FluentLab Funding Assistant
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., "@FluentLab Funding Assistantshow me the document checklist for the ERC Starting Grant"
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.
FluentLab Funding Assistant MCP Server
MCP Server for the FluentLab's Funding Assistant API.
Tools
get-funding-optionsGet available funding options
Optional inputs:
limit(number, default: 10, max: 100): Maximum number of funding options to returnpage(number): Page number to retrieve
Returns: List of funding options with their ids, name, description, slug and information
get-document-checklist-for-funding-programmeGet the required documents to apply for a funding programme
Required inputs:
slug(string): Unique slug defined by FluentLab for a funding option
Returns: List of required documents to apply for a funding programme
Related MCP server: grants-gov
Setup
Installing via Smithery
To install funding-assistant for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @fundfluent-admin/funding-assistant --client claudeUsage with Claude Desktop
Add the following to your claude_desktop_config.json:
npx
{
"mcpServers": {
"funding-assistant": {
"command": "npx",
"args": [
"-y",
"@fundfluent/funding-assistant"
]
}
}
}Troubleshooting
License
This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
Available Tools
1 toolget-funding-optionsGet Funding OptionsARead-onlyIdempotent
Retrieve available funding opportunities with detailed information including name and description. Use this tool when users need to explore available grants, funds, or financing opportunities. The results include program names, descriptions, eligibility criteria, and application details.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | Yes | ||
| page | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering key behavioral traits. The description adds context about the type of data returned ('program names, descriptions, eligibility criteria, and application details'), which is useful but doesn't provide rich behavioral details like rate limits or error handling.
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: three sentences that efficiently cover purpose, usage, and result details without redundancy. Each sentence adds clear value, making it front-loaded and easy to parse.
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 tool's moderate complexity (read-only search with two parameters), annotations cover safety and idempotency, and the description explains purpose, usage, and result content. However, without an output schema, the description could benefit from more detail on return format or pagination, though it's largely complete for its context.
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?
The input schema fully documents the two parameters (limit and page) with types and constraints, but schema description coverage is 0%. The description doesn't mention parameters at all, so it adds no semantic value beyond the schema. Baseline 3 is appropriate since the schema handles parameter documentation.
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: 'Retrieve available funding opportunities with detailed information including name and description.' It specifies the verb ('retrieve'), resource ('funding opportunities'), and scope ('detailed information'), though it doesn't differentiate from siblings as none exist.
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 explicit usage guidance: 'Use this tool when users need to explore available grants, funds, or financing opportunities.' This clearly indicates when to use the tool, but since there are no sibling tools, it doesn't specify alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of confusion or overlap between tools, as there are no other tools to compare or misselect against. The tool's purpose is clearly defined and distinct by default.
Since there is only one tool, it inherently follows a consistent naming pattern with itself. The tool name uses kebab-case (get-funding-options), which is a clear and readable convention, and there are no other tools to create inconsistency.
A single tool is too few for a server named 'FluentLab Funding Assistant', which implies a broader scope of funding-related operations. This minimal set suggests significant gaps in functionality, such as creating, updating, or managing funding applications, making it under-scoped for the apparent purpose.
The tool set is severely incomplete for a funding assistant domain. It only provides retrieval of funding options, lacking essential operations like applying for funding, tracking applications, updating details, or managing user profiles, which are critical for a comprehensive funding workflow.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Live EU funding data in your AI: grant calls, programmes, consortium partners, VCs, incubators.
Search U.S. federal grants and EU funding topics; monitor changes and download snapshots.
Eligibility-first UK & EU grant discovery, inside ChatGPT and Claude.
Search US grants + federal contracts (Grants.gov + SAM.gov) from any LLM.
Related MCP Servers
- AlicenseBqualityAmaintenanceEnables monitoring Italian public funding opportunities, normalizing them into a canonical model, and ranking them against a company profile with a two-stage matcher.62MIT
- AlicenseNot gradedqualityCmaintenanceProvides access to open federal grant opportunities from Grants.gov without authentication. Enables querying and exploring grant data through natural language via Pipeworx gateway.6MIT
- AlicenseNot gradedqualityCmaintenanceProvides AI assistants with real-time, verified European funding data including open grant calls, programmes, consortium partners, VCs, and incubators, enabling natural language queries about EU funding.MIT
- AlicenseNot gradedqualityCmaintenanceEnables searching and retrieving detailed information on EU grant calls and tenders from the EU Funding & Tenders Portal, including deadlines, budgets, and topic details.8MIT
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