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., "@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?
No annotations are provided, so the description carries the full burden. It mentions 'latest docs' implying freshness but doesn't disclose behavioral traits like rate limits, authentication needs, pagination, or error handling. For a search tool with zero annotation coverage, this leaves significant unknowns about its operation.
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 well-structured and concise, with a clear purpose statement, supported libraries list, and separate sections for Args and Returns. Each sentence adds value, though the Returns section could be more informative. It's front-loaded and avoids redundancy.
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 2 parameters with 0% schema coverage and no output schema, the description provides basic parameter semantics and return type ('Text from the docs'). However, it lacks details on output format, error cases, or behavioral constraints, making it minimally adequate but incomplete for effective use.
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 schema provides no parameter details. The description adds basic semantics: 'query' is 'The query to search for' with an example, and 'library' is 'The library to search in' with examples and supported values. This compensates partially but lacks depth on format constraints or library specifics.
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 purpose: 'Search the latest docs for a given query and library.' It specifies the verb ('Search'), resource ('docs'), and scope ('latest docs'), and lists supported libraries. However, with no sibling tools, it cannot demonstrate differentiation from alternatives, preventing a perfect score.
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 provides no guidance on when to use this tool versus alternatives, prerequisites, or exclusions. It mentions supported libraries but doesn't explain why or when to choose one over another. Without sibling tools, this is less critical, but the lack of any usage context is a gap.
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.
v1.0.0get_docsWith 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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
The documentation, as a tool your agent can call: 950+ AI-dev guides. Search + fetch tools.
@latest documentation and code examples to 9000+ libraries for LLMs and AI code editors in a singl…
Provide your AI coding tools with token-efficient access to up-to-date technical documentation for…
Versioned documentation registry and semantic search for AI tools and coding assistants.
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