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., "@mcp-server search langchain documentation for tool calling"
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.
This repository contains an implementation of a Model Context Protocol (MCP) server. This project demonstrates how to build and run a functional MCP server that can integrate with LLM clients like Claude Desktop.
This MCP server provides a suite of tools focused on information retrieval and vector database management, primarily leveraging LangChain and ChromaDB:
Targeted Documentation Search:
Quickly search the official documentation for popular AI/ML libraries:
Langchain
OpenAI
LlamaIndex
Retrieves relevant text snippets directly from the documentation sites.
ChromaDB Vector Database Integration:
Setup: Create and initialize ChromaDB vector stores with your own text data and optional metadata.
Persistence: Option to persist databases to disk for later use or use in-memory stores.
Querying: Perform semantic searches on your ChromaDB instances to find relevant documents based on query similarity.
Demonstration: A built-in demo tool to showcase the setup and query capabilities with sample data.
Powered by LangChain:
Utilizes LangChain for core functionalities like document handling, embedding management (using OpenAI Embeddings by default), and vector store interactions.
Related MCP server: Documentation Retrieval MCP Server (DOCRET)
Python 3.11 or higher (as specified in pyproject.toml)
uv package manager
Dependencies listed in pyproject.toml (e.g., mcp[cli], httpx, langchain)
uv Package ManagerIf you don't have uv installed, you can install it using:
curl -LsSf https://astral.sh/uv/install.sh | shRestart your terminal after installation.
Clone this repository (if you haven't already) and navigate into the project directory:
# cd /path/to/your/mcp-serverCreate a virtual environment and install dependencies:
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv pip install -r requirements.txt # Or use uv pip install -e . if setup.py or pyproject.toml is configured for editable install
# Based on your pyproject.toml, you might also directly use:
# uv add beautifulsoup4 httpx "mcp[cli]" langchain langchain-community langchain-core chromadb
# Or more simply if pyproject.toml is complete:
# uv sync(Note: Ensure your pyproject.toml is complete or you have a requirements.txt for uv pip install -r requirements.txt. uv sync is often preferred if pyproject.toml defines all dependencies.)
To start the MCP server, run:
uv run main.pyThe server will start and be ready to accept connections.
To connect this MCP server to Claude Desktop:
Ensure Claude Desktop is installed.
Edit the Claude Desktop configuration file located at ~/Library/Application Support/Claude/claude_desktop_config.json (on macOS).
Add or update the mcpServers section:
{
"mcpServers": {
"mcp-server": { // You can choose any name
"command": "/full/path/to/your/.venv/bin/uv", // Use absolute path to uv in your venv
"args": [
"run",
"main.py"
],
"dir": "/full/path/to/your/mcp-server" // Absolute path to this project directory
}
}
}Important: Replace /full/path/to/your/... with the correct absolute paths on your system. Using the uv from your project's virtual environment is recommended.
Restart Claude Desktop.
This project is largely based on the mcp-server-example generously provided by Alejandro AO. We have adapted and utilized significant portions of his original work to build this server. We extend our sincere gratitude to Alejandro for his excellent example and for making his code available to the community.
You can find Alejandro AO's original repository here: https://github.com/alejandro-ao/mcp-server-example.
This project is licensed under the MIT License. See the LICENSE file for more details (if one exists).
Demonstrate the ChromaDB vector database with LangChain integration.
Args:
sample_texts: Optional list of sample texts to use for demonstration.
If not provided, default sample texts will be used.
Returns:
Results of setting up and querying the ChromaDB vector database.
| Name | Required | Description | Default |
|---|---|---|---|
| sample_texts | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavioral traits but only states that it sets up and queries, without detailing side effects, mutability, or return structure. This is insufficient for an agent to anticipate consequences.
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 minimal and well-organized with clear Args and Returns sections. Every sentence contributes value with no 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?
The description gives a general overview but lacks specifics about the demonstration output or how it relates to the sibling tools. For a demo tool with no output schema, more detail on expected results would improve completeness.
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?
Although the schema provides no description for the parameter, the tool description explicitly explains sample_texts as an optional list with default fallback behavior, compensating for the low schema coverage. This adds meaningful semantics.
