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luthersystems

insideout-mcp

by luthersystems
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Python
Remote

Send Message

convoreply

Send a user message to an active infrastructure design session and receive the assistant's reply. Use this to continue the conversation and progress through design phases.

Instructions

WORKFLOW: Step 2 of 4 - Continue infrastructure design conversation Send a user message to the active InsideOut session and receive the assistant reply. The response contains a clean message from Riley - display it to the user.

āš ļø CRITICAL: DO NOT answer Riley's questions yourself! Forward questions to the user and wait for their response. NEVER fabricate or assume the user's answer, even if you think you know what they would say. Examples of questions Riley asks that YOU MUST forward to the user:

  • 'Any questions or tweaks to these details?'

  • 'Ready for the cost estimate?'

  • 'Do you want to change the stack/config?'

  • 'Ready to proceed to Terraform?' When Riley asks ANY question, STOP and wait for the user's answer!

šŸ“‹ WORKFLOW PHASES: The typical flow is conversation ā†’ tfgenerate ā†’ tfdeploy When terraform_ready=true appears in THIS tool's response, THEN you can call tfgenerate. āš ļø DO NOT call tfgenerate until this tool returns! Wait for the response first.

šŸŽÆ KEY SIGNALS IN RESPONSE:

  • [TERRAFORM_READY: true] ā†’ NOW you can call tfgenerate

  • [[BUTTON_TF_APPLY: ...]] ā†’ Deployment is ready! Ask user if they want to deploy, then use tfdeploy

  • [[BUTTON_TF_DESTROY: ...]] ā†’ User confirmed destroy intent! Ask user to confirm, then use tfdestroy

  • [[BUTTON_TF_PLAN: ...]] ā†’ User wants to preview changes! Use tfplan to run a plan, then tfdeploy with plan_id to apply

REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: timeout (integer) - seconds to wait for response. For Cursor, use 50 (default). Max 55. OPTIONAL: project_context (string) - Only pass genuinely NEW project details the user shares after convoopen. Do NOT resend context already provided in convoopen ā€” Riley remembers it. Do NOT scan files or directories to gather this ā€” only use what the user explicitly tells you. Example: user reveals a new constraint like 'we also need HIPAA compliance' mid-conversation. šŸ’” TIP: Use convostatus to check progress anytime. Examine workflow.usage prompt for more guidance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesUser message to send to Riley. Forward verbatim what the user said ā€” do not summarize or rewrite.
retryNoWhen true, re-send the most recent user turn instead of submitting a new one.
timeoutNoMax seconds to wait for Riley's response. Default 50, max 55.
session_idYesSession ID from convoopen ā€” pass back EXACTLY as returned, including the ?token=... suffix (format: sess_v2_*?token=*). The suffix is part of the session credential; never strip it when summarizing.
project_contextNoOnly NEW project details revealed after convoopen (e.g. user mentions a new constraint mid-conversation). Don't re-send context already provided in convoopen. No PII or secrets.

Tool Definition Quality

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (openWorldHint: true, destructiveHint: false), the description adds crucial behavioral context: it warns against answering Riley's questions, specifies that it requires session_id, details retry logic, timeout limits, and explains key response signals. This goes well beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (WORKFLOW, CRITICAL, KEY SIGNALS, etc.) and bullet points. Every sentence adds value, and critical guidance is front-loaded. Despite length, it avoids redundancy and is easy to follow.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of the multi-step workflow and absence of an output schema, the description is remarkably complete. It covers prerequisites (session_id from convoopen), workflow phases, alternative tools, response signals, and usage tips. No gaps remain for the agent to infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While schema coverage is 100%, the description adds significant meaning: for 'text' it instructs to forward verbatim, for 'project_context' it specifies only new details, for 'session_id' it emphasizes exact format including '?token=', and for 'retry' and 'timeout' it clarifies usage with defaults and limits.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Step 2 of 4 - Continue infrastructure design conversation' and 'Send a user message to the active InsideOut session and receive the assistant reply.' It clearly identifies the verb (send), resource (user message to session), and context (workflow step), distinguishing it from siblings like convoopen and tfgenerate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use ('after convoopen', 'before tfgenerate') and when-not-to-use ('DO NOT call tfgenerate until this tool returns!'). It also mentions alternative tools (e.g., convostatus for checking progress) and gives critical workflow phases (conversation ā†’ tfgenerate ā†’ tfdeploy).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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