Allows interaction with Perplexity AI models (Sonar, Sonar Pro, Sonar Reasoning, Sonar Deep Research) via Polza.ai, enabling question answering, deep research, and model listing.
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., "@perplexity-polza-mcp-server research the latest trends in quantum computing"
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/perplexity-polza-mcp-server
MCP сервер для Claude Desktop и Claude Code, который ходит в модели Perplexity (Sonar, Sonar Pro, Sonar Reasoning, Sonar Deep Research) через агрегатор Polza.ai.
perplexity_model_guide — шпаргалка по выбору модели под задачу
perplexity_ask — обычный вопрос в Perplexity
perplexity_research — более глубокий ресёрч с веб-поиском
list_perplexity_models — список доступных perplexity/* моделей из каталога Polza
Related MCP server: Perplexity Agent MCP
Claude Desktop или Claude Code (или любой другой MCP-клиент)
uv (ставится одной командой, см. ниже)
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 | shЭто самый простой путь: ничего клонировать не надо, uvx сам скачает пакет из PyPI.
Создай файл .mcp.json в корне своего проекта:
{
"mcpServers": {
"perplexity-polza": {
"command": "uvx",
"args": ["perplexity-polza-mcp-server@latest"],
"env": {
"POLZA_AI_API_KEY": "your-polza-api-key-here"
}
}
}
}Перезапусти Claude Code — готово.
Открой файл ~/Library/Application Support/Claude/claude_desktop_config.json и добавь:
{
"mcpServers": {
"perplexity-polza": {
"command": "uvx",
"args": ["perplexity-polza-mcp-server@latest"],
"env": {
"POLZA_AI_API_KEY": "your-polza-api-key-here"
}
}
}
}Перезапусти Claude Desktop.
Файл: %APPDATA%\Claude\claude_desktop_config.json. Содержимое такое же, как на macOS.
После перезапуска Claude попроси:
Покажи доступные модели Perplexity через polza
Claude должен вызвать tool list_perplexity_models и вернуть список.
Sonar — быстрый поиск + ответ. Новости, факты, Q&A, короткие суммаризации.
Sonar Pro — плотнее структура, сравнения, follow-up вопросы.
Sonar Pro Search — глубже поиск, больше поисковых шагов.
Sonar Reasoning Pro — не просто найти, а разобрать и сделать вывод.
Sonar Deep Research — полноценный ресёрч, market scan, длинный отчёт.
Переключить дефолтную модель можно через переменные окружения PERPLEXITY_MODEL и PERPLEXITY_RESEARCH_MODEL — полный список ниже.
Переменная | Обязательная | По умолчанию | Описание |
| да | — | Ключ с polza.ai |
| нет |
| Base URL для chat completions (с |
| нет |
| Модель для |
| нет |
| Модель для |
| нет |
| — |
git clone https://github.com/ivanantigravity-lgtm/perplexity-polza-mcp-server.git
cd perplexity-polza-mcp-server
uv sync
POLZA_AI_API_KEY=your_key uv run python -m perplexity_polza_mcp_server.serverДля локального подключения из source в Claude:
{
"mcpServers": {
"perplexity-polza-local": {
"command": "uv",
"args": ["run", "python", "-m", "perplexity_polza_mcp_server.server"],
"cwd": "/absolute/path/to/perplexity-polza-mcp-server",
"env": {
"POLZA_AI_API_KEY": "your-polza-api-key-here"
}
}
}
}Chat completions: POST https://polza.ai/api/v1/chat/completions (OpenAI-совместимый формат)
Model catalog: GET https://polza.ai/api/v1/models/catalog
pyproject.toml — метаданные пакета и entry points
server.json — описание для MCP Registry
fastmcp.json — локальная конфигурация FastMCP
.github/workflows/publish-pypi.yml — публикация в PyPI через GitHub Actions
MIT.
| Name | Required | Description | Default |
|---|---|---|---|
| include_providers | No | Include provider details from Polza catalog. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | User question or task for Perplexity. | |
| model | No | Polza model id. Default: perplexity/sonar | perplexity/sonar |
| system | No | Optional system instruction. | |
| temperature | No | Sampling temperature. | |
| max_tokens | No | Maximum completion tokens. | |
| search_context_size | No | Perplexity web search depth. | |
| reasoning_effort | No | Reasoning effort for models that support it. | |
| include_reasoning | No | Ask the provider to include reasoning in the response when supported. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Research topic or question. | |
| model | No | Research-focused Perplexity model. | perplexity/sonar-deep-research |
| system | No | Optional system instruction. | |
| max_tokens | No | Maximum completion tokens. | |
| search_context_size | No | Search depth for research. | high |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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.
v0.1.0list_perplexity_modelsperplexity_askperplexity_researchTool names suggest distinct purposes: listing models, asking a question, and doing research. However, the lack of descriptions creates some ambiguity about each tool's exact scope.
Naming is inconsistent: one tool uses a verb_noun pattern (list_perplexity_models), while the other two use a brand prefix followed by a verb (perplexity_ask, perplexity_research). The convention is not uniform.
Three tools is on the lower side but could be sufficient for a focused server (e.g., listing models, asking questions, researching). However, the lack of descriptions makes the scope feel underdeveloped.
The tool set covers basic interactions with Perplexity (list models, ask, research), but without descriptions it's unclear if common operations like context management or follow-up questions are supported. The surface appears minimal but possibly complete for simple use cases.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Real-time web search, reasoning, and research through Perplexity's API
Enable AI assistants to perform web searches using Perplexity's Sonar Pro.
Provides AI assistants with access to Seltz's powerful Web Search capabilities.
Web search, scraping, Google Trends and data lookups. Paid per call in USDC on Base via x402.
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