openenv/pr_793/en/_app/immutable/nodes/47.9286600a.js

import{s as jl,n as Fl,o as Nl}from"../chunks/scheduler.2b22cead.js";import{S as Ol,i as Zl,e as s,s as i,c as r,h as Dl,a,d as l,b as o,f as zl,g as p,j as c,k as Rl,l as Gl,m as n,n as d,t as u,o as m,p as v}from"../chunks/index.1a0e8013.js";import{C as Ql,H as f,E as Yl}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.48954dcb.js";import{C as Xl}from"../chunks/CodeBlock.9942e851.js";function Vl(Nt){let h,Je,ge,Ie,T,ke,C,Ee,$,Ot="This guide explains how MCP-backed environments work end to end in OpenEnv.",Ae,x,Zt="It exists to answer a common question: if an environment exposes MCP tools, when does <code>step()</code> run, when does <code>step_async()</code> run, and when should you use <code>call_tool()</code> versus <code>step(CallToolAction(...))</code>?",Ue,w,qe,y,Dt="MCP environments in OpenEnv can be used in two layers:",We,M,Gt="<li><strong>Simulation layer</strong>: the OpenEnv training loop controls <code>reset()</code>, <code>step()</code>, and <code>state()</code>.</li> <li><strong>Tool layer</strong>: MCP tools are exposed through <code>ListToolsAction</code>, <code>CallToolAction</code>, <code>list_tools()</code>, and <code>call_tool()</code>.</li>",Se,_,Qt="If you are training or evaluating with episode control, the canonical pattern is still the OpenEnv step loop.",Be,b,Yt="If you are serving tools to an external client, the MCP layer is the interface the agent should see.",ze,L,Re,P,Xt="OpenEnv keeps a strict API split:",je,g,Vt="<li><strong>Infrastructure boundary</strong>: Gym-like control over <code>/ws</code>, <code>reset()</code>, <code>step()</code>, and <code>state()</code></li> <li><strong>Agent boundary</strong>: MCP tools over <code>/mcp</code></li>",Fe,H,Kt="This means:",Ne,J,el="<li>agents should use MCP tools</li> <li>orchestration and training infrastructure use the simulation control loop</li> <li><code>/ws</code> is not an agent-facing interface, even if it is available on the server</li>",Oe,I,Ze,k,tl="<code>MCPEnvironment</code> is still an OpenEnv environment.",De,E,ll="It does not replace the step loop. Instead, it maps MCP actions into the step loop.",Ge,A,nl="In simulation mode, MCP tool usage is represented as normal environment actions:",Qe,U,Ye,q,il="That is why an MCP-backed environment can still participate in:",Xe,W,ol="<li>rewards</li> <li><code>done</code> handling</li> <li>step counts</li> <li>trajectory logging</li>",Ve,S,Ke,B,sl="This is the main source of confusion.",et,z,al="On the server side, the WebSocket handler checks whether the environment overrides <code>step_async()</code>.",tt,R,cl="<li>if <code>step_async()</code> is overridden, the WebSocket path calls <code>step_async()</code></li> <li>otherwise, it falls back to <code>step()</code></li>",lt,j,rl="That means an async client using the WebSocket session path may execute <code>step_async()</code> without hitting your synchronous <code>step()</code> instrumentation.",nt,F,pl="So if you add debug prints only to <code>step()</code> and use an async MCP client, it can look like “step is not being invoked” even though the action is being processed normally.",it,N,dl="For debugging, check both:",ot,O,ul="<li><code>step()</code></li> <li><code>step_async()</code></li>",st,Z,ml="The same rule applies to <code>reset()</code> and <code>reset_async()</code>.",at,D,ct,G,vl="Environment-specific MCP clients such as <code>EchoEnv</code> and <code>FinQAEnv</code> inherit from <code>MCPToolClient</code>.",rt,Q,fl="Those