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legifx

ModelRadar MCP

by legifx

modelradar_run

Selects a model matching your task and runs a chat completion via OpenRouter. Returns the answer, the model used, and an estimated cost.

Instructions

Route + execute: pick the best model for the need (or use model if given) and run a chat completion through OpenRouter. Returns the answer, the model it routed to, and an estimated cost. Requires OPENROUTER_API_KEY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNocoding | reasoning | multimodal | general (used to auto-route)
modelNoforce a model: ModelRadar id/name or an OpenRouter id (lab/model)
queryNo
preferNo
promptYes
systemNo
max_tokensNo
multimodalNo
min_contextNo
temperatureNo
open_weightsNo
max_input_priceNo
Behavior3/5

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

No annotations are provided, so the description must carry the transparency burden. It discloses return values (answer, model, estimated cost) and the API key requirement, but doesn't mention potential costs incurred, error handling, or routing fallback behavior, leaving gaps.

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?

Three short sentences, front-loaded with 'Route + execute', and every sentence adds unique value: purpose, return values, and prerequisite. No fluff or redundancy.

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

Completeness2/5

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

With 12 parameters, no annotations, and no output schema, the description is too sparse to fully guide an agent. It doesn't explain routing criteria, parameter interplay, or error cases, though it does mention return values. Significant gaps remain 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.

Parameters2/5

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

Schema description coverage is only 17%, and the description only references 'model' (and vaguely 'the need'). 10 other parameters (prefer, max_tokens, temperature, etc.) remain unexplained, so the description does not compensate for the low schema coverage.

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 'Route + execute' and 'run a chat completion through OpenRouter', differentiating it from sibling tools like modelradar_search or modelradar_get. It specifies a concrete action and resource.

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?

Implies this is the tool for running completions, with auto-routing or forced 'model'. It mentions the API key prerequisite. However, it doesn't explicitly say when not to use or name alternatives, so it falls 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.

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