[HN Gopher] Show HN: Fast-agent - Compose MCP enabled Agents and...
       ___________________________________________________________________
        
       Show HN: Fast-agent - Compose MCP enabled Agents and Workflows in
       minutes
        
       Hello, HN.  I've created fast-agent to make building my own
       products easier - and remove the friction between defining Prompts,
       MCP Servers and their composition. It uses a simple, declarative
       style that's easy to work with and source control - with inbuilt
       support for the patterns in the Building Effective Agents paper.
       Because you can "warm-up" and interact with Agents before, during
       or after the workflows, it's easy to diagnose and tune Agent
       prompts and behaviour for later runs. Being able to set these
       workflows up makes LLM Context Management and Tool Selection a lot
       easier and can vastly improve output quality for little effort.
       For MCP Server developers you can see how different models
       interpret tool descriptions. There's also MCP Roots support, and it
       comes bundled with a ChatGPT style data-analysis tool (`fast-agent
       bootstrap data-analysis`) as one of the demonstrations.  One of the
       thing I am most looking forward to is combining MCP data retrieval
       with Anthropic's Citations API - I think that's going to be an
       incredibly important feature in a lot of scenarios.  It's been
       forked from, and and builds upon Sarmad Qadri's mcp-agent
       framework, and we're collaborating to keep the projects in-sync.
       Anyway, I'd love to hear your thoughts and feedback on this
       project, and eager to hear from potential users, contributors and
       collaborators.
        
       Author : evalstate
       Score  : 9 points
       Date   : 2025-03-06 16:34 UTC (6 hours ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | qin wrote:
       | > you can see how different models interpret tool descriptions.
       | 
       | How's this done? I saw the creator of MCP recommended1 "investing
       | heavily in tool descriptions" but it wasn't clear exactly how to
       | 
       | 1 -- https://x.com/dsp_/status/1897599702859645345
        
         | evalstate wrote:
         | The Messages API contains a special section for placing Tool
         | information, which is added to the Context Window - and it's
         | this information that the Model then uses to decide whether to
         | attempt a Tool Call.
         | 
         | In that case, we configure the MCP Server, and then the Host
         | application (in this case fast-agent) uses the Anthropic or
         | OpenAI API to populate it, and they inject it in to the Context
         | Window[1] in the format best for their model.
         | 
         | So for fast-agent, we can set the model when we define the
         | agent with `model="o3-mini.medium"` or from a command line
         | switch. Depending on the type of eval you are doing you could
         | for example use a Parallel workflow to see how the different
         | models perform. Quite often, given a failing tool call the
         | model will attempt to recover (the
         | @modelcontextprotocol/server-filesystem is... an interesting
         | example).
         | 
         | Another fun one is to use Opus 3 tool calling, where it emits
         | <thinking> tags showing how/why it's calling it.
         | 
         | One final point is that different combinations of tools will
         | give different behaviours - if 2 MCP Servers have similar
         | definitions, it will degrade performance... One of the
         | motivations for fast-agent is precisely because it allows
         | dividing tasks up amongst different context windows to get the
         | sharpest performance.
         | 
         | Link to the Anthropic docs as it's my preferred explanation.
         | The Messaging API's grab the JSON and present it as Tool Call
         | types - other models will simply emit JSON and let the Client
         | handle it.
         | 
         | [1] https://docs.anthropic.com/en/docs/build-with-claude/tool-
         | us...
        
       | saqadri wrote:
       | Creator of mcp-agent here, OP and I are going to be keeping these
       | projects in sync, and build agent apps on top of these patterns.
       | MCP is pretty new and people are adding lots of servers for
       | different software services. I think the next step is exposing
       | agents (i.e. compound AI workflows) themselves as MCP servers.
       | 
       | In addition to simple abstractions for declaring agents,
       | fastagents has a really nice CLI utility, which can be useful for
       | interaction with MCPs outside of Cursor and Claude Desktop, where
       | they are most popular atm.
        
       ___________________________________________________________________
       (page generated 2025-03-06 23:02 UTC)