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tboome33

OpenRouter Fusion MCP Server

by tboome33

Fusion (start async)

fusion_start

Launch a multi-model deliberation task in the background, returning a job ID for asynchronous result collection. Configure preset, models, or reasoning effort.

Instructions

Start an OpenRouter Fusion deliberation in the BACKGROUND and return a job_id immediately (never times out), then call fusion_result with the job_id. Pick a config with preset:'' — call fusion_list to see them (quality, budget, + custom configs). Default: quality. reasoning_effort and temperature default to the config's; override per call if needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe question or task to deliberate on.
presetNoConfig name from fusion_list (quality, budget, or a custom one). Default: quality.
systemNoOptional system instruction (overrides the config's).
reasoning_effortNoOverride reasoning effort. Default: config's (quality/custom=high, budget=medium).
temperatureNoOverride sampling temperature. Default: config's (usually the model default).
analysis_modelsNoOverride the config's panel.
judge_modelNoOverride the config's judge/orchestrator.
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that the process runs in background and never times out, and that configs have defaults. However, it does not mention any destructive side effects, rate limits, or required permissions, which would be helpful for full transparency.

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 relatively concise but could be better structured (e.g., bullet points for workflow). It front-loads the key action and avoids unnecessary words, but a more list-like format would improve scannability.

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 7 parameters with full schema coverage, no output schema, and lack of annotations, the description adequately explains usage and context. It mentions sibling tools and default/override logic. However, it does not describe the return format beyond 'returns a job_id', which would be helpful for 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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining default behavior, override relationships, and the concept of config presets. It goes beyond schema by clarifying that reasoning_effort and temperature default to config's and can be overridden.

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 it starts an async deliberation and returns a job_id. It distinguishes from siblings by indicating the async starting nature and referencing fusion_list and fusion_result for config and results.

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?

Explicitly tells when to use fusion_result to retrieve results, and to use fusion_list for config exploration. Also states the behavior (never times out) and how overrides work, covering when-not and alternatives.

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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