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fal.ai MCP Server

fal.ai MCP Server

by thenavidm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Pricing

get_pricing
Read-onlyIdempotent

Fetches unit pricing for specified model endpoint IDs to compare rates and estimate generation costs.

Instructions

Returns unit pricing for requested endpoint IDs. Most models use output-based pricing (e.g., per image/video with proportional adjustments for resolution/length). Some models use GPU-based pricing depending on architecture. Values are expressed per model's billing unit in a given currency.

Authentication: Required. Users must provide a valid API key. Custom pricing or discounts may be applied based on account status.

Common Use Cases:

  • Display pricing in user interfaces

  • Compare pricing across different models

  • Build cost estimation tools

  • Check current billing rates

See fal.ai pricing for more details.

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": {
      "endpoint_id": "<endpoint_id>"
    },
    "name": "get_pricing"
  }
}

Input Schema

NameRequiredDescriptionDefault
accountNoExact private account key profile label, not an authenticated provider owner ID.
endpoint_idYesFilter by specific endpoint ID(s). Accepts 1-50 endpoint IDs. Supports comma-separated values: ?endpoint_id=model1,model2 or array syntax: ?endpoint_id=model1&endpoint_id=model2

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover the read-only, idempotent, non-destructive profile, and the description adds genuine context on top: authentication is required and custom pricing/discounts may apply based on account status. It also discloses that pricing varies by billing model (output-based vs GPU-based), which is useful behavioral information not in the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core return meaning, then auth, then use cases, with an external link at the end. Well organized and mostly tight, though the use-case bullets and marketing-style bolding add some length without proportional value.

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?

There is no output schema, so the description usefully explains that values are expressed per the model's billing unit in a given currency. Auth requirements and pricing-model variability are covered; only the account parameter's role and the response shape remain unaddressed.

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?

Schema description coverage is 100%, so endpoint_id and account are already fully documented in the schema (including the 1-50 ID limit and comma-separated vs array syntax). The description adds no parameter-level meaning beyond that, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Returns unit pricing for requested endpoint IDs', and explains that output is per billing unit in a currency. The purpose is unambiguous, but it never distinguishes itself from the close sibling estimate_pricing, leaving the agent to infer which one to call.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Common Use Cases' list (display pricing, compare models, build cost tools, check rates) implies when the tool is useful but is framed as use cases rather than when-to-use/when-not conditions. It never states the key routing rule against estimate_pricing, which is the most likely alternative for cost-related queries.

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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