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by io.github.molkex
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chat_gemini_flash

Быстрый и дешёвый Gemini 3 Flash — для коротких запросов, классификации, извлечения полей из текста. Идёт в вашу подписку Google, наши токены не тратит.

    Args:
        messages: список {"role": "user|assistant", "content": "..."}.
        system: системный промпт (опционально).
        max_tokens: максимальное количество токенов в ответе (по умолчанию 65536 — максимум модели,
            допустимо 1..65536). ВАЖНО: токены «размышления» Gemini 3 входят в этот
            лимит — при малом значении ответ обрывается (finish_reason "length").
        thinking_budget: бюджет токенов на размышление (необязательно): 0 — выключить
            (если модель позволяет), -1 — динамически, N — верхняя граница.
        thinking_level: уровень размышления (необязательно): "low" | "high"
            (у Flash ещё "minimal" / "medium"). Приоритетнее thinking_budget.
        account_email: форсировать конкретный Google-аккаунт.
        account_tag: форсировать аккаунты с указанным тэгом.

    Returns:
        {"text": str, "usage": {"input_tokens": int, "output_tokens": int}}.

    Raises:
        MCPError: если ключ невалиден, Google не подключён, или квота исчерпана.

Input Schema

NameRequiredDescriptionDefault
systemNo
messagesYes
max_tokensNo
account_tagNo
account_emailNo
thinking_levelNo
thinking_budgetNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / max_tokens
      Added value: +{
+  "default": 65536,
+  "title": "Max Tokens",
+  "type": "integer"
+}
    • addedInput schema / properties / thinking_budget
      Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Thinking Budget"
+}
    • addedInput schema / properties / thinking_level
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Thinking Level"
+}
  2. Changed2 schema fields changed
    • addedInput schema / properties / account_email
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Account Email"
+}
    • addedInput schema / properties / account_tag
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Account Tag"
+}
  3. Added

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations, the description discloses important behavior: calls go through the user's Google subscription rather than consuming platform tokens, quota/connection failures raise MCPError, and Gemini thinking tokens count toward max_tokens causing truncation. This adds meaningful operational context not present in the structured annotations.

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 purpose is front-loaded, followed by structured Args, Returns, and Raises sections. Although detailed, every sentence carries useful semantics for a seven-parameter LLM call, and there is no filler.

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

Completeness5/5

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

The description supplies return shape, error conditions, account selection, token-limit behavior, and thinking controls. Since there is no output schema, the explicit Returns section is especially valuable and makes the definition complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With schema description coverage at 0%, the description fully documents all seven parameters: messages format, optional system prompt, max_tokens default/range and truncation caveat, thinking_budget values, thinking_level values and priority, and account forcing via email or tag. It compensates completely for the bare 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 names the specific model (Gemini 3 Flash) and concrete use cases (short queries, classification, field extraction), which clearly distinguishes it from siblings such as chat_gemini_pro and chat_claude_sonnet. An agent can identify both what the tool does and where it fits.

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 gives clear use-case guidance (short requests, classification, extraction) and notes the cost/billing advantage, but it does not explicitly state when not to use it or name preferred alternatives for longer or more complex tasks. The context is clear, but sibling routing is only implicit.

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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