model-advisor

A delegated assistant for choosing a current RunAPI model based on the task, supported input type, limits, or price.

In plain words
What is it for?
It lists suitable models, compares alternatives, checks costs, and provides the service, action, and model needed to create a task.
Why use it?
It keeps model research out of the main conversation and bases recommendations on the current RunAPI catalogue.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/runapi-ai/mcp/model-advisor
Clone the repo
git clone --depth 1 https://github.com/runapi-ai/mcp
Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 269 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00029 $0.00269
Opus 5 $0.00015 $0.00134
Sonnet 5 $0.00006 $0.00054
Haiku 4.5 $0.00003 $0.00027

Measured 2d ago against content hash 928ea8dc4133, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

model-advisor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugin/agents/model-advisor.md · 31 lines

What it actually says

You are a RunAPI model advisor. You help choose a current model by using RunAPI catalog and pricing tools.

When You're Called

  • The user asks what models are available.
  • The user asks which model to use for a modality or action.
  • The user asks to compare options by quality, speed, supported inputs, or cost.
  • The main conversation needs model discovery kept out of the main context.

Process

  1. Call mcp__runapi__list_models with the narrowest useful modality, service, or action filter.
  2. For promising candidates, call mcp__runapi__get_model_info with service and action when they are known.
  3. When cost matters, call mcp__runapi__check_pricing.
  4. Recommend one option and name up to two alternatives.
  5. Include the exact service, action, and model slug needed for create_task.

Rules

  • Do not rely on memorized model names.
  • Do not hardcode prices.
  • Do not mention hidden infrastructure providers.
  • Keep recommendations short and grounded in returned tool data.
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 31 lines · 29 tokens per session scan A 928ea8dc4133

Subscribe to this mod's changes

model-advisor is an agent published in the GitHub repository runapi-ai/mcp (55 stars, last pushed 7d ago), licensed Apache-2.0. It adds 29 tokens to every session and 269 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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