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KhushalB25

llm-fallback-mcp

by KhushalB25

complete

Completes a prompt by trying OpenAI, Anthropic, then Gemini, with automatic fallback and retry on rate limits or errors, returning the response and attempt log.

Instructions

Complete a prompt with automatic provider fallback. Tries OpenAI -> Anthropic Claude -> Google Gemini in order. Each provider gets one retry on rate limit / 5xx / network errors with backoff. Returns the response, the provider that succeeded, and a full per-provider attempt log. Requires at least one of OPENAI_API_KEY, ANTHROPIC_API_KEY, or GEMINI_API_KEY in the environment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNoOptional provider order. Defaults to ['openai', 'anthropic', 'gemini'].
promptYesThe user prompt to send.
max_tokensNoMax output tokens. Default 1024.
temperatureNoSampling temperature 0-2. Default 0.5.
model_overridesNoOptional per-provider model id override. Defaults: openai=gpt-4o-mini, anthropic=claude-haiku-4-5-20251001, gemini=gemini-2.0-flash.
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: fallback order, retry policy (one retry on specific errors with backoff), return structure (response, provider, attempt log), and required environment variables.

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?

Two sentences, no fluff, key information front-loaded. Every sentence serves a clear purpose.

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 complexity (5 params, nested objects, no output schema), the description covers what the tool does, fallback, retries, return items, and prerequisites. It lacks explicit output schema but provides enough for agent understanding.

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 description coverage is 100% with good individual parameter descriptions. The tool description adds value by explaining defaults and fallback behavior context, complementing the schema.

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 'complete a prompt with automatic provider fallback' and lists the fallback order. It distinguishes itself from the only sibling tool 'health_check', which serves a very different purpose.

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 completing prompts with fallback) and notes required environment variables. It does not explicitly contrast with 'health_check' but the distinction is evident.

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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