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prism_infer

Run local AI inference using prism-coder models, automatically selecting the largest viable tier (2B-27B) based on available RAM. Falls back to cloud when local fails, saving tokens on successful runs.

Instructions

Run an inference on a local prism-coder model (Ollama) to save cloud tokens. Picks the largest viable tier — 27B / 9B / 4B / 2B — based on free RAM at call time, clamped by model_ceiling and what is actually pulled in Ollama. Falls through to the synalux portal cloud cascade (9B → 27B → Claude Opus 4.7) only when local is unviable AND cloud_fallback=true. Use this for code generation, summarisation, classification, or any synth task you would otherwise hand to the cloud model — it costs $0 when the local hit succeeds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoExecution mode. 'route' (default) for MCP tool routing — fast, nothink. 'chat' for general conversation — uses thinking, escalates to cloud on failure. 'code' for code generation — uses thinking, larger context. In chat/code modes, prefers the 27B tier and enables <think> reasoning.route
thinkNoEnable thinking mode (<think> blocks). Default: true for chat/code, false for route. Thinking improves quality on complex tasks but adds latency (~2-5s).
promptYesThe user prompt. Required.
systemNoOptional system instruction prepended to the prompt.
verifyNoEnable the L3 grounding verifier. Default: true when `evidence` is provided, false otherwise. When enabled, the model's draft is checked by a different model (qwen3.5:4b by default) against the supplied `evidence`. Drafts with NEUTRAL or CONTRADICTED claims are refused.
evidenceNoOptional evidence snippets the model output must be grounded in. When supplied with `verify: true`, every assertive claim in the draft (numbers, names, dates, codes, $ amounts) must be ENTAILED by one of these snippets or the draft is refused.
max_tokensNoMax output tokens (default 1024, hard cap 8192).
timeout_msNoOverride per-call timeout. Default scales with model size: 27B=120s, 9B=60s, 4B=20s, 2B=15s.
temperatureNoSampling temperature, 0 = deterministic (default 0).
model_ceilingNoCap the largest tier the picker may select. e.g. '9b' forbids 27B even if RAM allows.
cloud_fallbackNoIf true, fall through to synalux portal cascade on local fail. Default false — token-saving mode is the point of this tool.
verifier_modelNoOverride the verifier model. Default: qwen3.5:4b.
verifier_timeout_msNoOverride the verifier hard timeout. Default 2000 ms.
Behavior5/5

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

Without annotations, the description fully discloses behavioral traits: tier selection based on RAM, thinking modes, verification with evidence, cloud fallback conditions. It covers safety, cost implications, and edge cases without contradiction.

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 concise (4-5 sentences) with no fluff. It front-loads the core purpose and tier logic, then covers fallback. Every sentence adds value.

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 (13 parameters, no output schema), the description covers model selection, modes, verification, fallback, and parameter defaults. It provides sufficient context for an AI agent to invoke correctly.

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 context beyond the schema by explaining the overall inference flow, tier selection, and mode-specific behaviors, enhancing understanding of parameter interactions.

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 runs inference on a local model to save cloud tokens, selects tiers based on RAM, and falls back to cloud when needed. It specifies use cases like code generation, summarisation, classification, and differentiates from siblings by being the local inference tool.

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 explains when to use the tool (for any synth task that would otherwise go to cloud) and when fallback occurs. However, it does not explicitly mention alternatives or scenarios where this tool should be avoided, leaving some ambiguity for edge cases.

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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