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jev_modulate_reasoning_effort

Adjusts reasoning effort and stability lease steps per generation, matching provider parameters to cut token waste and delays on mechanical tool calls.

Instructions

Dynamically modulates reasoning effort (low, medium, high, etc.) and stability lease steps for the immediate generation step. Maps exact parameters for OpenAI (GPT-6 Astra/o3), DeepSeek (V4.1-Flash/R1), Qwen (3.8 Max), Anthropic (Claude Fable 5.1), and Gemini (3.8 Thinking). Eliminates reasoning token waste and cuts multi-minute delays on mechanical tool calls.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoOptional model identifier to check for direct non-reasoning compatibility.
contextYesThe command, prompt, or next step to evaluate.
providerNoTarget provider (openai, deepseek, qwen, anthropic, gemini, kimi, mimo). Default: openai.
max_lease_stepsNoOptional upper bound for generation stability lease steps (default: 10).
supported_effortsNoOptional list of supported effort levels (e.g. ['none', 'minimal', 'low', 'medium', 'high', 'xhigh', 'max', 'ultra']).
session_context_tokensNoOptional active prompt tokens in session context to evaluate prompt cache risk.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description must fully disclose behavior. It states that it 'dynamically modulates' and 'maps exact parameters' for multiple providers, and mentions the outcome of reducing waste and delays. However, it does not explain the mechanism, potential side effects, or whether the change is temporary or persistent. The description is informative but lacks depth on what happens during execution.

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?

The description is two sentences, highly concise, and front-loaded with the primary action. It wastes no words and efficiently conveys the tool's core function, provider coverage, and a key benefit. Structure is optimal.

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?

Given the tool has six parameters, full schema coverage, no output schema, and no annotations, the description covers the main purpose and some context (mechanical calls, provider mapping). However, it omits details about when to use this tool vs. siblings, how the tool interacts with the generation pipeline, and what the expected result or return value is. It is adequate but leaves gaps that an agent might need to resolve.

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 has 100% coverage, so all six parameters have individual descriptions. The tool description adds context about provider mapping and effort levels but does not elaborate on any specific parameter beyond what the schema already provides. With full schema coverage, the baseline of 3 is appropriate; the description adds minimal extra value for parameters.

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 clearly states the tool's purpose: it dynamically modulates reasoning effort and stability lease steps for the immediate generation step. It uses specific verbs and a specific resource, and the focus on reasoning effort distinctly separates it from sibling tools like triage, routing, or verification, even without naming them.

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 description implies usage for mechanical tool calls to avoid token waste and delays, which gives some context. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusion criteria or prerequisites. The guidance is inferred rather than direct.

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