How to Use the NASA TechPort (Technology Projects) MCP in LlamaIndex

Q: Can I use NASA TechPort (Technology Projects) tools for semantic search in LlamaIndex?

Yes. While the server provides structured search via searchobjectadvanced, you can feed these results into a LlamaIndex vector index. This allows you to perform semantic vector searches over the structured federal project data returned by the API.

Q: How do I filter which tools my LlamaIndex agent can access?

You can define an allowedtools list when initializing the tool specification. This lets you restrict your agent to specific tools like listopportunities or getopportunity while keeping sensitive administrative tools locked down.

Index NASA TechPort aerospace research directly into LlamaIndex vector stores for hallucination-free retrieval.

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Connect NASA TechPort (Technology Projects) MCP to LlamaIndex

Create your Vinkius account to connect NASA TechPort (Technology Projects) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup NASA TechPort (Technology Projects) with LlamaIndex

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Build a RAG pipeline for federal aerospace research

Stop relying on outdated training data for your aerospace market intelligence. This MCP Server allows LlamaIndex to query `search_object_all_data` and feed the live results directly into your document index. Your queries are grounded in current federal R&D efforts, preventing common LLM hallucinations about active projects. By indexing the output of `get_project`, your application builds a searchable knowledge base of active NASA research. Users can run semantic queries across the indexed data to identify which organizations are leading specific technology developments.

Map technology taxonomy trees to vector embeddings

Understanding complex federal structures requires structured data indexing. Use `get_taxonomy_tree` to fetch the complete hierarchical classification of NASA's technology sectors and load it directly into a LlamaIndex VectorStoreIndex. This transforms a rigid taxonomy into a searchable semantic map. When your agent needs to classify a new internal concept, it queries the indexed taxonomy nodes using `list_taxonomy_nodes`. This ensures your internal projects are categorized using the exact terminology used by federal grant evaluators.

Keep your LlamaIndex knowledge base updated on funding

Automate the collection of federal funding data for your investment or research team. By running a pipeline that calls `list_opportunities` and indexes the results, your LlamaIndex application maintains an up-to-date repository of active solicitations. You can query this repository to find trends in federal funding allocations. For deeper analysis, the pipeline uses `get_opportunity` to pull down specific requirements and updates the index incrementally. This means your research team always has access to the latest compliance criteria and funding caps.

Setup guide

Set up NASA TechPort (Technology Projects) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all NASA TechPort (Technology Projects) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to NASA TechPort (Technology Projects) tools.",
)
response = await agent.run("List recent NASA TechPort (Technology Projects) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by NASA TechPort. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about NASA TechPort (Technology Projects) MCP in LlamaIndex

You use the LlamaIndex integration package to initialize the client and convert the server tools into a tool spec. From there, you can pass the tools to a `FunctionAgent` or use them within a query engine pipeline to fetch data like `get_project` dynamically during query time.

Yes. While the server provides structured search via `search_object_advanced`, you can feed these results into a LlamaIndex vector index. This allows you to perform semantic vector searches over the structured federal project data returned by the API.

You can implement document caching at the LlamaIndex level to avoid redundant API calls. For example, caching the output of `get_taxonomy` ensures you do not waste API quota fetching the same technology taxonomy tree multiple times.

You can define an `allowed_tools` list when initializing the tool specification. This lets you restrict your agent to specific tools like `list_opportunities` or `get_opportunity` while keeping sensitive administrative tools locked down.

Your NASA TechPort API key and search parameters remain isolated within your local LlamaIndex runtime. The Vinkius MCP Server acts as an ephemeral proxy, passing queries to the federal API without logging your proprietary search terms or the project IDs you inspect.

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