Skip to main content
Glama

openFlow

by io.github.molkex
OverviewDiscussionsAdmin

chat_claude_sonnet

Запрос к Claude Sonnet 4.6 через вашу подписку Anthropic (если подключена) или через подписку Google (Vertex). Поддерживает инструменты (tool_use).

    Args:
        messages: список {"role": "user|assistant", "content": "..."}.
        system: системный промпт (опционально).
        max_tokens: максимальное количество токенов в ответе.
        tools: описание инструментов в формате Anthropic tool_use (опционально).
        account_email: форсировать конкретный Google-аккаунт.
        account_tag: форсировать аккаунты с указанным тэгом.

    Returns:
        {"text": str, "usage": {...}}. Если модель решила звать инструмент,
        дополнительно вернётся "tool_calls": [...].

    Raises:
        MCPError: если ключ невалиден, подписка не подключена, или квота исчерпана.

Input Schema

NameRequiredDescriptionDefault
toolsNo
systemNo
messagesYes
max_tokensNo
account_tagNo
account_emailNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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"
+}
  2. Added

TDQS

A4.1/5.0
Behavior4/5

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

Beyond the annotations, the description discloses that the tool routes through Anthropic or Google Vertex subscriptions, surfaces 'tool_calls' when the model elects to invoke tools, and enumerates failure conditions (invalid key, subscription not connected, quota exhausted). These auth and error specifics add real value over the annotation set.

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 Args/Returns/Raises structure is front-loaded and easy to scan, with the core purpose in the opening sentence. It is slightly verbose, but every section carries distinct information, so little is wasted.

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

Completeness4/5

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

With no output schema, the description compensates by specifying the return shape ({text, usage}, plus tool_calls) and the error surface. Combined with the auth/subscription context, it is nearly complete for a chat tool; only explicit selection guidance versus the Gemini siblings is missing.

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

Parameters4/5

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

Schema description coverage is 0%, so the description carries the full burden, and it documents all six parameters (messages, system, max_tokens, tools, account_email, account_tag) with meaning and optionality. It notably clarifies the Anthropic tool_use format for 'tools' and the filter semantics of account_email/account_tag, which the schema does not.

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?

States a specific verb and resource ('Запрос к Claude Sonnet 4.6' — a chat request to the Claude Sonnet model) and names the exact model, which distinguishes it from siblings like chat_gemini_flash and chat_gemini_pro. An agent can identify the tool's action and target without opening the schema.

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?

It notes the tool requires an Anthropic subscription (or falls back to Google/Vertex) and supports tool_use, which frames usage, but it never states explicitly when to choose this over the sibling chat_gemini_* tools. The routing guidance is only implied by the model name, so it sits at minimum-viable.

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

All openFlow toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources