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yksanjo

fusion-mcp

by yksanjo
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JavaScript
Remote

fusion-mcp

A zero-dependency MCP server that exposes OpenRouter Fusion as a tool any agent can call.

Fusion sends your prompt to a panel of models in parallel (with web search), a judge model compares their answers, and you get back one synthesized answer — stronger than any single model. This wraps it so Claude Code (or any MCP client) can reach Fusion directly instead of going through the OpenRouter web UI.

Tool

fusion_ask

arg

required

description

prompt

✅

The question or task for the Fusion panel.

analysis_models

1–8 model slugs forming the panel. Omit for Fusion's default preset.

judge_model

Model that synthesizes the panel's answers. Defaults to the first panel model.

preset

Curated preset: general-high or general-budget.

max_tool_calls

Tool iterations per panel model (1–16, default 8).

Fusion runs several completions per call (panel + judge), so it costs more than a single model call. Use it where being wrong is costly: research, critique, decisions.

Related MCP server: mcp-second-opinion

Setup

Requires Node ≥ 18 and an OpenRouter API key.

export OPENROUTER_API_KEY=sk-or-...

Wire into Claude Code

claude mcp add fusion -e OPENROUTER_API_KEY=sk-or-... -- node /Users/yoshikondo/fusion-mcp/server.mjs

Or add to your MCP config manually:

{
  "mcpServers": {
    "fusion": {
      "command": "node",
      "args": ["/Users/yoshikondo/fusion-mcp/server.mjs"],
      "env": { "OPENROUTER_API_KEY": "sk-or-..." }
    }
  }
}

Then in a session: "use fusion_ask to research X".

Test

npm test

The test suite spins up a real local HTTP server standing in for OpenRouter and drives a real child-process MCP server over real stdio — nothing internal is mocked.

License

MIT

Available Tools

1 tool
fusion_askA

Ask OpenRouter Fusion: a panel of models answers your prompt in parallel (with web search), a judge compares them, and one synthesized answer is returned — stronger than any single model. Use for research, expert critique, or any question where being wrong is costly. Costs more than a single call (panel + judge completions).

ParametersJSON Schema
NameRequiredDescriptionDefault
presetNoOptional curated preset: 'general-high' or 'general-budget'. Mutually exclusive with analysis_models.
promptYesThe question or task to send to the Fusion panel.
judge_modelNoOptional model slug for the judge that synthesizes the panel's answers. Defaults to the first panel model.
max_tool_callsNoTool iterations per panel model (1-16, default 8).
analysis_modelsNoOptional 1-8 model slugs forming the panel (e.g. ['anthropic/claude-opus-4-8','openai/gpt-5.5']). Omit to use Fusion's default quality preset.

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well by disclosing the parallel panel behavior, web search, judge comparison, and single synthesized answer. It also surfaces the cost implications. It does not cover edge cases like failure modes or latency, but for the described functionality it is adequately transparent.

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 three sentences, front-loaded with the core purpose, and every sentence contributes (purpose, use cases, cost trade-off). There is no redundancy or filler, making it highly efficient.

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

Completeness4/5

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

Given the absence of an output schema and annotations, the description covers the essential context: it explains what the tool does, when to use it, and its cost. It mentions the return value ('one synthesized answer is returned'). It lacks details on performance or error handling, but these are secondary for a tool of this complexity.

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?

Schema coverage is 100%, setting a baseline of 3. The description adds value by explaining the high-level process (panel of models, judge comparison), which clarifies the role of parameters like judge_model and analysis_models beyond their raw schema definitions. This contextual enrichment justifies a small uplift over the baseline.

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: 'Ask OpenRouter Fusion: a panel of models answers your prompt in parallel...' and explains the process (panel, judge, synthesized answer). It distinguishes itself from a single model call by emphasizing the multi-model synthesis, leaving no ambiguity about what the tool does.

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 explicit use cases: 'Use for research, expert critique, or any question where being wrong is costly.' It also implies a trade-off with cost: 'Costs more than a single call (panel + judge completions),' which serves as a when-not-to-use hint. However, it does not explicitly name alternative tools or provide a formal when-not-to-use list, so it falls just short of a 5.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.0
    • First observedfusion_ask
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TDQS

A4.3/5.0
Disambiguation5/5

With only one tool, there is no possibility of overlapping or confusing tools. The single tool's purpose is clearly defined and distinct.

Naming Consistency5/5

With a single tool, the naming is trivially consistent. The verb_noun pattern (fusion_ask) is clear and well-formed.

Tool Count2/5

A single tool is too few for a typical MCP server, making the tool surface feel sparse. Even if the tool is powerful, the server lacks the breadth expected of a useful toolkit.

Completeness4/5

The server fully covers its stated purpose of asking a fusion panel, but there are minor gaps such as no configuration options or alternative request modes. The core functionality is complete, but some auxiliary features are missing.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources

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