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run_end_to_end_workflow

Runs a full autonomous multi-agent optimization workflow—clarification, math modeling, code generation, sandbox execution—only when users explicitly request end-to-end problem solving.

Instructions

Executes the full autonomous multi-agent optimization workflow graph with automated feedback loops (Clarification -> Math Modeling -> Code Generation -> Sandbox Execution). CRITICAL WARNING: This tool should ONLY be used when the user EXPLICITLY requests full autonomous end-to-end workflow execution. Otherwise, use individual modular tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
execution_timeoutNo
max_node_executionsNo
problem_descriptionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that the run is autonomous with automated feedback loops and enumerates the stages, which is meaningful behavioral context. However it says nothing about failure handling, cost/latency implications beyond the timeout parameter name, or what a partial run yields, so it is only partially 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?

Two sentences, the pipeline enumeration is compact, and the critical usage constraint is front-loaded and visually flagged. Slightly dense parenthetical listing, but nothing wasted.

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, and the usage constraint is covered well. But for a heavyweight autonomous multi-step tool with zero annotation coverage and 0% schema description coverage, the absence of any parameter semantics or failure/termination behavior leaves real gaps.

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%, so the description must compensate, yet it never mentions problem_description, execution_timeout, or max_node_executions. The pipeline narrative implies a problem goes in, but the timeout and node-execution caps — the parameters that govern a potentially long autonomous run — are left entirely unexplained.

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 names a specific verb ('Executes') and resource ('full autonomous multi-agent optimization workflow graph') and enumerates the pipeline stages (Clarification -> Math Modeling -> Code Generation -> Sandbox Execution). It also explicitly contrasts itself with the individual modular siblings, so an agent can distinguish this orchestration tool from the per-stage tools without opening a schema.

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

Usage Guidelines5/5

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

Gives an explicit gating condition ('ONLY ... when the user EXPLICITLY requests full autonomous end-to-end workflow execution') plus the fallback route ('Otherwise, use individual modular tools'). This is exactly the when-to-use / when-not-to-use guidance a routing decision needs.

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