clients target a running environment server and expose convenience methods:",pt,Y,hl="<li><code>list_tools()</code></li> <li><code>call_tool()</code> — <strong>async</strong>, must be awaited</li>",dt,X,Tl="These are helpers, not a separate environment lifecycle.",ut,V,mt,K,Cl="<code>MCPToolClient</code> and its base <code>MCPClientBase</code> only support <code>mode=&quot;production&quot;</code>; construction raises <code>ValueError</code> for other modes. For direct in-process training or eval code, instantiate the environment class and call <code>env.step(CallToolAction(...))</code> instead of using <code>MCPToolClient</code>.",vt,ee,$l="<li><code>list_tools()</code> wraps <code>step(ListToolsAction())</code> and returns the <code>list[Tool]</code> directly.</li> <li><code>call_tool(name, **kwargs)</code> returns the <strong>unwrapped tool return value</strong> directly — not the <code>CallToolObservation</code>, and not the runtime result object you would get from <code>obs.result</code>.</li> <li><code>call_tool()</code> raises <code>RuntimeError</code> on any tool error. Use <code>step(CallToolAction(...))</code> when you need to inspect <code>ToolError.error_type</code> or continue after a failed tool call.</li>",ft,te,xl="Remote <code>step(CallToolAction(...))</code> calls return a <code>StepResult</code>; the full observation is on <code>result.observation</code>, while <code>result.reward</code> carries the serialized reward field.",ht,le,Tt,ne,wl="When production MCP access is explicitly enabled on the client, the same convenience methods use the HTTP <code>/mcp</code> JSON-RPC endpoint directly.",Ct,ie,yl="That path is for tool-serving behavior, not the training loop. It bypasses reward computation, step counts, trajectory tracking, and <code>done</code> handling.",$t,oe,xt,se,Ml="Use <code>step(CallToolAction(...))</code> when you need the full <code>CallToolObservation</code>:",wt,ae,_l="<li><code>reward</code></li> <li><code>done</code></li> <li>observation metadata</li> <li><code>obs.result</code>, a runtime result object typed as <code>Any</code>; FastMCP commonly returns <code>fastmcp.client.client.CallToolResult</code> with <code>.data</code>, <code>.content</code>, and <code>.structured_content</code>, but serialized clients or custom envs may surface a dict or plain value</li> <li>trajectory-compatible behavior</li>",yt,ce,bl="Use <code>await env.call_tool(name, **kwargs)</code> when you only want the tool’s raw return value and do not need to inspect the full observation. It is async and unwraps the result for you.",Mt,re,Ll="In other words:",_t,pe,Pl="<li><code>step(...)</code> is the canonical simulation pattern</li> <li><code>call_tool()</code> is an async convenience wrapper that returns the unwrapped tool output</li>",bt,de,Lt,ue,gl="Two good references in this repo are:",Pt,me,Hl='<li><a href="../environments/echo">Echo environment</a></li> <li><a href="../environments/finqa">FinQA environment</a></li>',gt,ve,Jl="For a minimal simulation-mode example, see:",Ht,fe,Il="<li><code>examples/echo_mcp_demo.py</code></li>",Jt,he,kl="Echo is useful because it shows the MCP mechanics with almost no domain logic.",It,Te,El="FinQA is useful because it shows an MCP environment where tool calls also participate in episode progression, rewards, and terminal submission.",kt,Ce,Et,$e,Al="Think about MCP environments in OpenEnv like this:",At,xe,Ul="<li>The environment is still an OpenEnv environment.</li> <li>MCP tools are one kind of action the environment knows how to handle.