A
licenseNot graded
qualityC
maintenanceA Model Context Protocol server providing real-time stock data and options analysis through Yahoo Finance, enabling LLMs to access market data, analyze stocks, and evaluate options strategies.
25
MIT
Agentic AI with MCP
by dev484p
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., "@Agentic AI with MCPget me the latest stock price for Tesla and a brief summary about Elon Musk"
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.
Agentic AI with Model Context Protocol (MCP)
This project implements an Agentic AI system that connects a Groq-hosted LLM (qwen-qwq-32b model) with various tools through a custom Model Context Protocol (MCP) server. The system enhances the LLM's capabilities by providing contextual information from Wikipedia, internet search (via Tavily API), and financial data (via Yahoo Finance API).
About MCP
The Model Context Protocol (MCP) is an open standard developed by Anthropic to standardize how applications provide context to large language models (LLMs). It facilitates seamless integration between LLM applications and external data sources and tools, allowing AI systems to interact dynamically with various services through a standardized interface.
Key Features of MCP:
Standardization: Provides a universal protocol for interfacing AI assistants with structured tools and data layers.
Modular Architecture: Follows a client–server pattern over a persistent stream, typically mediated by a host AI system.
Dynamic Introspection: Supports dynamic discovery of tools and resources through methods like tools/list and resources/list.
Security: Incorporates host-mediated authentication and supports secure transport protocols. By adopting MCP, developers can build AI applications that are more interoperable, secure, and capable of complex workflows.
To add new tools to the MCP server:
Define the Tool: Create a new function that handles the specific task or data retrieval.
Register the Tool: Update the server's tool registry to include the new function, specifying the tool's name and description.
Handle Requests: Ensure the server can route incoming requests to the appropriate tool based on the query. This modular approach allows for easy expansion of the server's capabilities, enabling the language model to access a broader range of contextual information.
Related MCP server: Wikipedia MCP Server
Features
MCP Server: Central hub that provides access to various tools
Three Integrated Tools:
Wikipedia Search - for factual information retrieval
Internet Search - powered by Tavily API for comprehensive web results
Yahoo Finance API - for real-time stock and financial data
Groq API Integration: Ultra-fast LLM processing using qwen-qwq-32b model
Client-Server Architecture: Clean separation between tool management and LLM interaction
Prerequisites
Before you begin, ensure you have the following:
Install UV fro python installation
Groq API key. Refer to the documentation
Tavily API key (sign up at Tavily AI)
Installation
Clone the repository:
git clone https://github.com/dev484p/AgenticAI_MCP cd AgenticAI_MCPInstall dependencies:
uv add "mcp[cli]"Set up your environment variables: Update Groq and Tavily api key in keys.json
Optional (To run the server with the MCP Inspector for development):
uv run mcp dev server.pyRun the following command to initiate the chatbot:
uv run client.py
Available Tools
The system provides three tools through the MCP server:
Wiki Search:
Access Wikipedia information
Example query: "Tell me about the history of artificial intelligence"
Internet Search (Tavily):
Get comprehensive web search results
Example query: "What are the latest developments in quantum computing?"
Yahoo Finance:
Access stock prices and financial data
Example query: "What is the current price of AAPL stock?"
Refrence
Available Tools
3 toolsinternet_searchC
Search the internet using Tavily API.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| limit | No | ||
| include_raw_content | No |
TDQS
C2.8/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the Tavily API but doesn't describe key traits like rate limits, authentication needs, response format, or error handling. This is inadequate for a tool that performs external searches with potential constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded with the core purpose, making it easy to parse quickly, though this brevity contributes to gaps in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (external API search with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral traits, parameter meanings, or return values, making it insufficient for reliable agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 no information about parameters like 'query', 'limit', or 'include_raw_content', failing to compensate for the coverage gap. This leaves the agent without semantic understanding of inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search') and target ('the internet'), and mentions the specific API ('Tavily API'), which provides implementation context. However, it doesn't explicitly differentiate from sibling tools like 'wiki_search' or 'yahoo_finance_search' beyond the general internet scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 ('wiki_search' and 'yahoo_finance_search'). The description lacks any context about appropriate use cases, exclusions, or alternatives, leaving the agent without direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wiki_searchC
Search Wikipedia for articles.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| limit | No |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it's a search operation but doesn't describe what the search returns (e.g., article summaries, links), whether it has rate limits, authentication needs, or any constraints like language or date ranges. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words, making it easy to parse. It's front-loaded with the core action and resource, though it could benefit from additional context to improve usefulness without sacrificing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't address the tool's complexity (a search operation with parameters), leaving the agent unsure about return values, error handling, or how results are structured. For a tool with 2 parameters and no structured output, more detail is needed to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate, but it adds no information about parameters. It doesn't explain what 'query' should contain or that 'limit' controls the number of results, leaving both parameters undocumented beyond their schema definitions. However, with only 2 parameters and a default for 'limit', the baseline is slightly higher.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search') and resource ('Wikipedia for articles'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like 'internet_search' or 'yahoo_finance_search', which would require mentioning Wikipedia specifically as the search target versus broader internet or finance searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. It doesn't mention that it's for Wikipedia-specific searches as opposed to general internet searches or finance-related searches, nor does it specify any prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
yahoo_finance_searchC
Get stock information from Yahoo Finance.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | ||
| period | No | 1mo |
TDQS
C2.7/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets' information, implying a read-only operation, but doesn't clarify if it's safe, requires authentication, has rate limits, or what kind of data it returns. For a tool with zero annotation coverage, this is insufficient to guide the agent's expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of a single sentence that directly states the tool's purpose. There's no wasted language or unnecessary elaboration, making it efficient for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (2 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover parameter meanings, return values, or behavioral traits. While it states the basic purpose, it lacks the depth needed for the agent to use the tool effectively without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no meaning beyond what the input schema provides. With 0% schema description coverage, both parameters ('symbol' and 'period') are undocumented in the schema, and the description doesn't explain what they represent, their formats, or valid values. This leaves the agent guessing about parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get stock information from Yahoo Finance.' It specifies the verb ('Get') and resource ('stock information from Yahoo Finance'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'internet_search' or 'wiki_search', which prevents 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.
Usage Guidelines2/5
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. It doesn't mention sibling tools or any context for choosing this over general search tools. There's no information about prerequisites, limitations, or specific use cases, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
B3.1/5.0
Disambiguation5/5
Each tool has a clearly distinct purpose: internet_search uses Tavily API for general web searches, wiki_search targets Wikipedia articles specifically, and yahoo_finance_search focuses on stock information from Yahoo Finance. There is no overlap in functionality or ambiguity between these three tools.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with a clear verb_noun structure: internet_search, wiki_search, and yahoo_finance_search. The naming is uniform and predictable across the entire set.
Tool Count3/5
With only 3 tools, the server feels thin for the broad domain implied by 'Agentic AI with MCP', which suggests a more comprehensive set of capabilities. While each tool is distinct, the count is borderline low for typical agentic AI tasks that might require more varied operations.
Completeness2/5
The tool surface is severely incomplete for an 'Agentic AI' server, as it only provides search functionalities without any CRUD operations, data processing, or other AI-related actions. There are significant gaps that will limit agent effectiveness, such as missing tools for data manipulation, analysis, or interaction with AI models.
Maintenance
ActivityInactive
ResponsivenessNo issues
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