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AI Model Advisor MCP Server

AI Model Advisor MCP Server

by Semicolon-D
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

batch_get_pricing

Fetch pricing for multiple AI models in a single call, receiving a compact comparison table. Avoids slow per-model lookups.

Instructions

Get pricing for multiple models in a single call. Returns a compact table with pricing for all requested models. Much faster than calling get_model_info one at a time.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model_ids": "<model_ids>"
    },
    "name": "batch_get_pricing"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_idsYesArray of model IDs to get pricing for. Example: ["fal-ai/flux-pro/v1.1", "fal-ai/flux/schnell", "openai/gpt-4o"]

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.3.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It mentions returning a compact table, which gives some idea of the return format, but does not disclose potential error behavior, rate limits, or whether data is live or cached. For a simple read-only batch tool, this is acceptable but not thorough.

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 two sentences, front-loaded with the main action, and every sentence adds useful information. No filler or repetition.

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 simple input (one array parameter) and no output schema, the description covers the core use case and return behavior sufficiently. It could be more explicit about handling unknown model IDs or the exact response structure, but it is largely complete for this straightforward batch retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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

The schema has 100% coverage for the single parameter model_ids, including a description and example. The tool description adds no semantic detail beyond the schema, so the baseline of 3 applies.

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 gets pricing for multiple models in one call, which is a specific verb+resource. It also distinguishes itself from get_model_info by highlighting the batch aspect and speed advantage.

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 explicitly compares to get_model_info and states when this tool is preferable ('Much faster than calling get_model_info one at a time'). It does not list exclusions or alternative tools like estimate_cost, but the primary use case is clear.

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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