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LLM Usage & Cost Tracker

LLM Usage & Cost Tracker

by zhaoyue722
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_providers

List all providers, their models, and OpenAI-compatibility flags for comparing LLM costs and options.

Instructions

List every provider we know about, with their models and OpenAI-compat flag.

Sources the provider/model lists from pricing_snapshot, so a provider whose pricing hasn't been seeded simply doesn't appear. After bootstrap() runs on a fresh install this includes every v1 provider (anthropic, openai, qwen, deepseek). Order is alphabetical by provider, then by model within each provider.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_providers"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
providersYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the list depends on pricing_snapshot, that unseeded providers are omitted, and ordering is alphabetical. These are useful behavioral traits for a read-only list operation.

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?

The description is clear and front-loaded with the main purpose. It uses multiple sentences but each provides necessary detail about source, provider selection, and ordering. Could be slightly more terse, but remains efficient.

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

Completeness5/5

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

Given that an output schema exists (not shown, but indicated), the description does not need to detail return values. It explains the source, ordering, and which providers appear. For a zero-parameter list tool, this is complete and informative.

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?

The input schema has no parameters, so the description compensates by explaining the output content (models, flag) and ordering. This adds value beyond the empty schema, earning a baseline of 4.

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 'List every provider we know about, with their models and OpenAI-compat flag.' It specifies the action (list) and resource (providers), and adds details about what is included (models, flag) and ordering. This distinguishes it from sibling tools like compare_providers and get_pricing.

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 description explains that providers appear only if pricing is seeded, and after bootstrap it includes specific providers. However, it does not explicitly state when to use this tool versus alternatives, such as when to use compare_providers or get_pricing instead.

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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