Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Model Council MCP Server Run through model council (usa): should I add a subscription to my product?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-name: io.github.ivanantigravity-lgtm/model-council-mcp-server
MCP сервер, который прогоняет одну и ту же задачу через 3 модели параллельно и возвращает Claude их короткие ответы. Claude сам сверху делает итоговую выжимку.
Два пресета:
china — Moonshot Kimi, Qwen, DeepSeek
usa — Gemini, Grok, OpenAI
Полезно, когда:
задача неоднозначная
есть риск самоуверенного ответа одной модели
важны слабые места, возражения, trade-offs
нужно 3 разные перспективы, а не один ответ
Не надо вызывать для:
простого факта
быстрой суммаризации
задачи, которую решит одна нормальная модель
Related MCP server: AI Council MCP Server
Claude Desktop или Claude Code
Python 3.11+
POLZA_AI_API_KEY — ключ берётся на polza.ai/dashboard/api-keys
Поставить uv:
curl -LsSf https://astral.sh/uv/install.sh | shСоздай .mcp.json в корне проекта:
{
"mcpServers": {
"model-council": {
"command": "uvx",
"args": ["model-council-mcp-server@latest"],
"env": {
"POLZA_AI_API_KEY": "your-polza-api-key-here"
}
}
}
}Перезапусти Claude Code.
Файл ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"model-council": {
"command": "uvx",
"args": ["model-council-mcp-server@latest"],
"env": {
"POLZA_AI_API_KEY": "your-polza-api-key-here"
}
}
}
}Файл: %APPDATA%\Claude\claude_desktop_config.json. Содержимое идентичное.
После перезапуска Claude попроси:
Прогони через model council (usa) задачу: стоит ли мне добавить подписку в мой продукт?
Claude должен вызвать tool tri_model_scan и вернуть 3 коротких ответа.
tri_model_scan — 3 модели отвечают на одну задачу
tri_model_compare — сравнение нескольких вариантов
tri_model_red_team — атака на идею, план или оффер
council_model_guide — краткая памятка по сильным и слабым сторонам моделей
У каждого tool есть параметр preset (china или usa).
chinamoonshotai/kimi-k2.5
qwen/qwen3.6-plus
deepseek/deepseek-v3.2
usagoogle/gemini-3.1-flash-lite-preview
x-ai/grok-4.1-fast
openai/gpt-5.4-nano
Проверить актуальность ID моделей можно через GET https://polza.ai/api/v1/models/catalog. Если какая-то модель у Polza переименована — подставь свой ID через переменные окружения ниже.
Переменная | Обязательная | По умолчанию |
| да | — |
| нет |
|
| нет |
|
| нет |
|
| нет |
|
| нет |
|
| нет |
|
| нет |
|
| нет |
|
Компактный JSON:
задача
контекст
3 сырых коротких ответа от моделей
источники по каждой модели
Сервер заставляет модели отвечать коротко, без воды и без повтора вопроса — чтобы не раздувать контекст Claude.
git clone https://github.com/ivanantigravity-lgtm/model-council-mcp-server.git
cd model-council-mcp-server
uv sync
POLZA_AI_API_KEY=your_key uv run python -m model_council_mcp_server.serverMIT.
Return a short guide on the strengths, weaknesses, and best usage of Grok, Gemini, and DeepSeek.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided. The description states it 'returns a short guide', which is minimal behavioral disclosure. It does not mention any side effects or constraints, but for a read-only informational tool, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and the presence of an output schema, the description sufficiently explains the tool's purpose. It could be more specific about the output format, but the output schema likely handles that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so the description does not need to explain parameter usage. This is a baseline 4 for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool returns a guide on strengths, weaknesses, and best usage of three specific AI models. This clearly differentiates it from sibling tools like tri_model_scan, tri_model_compare, and tri_model_red_team.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a user needs a guide on these models, but it does not provide explicit guidance on when to choose this tool over siblings or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare several options through three different model families.