</li> <li>In simulation mode, tool calls are part of the step loop.</li> <li>In production mode, MCP becomes the agent-facing boundary.</li> <li>The WebSocket simulation interface remains infrastructure-only and must not be given directly to agents.</li>",Ut,we,qt,ye,ql="If an MCP environment “doesn’t call step”, check these first:",Wt,Me,Wl="<li>Are you using an async client path that triggers <code>step_async()</code>?</li> <li>Did you instrument both <code>step()</code> and <code>step_async()</code>?</li> <li>Are you using <code>call_tool()</code> and assuming it bypasses the step loop?</li> <li>Are you expecting the MCP tool layer to behave like a separate environment lifecycle?</li>",St,_e,Sl="Usually the action is flowing correctly, but through the async WebSocket path rather than the synchronous method you were watching.",Bt,be,zt,Le,Bl='<li><a href="../reference/core">Core API</a></li> <li><a href="../environments/echo">Echo environment</a></li> <li><a href="../environments/finqa">FinQA environment</a></li>',Rt,Pe,jt,He,Ft;return T=new Ql({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),C=new f({props:{title:"MCP Environment Lifecycle",local:"mcp-environment-lifecycle",headingTag:"h1"}}),w=new f({props:{title:"The Short Answer",local:"the-short-answer",headingTag:"h2"}}),L=new f({props:{title:"The Two Boundaries",local:"the-two-boundaries",headingTag:"h2"}}),I=new f({props:{title:"How MCP Environments Handle Actions",local:"how-mcp-environments-handle-actions",headingTag:"h2"}}),U=new Xl({props:{code:"ZnJvbSUyMG9wZW5lbnYuY29yZS5lbnZfc2VydmVyLm1jcF90eXBlcyUyMGltcG9ydCUyMENhbGxUb29sQWN0aW9uJTJDJTIwTGlzdFRvb2xzQWN0aW9uJTBBJTBBb2JzJTIwJTNEJTIwZW52LnN0ZXAoTGlzdFRvb2xzQWN0aW9uKCkpJTBBJTBBb2JzJTIwJTNEJTIwZW52LnN0ZXAoJTBBJTIwJTIwJTIwJTIwQ2FsbFRvb2xBY3Rpb24oJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwdG9vbF9uYW1lJTNEJTIyZWNob19tZXNzYWdlJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwYXJndW1lbnRzJTNEJTdCJTIybWVzc2FnZSUyMiUzQSUyMCUyMkhlbGxvJTIyJTdEJTJDJTBBJTIwJTIwJTIwJTIwKSUwQSk=",highlighted:`<span class="hljs-keyword">from</span> openenv.core.env_server.mcp_types <span class="hljs-keyword">import</span> CallToolAction, ListToolsAction

obs = env.step(ListToolsAction())

obs = env.step(
    CallToolAction(
        tool_name=<span class="hljs-string">&quot;echo_message&quot;</span>,
        arguments={<span class="hljs-string">&quot;message&quot;</span>: <span class="hljs-string">&quot;Hello&quot;</span>},
    )
)`,lang:"python",wrap:!1}}),S=new f({props:{title:"Why step() May Look Like It Is Not Running",local:"why-step-may-look-like-it-is-not-running",headingTag:"h2"}}),D=new f({props:{title:"What list_tools() and call_tool() Actually Do",local:"what-listtools-and-calltool-actually-do",headingTag:"h2"}}),V=new f({props:{title:"Client behavior",local:"client-behavior",headingTag:"h3"}}),le=new f({props:{title:"Direct MCP behavior",local:"direct-mcp-behavior",headingTag:"h3"}}),oe=new f({props:{title:"Which Pattern Should You Use?",local:"which-pattern-should-you-use",headingTag:"h2"}}),de=new f({props:{title:"Concrete Examples",local:"concrete-examples",headingTag:"h2"}}),Ce=new f({props:{title:"Recommended Mental Model",local:"recommended-mental-model",headingTag:"h2"}}),we=new f({props:{title:"Debugging Checklist",local:"debugging-checklist",headingTag:"h2"}}),be=new f({props:{title:"Related Reading",local:"related-reading",headingTag:"h2"}}),Pe=new 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