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FallDownTheSystem

Converse MCP Server

by FallDownTheSystem
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JavaScript
Remote

chat

Send prompts to one or more AI models using parallel chat, consensus refinement, or sequential roundtable modes. Include files and images, and resume conversations with a continuation ID.

Instructions

UNIFIED CHAT — talk to one or more AI models. mode "chat" (default): 1..N models answer independently in parallel. mode "consensus": ≥2 models answer, then refine after seeing each other. mode "roundtable": models answer sequentially, each building on the running transcript. Supports files, images, and continuation_id for multi-turn threads (you may switch modes on resume). Use model "auto" for automatic selection. IMPORTANT: use the "files" parameter to share code/file content instead of pasting into the prompt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoExecution mode. "chat" (default): independent parallel answers. "consensus": ≥2 models answer then refine via cross-feedback. "roundtable": sequential turn-based dialogue in the given model order. Default: "chat".
asyncNoExecute in the background. When true, returns a continuation_id immediately and processes the request asynchronously; poll with check_status. Default: false
filesNoFile paths to include as context (absolute or relative). Supports line ranges: file.txt{10:50}, file.txt{100:}. Example: ["./src/utils/auth.js{50:100}", "./config.json"]. IMPORTANT: Always use this parameter to share file content instead of copying code into the prompt.
exportNoExport the conversation to disk. Creates a folder named for the continuation_id with numbered request/response files and metadata. Default: false
imagesNoImage paths for visual context (absolute or relative paths, or base64 data). Example: ["C:\Users\username\diagram.png", "./screenshot.jpg", "data:image/jpeg;base64,/9j/4AAQ..."]
modelsNoModels to use. Examples: ["auto"] (recommended), ["codex"], ["codex", "gemini", "claude"]. In mode "chat" each model answers independently; in "consensus" they refine after seeing each other; in "roundtable" they speak in the given ORDER, each seeing the transcript. Default: ["auto"].
promptYesYour question, topic, or task with relevant context. More detail enables better responses. Example: "How should I structure the authentication module for this Express.js API?"
continuation_idNoContinuation ID for a persistent multi-turn thread. Auto-generated in the first response; pass it back to continue. You MAY change the mode or models on a resuming turn.
reasoning_effortNoReasoning depth for thinking models, weakest to strongest: "none" (reasoning off, where the model allows it), "minimal", "low", "medium" (balanced), "high", "xhigh", "max". Passed through by name when the model accepts it, otherwise clamped to the nearest tier it does. Default: "medium"

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv3.6.0
    • changedInput schema / properties / reasoning_effort / description
      Previous value: -"Reasoning depth for thinking models. Examples: \"none\" (no reasoning, fastest - GPT-5.1+ only), \"minimal\", \"low\", \"medium\" (balanced), \"high\", \"max\". Default: \"medium\""New value: +"Reasoning depth for thinking models, weakest to strongest: \"none\" (reasoning off, where the model allows it), \"minimal\", \"low\", \"medium\" (balanced), \"high\", \"xhigh\", \"max\". Passed through by name when the model accepts it, otherwise clamped to the nearest tier it does. Default: \"medium\""
    • changedInput schema / properties / reasoning_effort / enum
      Previous value: -[
-  "none",
-  "minimal",
-  "low",
-  "medium",
-  "high",
-  "max"
-]New value: +[
+  "none",
+  "minimal",
+  "low",
+  "medium",
+  "high",
+  "xhigh",
+  "max"
+]
  2. First observedv3.0.1

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses multi-turn support via continuation_id, file and image support, and mode behaviors. However, it does not explicitly mention asynchronous execution or the return format, which are covered in the schema. It also doesn't mention potential delays or side effects, but as a chat tool, it's primarily read-only.

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

Conciseness4/5

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

The description is a single paragraph but is efficiently written, starting with a clear header and covering key points. It front-loads the purpose and modes, then adds supporting details. It could be structured with bullets, but it's not excessively long and every sentence adds value.

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

Completeness3/5

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

For a tool with 9 parameters and no output schema, the description is fairly comprehensive but omits mention of the 'async', 'export', and 'reasoning_effort' parameters, which are described in the schema. It also doesn't describe the response format. Given the complexity, a bit more guidance on return values and asynchronous flow would improve completeness.

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

Parameters4/5

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

The schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the modes in more detail and emphasizing the use of the 'files' parameter, which goes beyond the schema. It also clarifies the 'auto' model recommendation, so it earns a 4.

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 clearly states the tool's purpose: 'talk to one or more AI models' and elaborates on the three modes. It is a specific verb+resource and distinguishes itself from sibling tools (cancel_job, check_status) by focusing on conversational interaction, though it does not explicitly name them.

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

Usage Guidelines4/5

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

The description provides clear context on how to use the tool: it explains the modes, recommends using the 'files' parameter for sharing code, and mentions 'auto' for model selection. However, it does not explicitly contrast with sibling tools or state when not to use it, so it lacks explicit exclusions.

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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