| Name | Required | Description | Default |
|---|---|---|---|
| preset | No | china | |
| context | No | ||
| options | Yes | ||
| objective | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden for transparency. Only states 'compare several options' without indicating whether operations are read-only, destructive, or require specific permissions. Behavioral traits like side effects or limitations are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is a single sentence, which is concise but lacks structure. No bullet points or front-loading of key information. Could be improved by adding context or purpose without increasing verbosity significantly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 4 parameters, 2 required, and an output schema, the description is insufficient. It does not explain what 'three different model families' are, how comparison works, or what the output contains. Even though output schema exists, the description should clarify the tool's function beyond the minimal statement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and description adds no meaning to parameters. Parameter names ('objective', 'options', 'preset', 'context') are not elaborated. The description fails to clarify their roles or expected formats.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states verb 'compare' and resource 'options through model families', which provides basic purpose. However, it lacks specificity on the type of comparison or output, and does not clearly differentiate from sibling tools like 'council_model_guide' or 'tri_model_scan'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. No mention of contexts, prerequisites, or when not to use it. Sibling tools exist but no differentiation provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Stress-test a plan through three different model families.
| Name | Required | Description | Default |
|---|---|---|---|
| plan | Yes | ||
| preset | No | china | |
| context | No | ||
| success_criteria | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It only states 'Stress-test' without explaining what that entails (e.g., does it generate adversarial inputs? check vulnerabilities?). No disclosure of side effects, permissions, or destructive potential.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (one sentence, 9 words). It is front-loaded with the key action and resource. However, it could be slightly expanded to cover more context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, 0% schema coverage, no annotations, and an output schema (presumably covering return values), the description is minimal. It lacks explanation of output, usage conditions, and differentiation from siblings, making it incomplete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description only mentions 'plan'. The meaning of 'preset', 'context', and 'success_criteria' is left entirely to inference from their names, with no additional context provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Stress-test'), the resource ('a plan'), and the method ('through three different model families'). It distinguishes from siblings like 'tri_model_scan' or 'tri_model_compare' by implying adversarial testing, though this could be more explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines provided. The description does not specify when to use this tool versus alternatives like 'tri_model_scan' or 'tri_model_compare', nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run the same task through Grok, Gemini, and DeepSeek once each and return compact JSON with raw model answers.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| preset | No | china | |
| context | No | ||
| format_hint | No | verdict, 3 key points, 2 risks, recommendation |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only states that the tool runs the task once per model and returns JSON. It does not mention side effects, permissions, rate limits, or whether the operation is read-only. The description lacks important behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, effectively concise and front-loaded with the core purpose. However, it sacrifices necessary detail for brevity, bordering on under-specification.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no schema descriptions, no annotations, and an output schema (not shown), the description is incomplete. It fails to explain parameter roles or expected behavior, leaving significant gaps for an AI agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description explains none of the four parameters. It mentions 'task' implicitly but offers no semantics for 'preset', 'context', or 'format_hint'. The description adds minimal value beyond the schema's property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'Run' and specifies the resource: 'the same task through Grok, Gemini, and DeepSeek once each'. It also mentions the output format 'compact JSON with raw model answers'. The purpose is distinct from sibling tools like council_model_guide, tri_model_compare, and tri_model_red_team, which suggest different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus its siblings, nor does it mention prerequisites or limitations. The brief statement only implies use when wanting raw answers from these three models, but no explicit alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.2council_model_guidetri_model_comparetri_model_red_teamtri_model_scanEach tool has a distinct purpose: guide provides overview, scan runs a single task, compare evaluates options, and red_team stress-tests plans. No overlap in functionality.
Tools follow a pattern of prefix (council_ or tri_model_) plus action (guide as noun, scan/compare/red_team as verbs). Slight inconsistency because 'guide' is a noun while others are verbs, but overall clear and predictable.
Four tools cover the core use cases for model comparison and evaluation without being excessive. The number is well-scoped for the server's purpose.
Covers guidance, raw outputs, comparison, and stress-testing. Minor gap: no tool for detailed individual model capabilities beyond the guide. Still adequately complete for the domain.
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