description	Default
`tasks_json`	Yes	JSON array of {id, prompt, depends_on: [ids]}

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Aug 31, 2026

TDQS

B3.1/5.0

Behavior2/5

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 behavioral disclosure. It only mentions dependency-ordered waves and fan-out/fan-in, but says nothing about side effects, return behavior, error handling, permissions, or what 'dispatch' actually entails. This is a meaningful transparency gap.

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?

The description is a single tight sentence with the core operation front-loaded. It loses a point because 'Algernon' is unexplained jargon, making the sentence not fully self-contained for an unfamiliar agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has only one parameter and no nested objects, so complexity is low. But with no output schema and no annotations, the description should explain what the tool returns, whether it waits for completion, and how it differs from dispatch-adjacent sibling tools; it does none of this.

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 100%, since the schema already explains tasks_json as a JSON array of {id, prompt, depends_on: [ids]}. The description adds no additional parameter-level meaning, so the baseline of 3 is appropriate.

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?

The description uses a specific verb ('Run') and clearly identifies the resource ('tasks in dependency-ordered waves via Algernon') with a distinctive fan-out/fan-in mechanism. It does not explicitly differentiate from sibling tools such as orchestrate_and_record or govern, so it stops short of a 5.

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 phrase 'dependency-ordered waves' and 'fan-out/fan-in' imply this tool is for tasks that have dependencies and should be executed in waves. However, it gives no explicit when-to-use or when-not-to-use guidance and does not mention alternatives among the listed siblings.

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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