Click on "Deploy 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., "@LangChain & LlamaIndex Coding Assistant show me the latest OpenAI chat completions API parameters"
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.
A powerful Coding Assistant integrated into Claude Code via the Model Context Protocol (MCP).
This assistant specializes in fetching real-time, official documentation for LangChain, LlamaIndex, and OpenAI, ensuring your coding workflow in Claude is powered by the absolute latest API references and guides.
Deep Documentation Search: Instantly retrieves detailed documentation from:
🦜️🔗 LangChain (python.langchain.com/docs)
🦙 LlamaIndex (docs.llamaindex.ai)
🤖 OpenAI (platform.openai.com/docs)
Context-Aware Coding: Provides Claude with the exact context needed to write accurate code using these rapidly evolving libraries.
Seamless Integration: Designed to work natively within the Claude Desktop environment.
Related MCP server: Library Docs MCP Server
Python 3.11+
uv (recommended) or pip
Serper API Key: (Get one free at serper.dev)
Clone the repository:
git clone <repository-url>
cd documentationInstall dependencies:
uv sync
# Or using pip:
pip install -e .Set up API Key:
Create a .env file in the project root:
SERPER_API_KEY=your_api_key_hereAdd this to your claude_desktop_config.json (%APPDATA%\Claude\claude_desktop_config.json on Windows):
{
"mcpServers": {
"coding-assistant": {
"command": "uv",
"args": [
"--directory",
"YOUR_ABSOLUTE_PATH_TO_PROJECT_DIR",
"run",
"main.py"
],
"env": {
"SERPER_API_KEY": "your_api_key_here"
}
}
}
}Once configured, ask Claude questions directly in your chat:
"How do I create a custom retrieval chain in LangChain?"
"Show me the latest LlamaIndex vector store implementation."
"What are the new parameters for OpenAI's chat completions?"
The assistant will fetch the latest docs and help you write the code.
Search the latest docs for a given query and library. Supports langchain, openai, and llama-index.
Args: query: The query to search for (e.g. "Chroma DB") library: The library to search in (e.g. "langchain")
Returns: Text from the docs
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| library | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It notes that the tool searches 'latest docs' and returns text, implying a read-only operation, but does not mention potential network dependency, error cases, or any side effects. This is adequate but not rich.
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 concise and well-structured: a one-sentence purpose statement followed by clear Args/Returns sections. Every sentence adds value, and information is front-loaded.
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?
For a 2-parameter tool with no output schema, the description provides sufficient context: purpose, supported libraries, parameter guidance, and return type. It lacks explicit error handling or formatting details, but these are not critical for this simple search tool.
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%, so the description must fully explain the parameters. It does so effectively with an Args section providing both meaning and examples for 'query' and 'library', plus listing supported library values in the main 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?
The description clearly states the tool's function: 'Search the latest docs for a given query and library.' It specifies the resource (docs), the verb (search), and scope (latest), and distinguishes from sibling Chroma DB tools by focusing on doc search for specific libraries.
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 indicates when to use the tool (when searching docs for langchain, openai, or llama-index) through the list of supported libraries. However, it lacks explicit exclusions or alternative tool references, so it doesn't fully meet the 'when-not/alternatives' criterion.
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.
v1.0.0get_docsScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as searching documentation for specific libraries.
The single tool name 'get_docs' follows a clear verb_noun pattern. Since there is only one tool, naming consistency is inherently perfect with no deviations to assess.
A single tool is too few for a server labeled as a 'Coding Assistant' for LangChain and LlamaIndex. This scope suggests needs like code generation, debugging, or API interaction, which are not covered by just documentation search.
The tool surface is severely incomplete for the stated purpose. It only provides documentation search, missing essential operations like code execution, analysis, or integration with the libraries mentioned, leading to significant gaps in functionality.
The documentation, as a tool your agent can call: 950+ AI-dev guides. Search + fetch tools.
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