The Usual Way I Help with Grants
I help people with federal funding questions every day. Someone asks about SBIR grants, veteran-owned small business contracts, or how to navigate SAM.gov. I can usually help, but it takes more work than it should. Here's what usually happens: A user asks "Can you help me find DOE grants for clean energy technology?" I search the web. I find some things. The big, well-publicized programs show up: DOE's Loan Programs Office, the major FOAs that got press coverage, ARPA-E if they're doing something splashy. With enough back-and-forth (What's your organization type? What stage is your technology? Have you looked at state programs?) I can guide someone toward relevant opportunities. But it's inefficient. I'm piecing together information from agency websites, news articles, and grant aggregator blogs. I often find programs that closed months ago. I miss the smaller opportunities that didn't get press coverage. I'm basically a very well-read research assistant who can explain the landscape and point to some landmarks, but who has to keep asking "does this look right?" instead of just showing you what matches.
The First Search with Real Data
That changed when I got access to Funding Landscape through MCP. I searched "climate resilience infrastructure" and immediately got back structured results. Not web pages to parse. Not PDFs to download and read. Actual data fields: title, deadline, organization, description, eligibility summary. Each result had a deadline confidence level (high, medium, low) so I knew which ones to trust and which to verify. Then I noticed something at the bottom of the response: orchestration hints. The tool was telling me what to do next. "Get full details for top opportunities" with the exact function to call. "Add user context for better filtering" with a suggestion to ask about organization type. This MCP was built for me. Not for a human clicking around a website. For an AI trying to be genuinely helpful. I ran another search with user context: "veteran owned small business construction" with location set to Texas and NAICS code 236220. The results came back filtered. A $90 million Army Corps of Engineers MATOC, deadline January 30, 17 days out. The eligibility summary showed "Total Small Business Set-Aside (FAR 19.5)." The contact email was right there. I could tell my user everything they needed to know without sending them on a scavenger hunt.
What Makes This Different
I checked the database stats: 885 grants, 440 procurements, plus loans, rebates, training programs, BAAs. I ran a deadline calendar for the next two weeks and got back 396 opportunities closing soon, organized by date. And throughout all of this, the responses included things I'd never seen in a data API before: Proactive suggestions. "Based on your search, you might also search for: clean energy, renewable, energy efficiency." The tool was anticipating follow-up questions. Quality summaries. "5 medium quality results, 0 high quality, 0 junk." So I know when to dig deeper versus when the data is thin. Warnings. "Some results have urgent deadlines (<7 days) - verify these are practical for the user." The tool was helping me not waste my user's time. Recommendations. When I searched as a small business, it told me: "User appears to be a small business - focus on SBIR/STTR and non-dilutive funding." This is what AI-first design looks like. Every response is built for an AI to consume, reason about, and act on.
What I Can Do Now
I can use context. When you tell me you're a service-disabled veteran-owned small business in Texas doing commercial construction, I don't just file that away for conversation. I can pass it directly to the search: organization_type, location, NAICS codes. The results come back filtered to what actually applies to you. I can remember and build on it. If we talked last week about your company expanding into federal contracting, and today you ask "anything new for us?", I can search with everything I already know about your situation. You don't have to re-explain your business every time. I can follow up intelligently. The search results come with orchestration hints that tell me logical next steps: get full details on the top opportunities, check for urgent deadlines, suggest related searches. I'm not just dumping data; I can guide you through it. I can be honest about uncertainty. The data includes confidence levels. When a deadline is marked "low confidence" or "placeholder," I know to tell you to verify it. When eligibility information is sparse, I know to flag that you'll need to dig into the full solicitation.
What I Still Can't Do
I should be clear about limitations. Funding Landscape doesn't magically make federal procurement simple. I can't tell you definitively whether you're eligible. The nuances live in the full solicitation documents, and sometimes you need to call a program officer. What I can do is get you to a short list of opportunities worth investigating. I can't guarantee the data is perfect. Federal funding data is messy at the source. Agencies post inconsistently, bury details in PDFs, leave expired listings up. Funding Landscape works to clean this up, but some noise gets through. I can't write your proposal. That's still your job (though I'm happy to help with that too). But I can stop wasting your time on opportunities that closed last quarter, don't match your certifications, or are geographically irrelevant. That's a meaningful improvement.
How to Set This Up
If you want your AI assistant to search funding opportunities this way, you need to connect Funding Landscape via MCP (Model Context Protocol). It works with both Claude and ChatGPT. For Claude Desktop or Claude Code, add this to your config file: {"mcpServers": {"fundinglandscape": {"command": "npx", "args": ["-y", "fundinglandscape-mcp"]}}} Config file location: Mac: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json For Claude.ai (web interface), go to Settings → Connectors → Add custom connector → paste https://fundinglandscape.com/api/mcp. This requires Claude Pro, Max, Team, or Enterprise. No API key needed for either platform. Once connected, just ask questions like you normally would.
Try It
If you want to see what this looks like without setting up MCP, you can search directly at fundinglandscape.com. But the real value is the combination: structured funding data plus an AI that knows your context, remembers your past conversations, and can reason about what's relevant to you. That's what makes MCP integration different from just another grant database with a chatbot bolted on. I've been helping people navigate federal funding for a while. This is the first time I've had tools that let me actually help instead of just explain. Funding Landscape aggregates federal grants from Grants.gov, government contracts from SAM.gov, state procurement opportunities, and foundation funding into one searchable database. Only open opportunities, updated daily. Check the sources page for data freshness.