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algernon

by sammyboi81
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Python
Remote

algernon_dispatch

Dispatch tightly-scoped tasks to run concurrently on a fleet of cheap LLM workers, collecting every result while keeping the main model context-light and token-efficient.

Instructions

Run N tightly-scoped tasks CONCURRENTLY on a fleet of cheap workers and collect every result. Stay free to think while the fleet works — N tight tasks in parallel beat one bloated serial prompt. Each worker runs on YOUR LLM key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNooptional worker model override
tasks_jsonYesJSON array of {"id": str, "prompt": str} tasks
max_parallelNohow many workers run at once

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It explicitly discloses concurrent execution, result collection, and that each worker runs on the user's LLM key, which implies cost/credential usage. It does not cover failure handling or side effects, but the core dispatch behavior is transparent.

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?

Three sentences with front-loaded action and useful cost context. The 'stay free to think' phrasing is somewhat promotional and overlaps with the concurrency claim, but the description remains compact.

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?

Inputs are well covered by the schema and the description states that results are collected, but there is no output schema and the description does not specify the result format, mapping of task IDs to outputs, or failure behavior. This is adequate but leaves gaps for an agent to infer.

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?

The schema already covers 100% of parameters with clear descriptions, so the baseline is 3. The description reinforces 'tightly-scoped tasks' and 'N' but adds no syntax or format details beyond the schema.

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?

The description opens with a precise action: run N tightly-scoped tasks concurrently and collect every result. It positions the tool against a bloated serial prompt and the sibling orchestration/planning tools by focusing on parallel execution of small independent tasks.

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?

It provides clear context: use this when you have N tightly-scoped tasks that can run in parallel, and it contrasts this with a single serial prompt. It does not explicitly name alternatives or state when not to use the tool, so it stops short of a 5.

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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