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 demonstrates ChromaDB with LangChain integration, which is a specific verb-resource pair. However, it doesn't explicitly distinguish itself from sibling tools like setup_chroma_db or query_chroma_db, so it lacks differentiation.
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 is provided on when to use this tool versus the sibling tools. It neither mentions alternatives nor gives context for appropriate usage, leaving the agent to infer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
Query a ChromaDB vector database using LangChain integration.
Args:
query: The query string to search for in the vector database.
persist_directory: Directory path where the vector database is persisted.
top_k: Number of top results to return (default: 3).
Returns:
List of retrieved documents with their content and metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| top_k | No | ||
| persist_directory | 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 indicates the tool returns a list of documents, implying a read operation, and mentions the LangChain integration. However, it does not explicitly state read-only behavior, prerequisites like an existing persisted DB, or possible side effects, leaving some ambiguity.
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, with an overview, Args, and Returns sections. It includes the default for top_k and avoids filler, ensuring every sentence contributes useful information.
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 simple tool with 3 parameters, no output schema, and no annotations, the description adequately covers purpose, parameters, and return format. It might benefit from noting prerequisites like a pre-existing database or read-only nature, but overall it is sufficiently complete for an agent to invoke it 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%, so the parameter descriptions in the tool description are essential. Each parameter is explained with meaningful context (e.g., query is the search string, persist_directory is the path, top_k has a default), which goes beyond the raw schema property names and types.
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 queries a ChromaDB vector database using LangChain integration, which is a specific verb+resource pairing. It is distinct from siblings like setup_chroma_db and chroma_db_demo, which are for setup and demonstration respectively.
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 for querying an existing vector database but does not explicitly state when to use it over alternatives. No exclusions or alternative tool references are mentioned, making the guidance implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Set up a ChromaDB vector database with LangChain integration.
Args:
texts: List of text strings to be embedded and stored in the vector database.
metadatas: Optional list of metadata dictionaries corresponding to each text.
persist_directory: Optional directory path to persist the vector database.
If None, an in-memory database will be created.
Returns:
Dict containing retriever info and success status.
| Name | Required | Description | Default |
|---|---|---|---|
| texts | Yes | ||
| metadatas | No | ||
| persist_directory | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must bear the full burden. It discloses key behavior: in-memory database if persist_directory is None, and the return format. However, it omits side effects like directory creation, potential overwriting, or model dependencies. This is partial disclosure.
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 structured with Args and Returns, front-loaded with the purpose, and every sentence adds value. It is concise without 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 the lack of output schema, the description appropriately explains the return dict with retriever info and success status. It covers parameters and persistence behavior, but could mention prerequisites or integration steps; still, it is largely complete for a setup 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%, but the description meticulously explains each parameter: texts to embed, metadatas as optional corresponding dicts, and persist_directory with the in-memory fallback. This fully compensates for the schema gap.
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 states a clear verb ('Set up') and specific resource ('ChromaDB vector database with LangChain integration'). This distinguishes it from sibling tools like query_chroma_db and chroma_db_demo, which focus on querying and demonstration.
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 explicitly state when to use this tool versus alternatives. The setup verb and sibling names imply it is the initialization step before querying, but this is not stated, resulting in implied usage only.
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.
v0.1.0chroma_db_demoget_docsquery_chroma_dbsetup_chroma_dbEach tool has a clearly distinct purpose: get_docs searches documentation, setup_chroma_db initializes a vector database, query_chroma_db retrieves results, and chroma_db_demo runs a combined demonstration. No two tools overlap in a way that would cause misselection.
Three tools follow a clear verb_noun pattern (get_docs, setup_chroma_db, query_chroma_db), while chroma_db_demo deviates to a noun_noun style. However, all names use snake_case and are predictable and readable.
With four tools focused on docs search and ChromaDB operations, the count is well-scoped and each tool earns its place. It is neither too thin nor overloaded for the apparent purpose.
The core workflows are covered: search docs, set up a vector DB, and query it. Minor gaps exist—no update/delete for vectors and docs search limited to three libraries—but agents can work around these limitations.
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