Skip to main content
Glama

chat_claude_opus

Запрос к Claude Opus 5.5 (medium) — сильная модель для сложных задач.

    Поддерживает tool_use. Если модель не отдаётся, у аккаунта нет доступа —
    проверьте list_available_llm.

    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": {...}}; при вызове инструмента — "tool_calls": [...].
    

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "messages": "<messages>"
    },
    "name": "chat_claude_opus"
  }
}

Input Schema

NameRequiredDescriptionDefault
toolsNo
systemNo
messagesYes
max_tokensNo
account_tagNo
account_emailNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations only give a generic read/write/open-world profile, and the description adds real context beyond them: tool_use support, the access-failure mode and its remedy (check list_available_llm), and the response shape including tool_calls. It stops short of describing multi-account selection behavior or limits, but it is well above annotation-only coverage.

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?

Front-loaded with the core purpose and capability, then a compact Args/Returns block that is easy to scan. The indentation and docstring-style formatting are slightly verbose but every line carries usable information.

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?

For a six-parameter, open-world chat tool with no output schema, the description covers purpose, tool_use, failure handling, all parameters, and the return shape. Only secondary details (token defaults, account-selection precedence, response truncation behavior) are missing, which an agent could infer or discover at call time.

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 must carry the parameter burden and it largely does, explaining all six: messages format, system prompt, max_tokens, Anthropic-format tools, account_email, and account_tag. Gaps remain — max_tokens has no stated default (32000) or guidance, and account_tag/account_email don't explain selection precedence when both are set.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: a request to the Claude Opus model, plus a positioning phrase ('strong model for complex tasks') that hints at its niche versus lighter siblings. It does not explicitly name chat_claude_sonnet or chat_claude_sonnet_55 as alternatives, so differentiation is inferred from the model name rather than stated.

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?

Usage is only implied by 'strong model for complex tasks' — no explicit when-to-use or when-not guidance relative to the sibling chat tools. It does route the agent to list_available_llm when the model is unavailable, which is a genuinely useful conditional, but that is a troubleshooting path rather than invocation guidance.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.