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tokencost-dev

tokencost-dev

by atriumn
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Install in 30 seconds

Claude Code:

claude mcp add tokencost-dev -- npx -y tokencost-dev

Then ask: "How much would 1M input tokens cost on claude-sonnet-4-5?"

Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "tokencost-dev": {
      "command": "npx",
      "args": ["-y", "tokencost-dev"]
    }
  }
}

No API keys. No accounts. No configuration files. Pricing data is fetched from the LiteLLM community registry and cached locally for 24 hours.

Related MCP server: UsageWall

Tools

get_model_details

Look up pricing, context window, and capabilities for any model. Fuzzy matching means "sonnet 4.5" works just as well as "claude-sonnet-4-5-20250514".

> "What are Claude Sonnet 4.5's pricing and capabilities?"

Model: claude-sonnet-4-5
Provider: anthropic | Mode: chat

Pricing (per 1M tokens):
  Input:  $3.00
  Output: $15.00

Context Window:
  Max Input:  200K
  Max Output: 8K

Capabilities: vision, function_calling, parallel_function_calling

calculate_estimate

Estimate cost for a given number of input and output tokens.

> "How much will 1000 input + 500 output tokens cost on Claude Sonnet 4.5?"

Cost Estimate for claude-sonnet-4-5

  Input:  1K tokens × $3.00/1M  = $0.003000
  Output: 500 tokens × $15.00/1M = $0.007500
  ─────────────────────────────
  Total:  $0.0105

compare_models

Find the most cost-effective models matching your requirements.

> "What are the cheapest OpenAI chat models?"

Top 2 most cost-effective models (provider: openai) (mode: chat):

1. gpt-4o-mini
   Provider: openai | Mode: chat
   Input: $0.15/1M | Output: $0.60/1M
   Context: 128K in / 16K out

2. gpt-4o
   Provider: openai | Mode: chat
   Input: $5.00/1M | Output: $15.00/1M
   Context: 128K in / 16K out

refresh_prices

Force re-fetch pricing data from the LiteLLM registry (cache is refreshed automatically every 24h).

Docs

Full documentation at tokencost.dev

License

MIT

Available Tools

4 tools
calculate_estimateA

Estimate the cost for a given number of input and output tokens on a specific model. Supports optional cached_tokens for prompt caching discounts.

Inputs: model_name (required), input_tokens (required), output_tokens (required), cached_tokens.

compare_modelsA

Filter and compare models by provider, minimum context window, or mode. Returns top 5 most cost-effective matches.

Inputs: provider, min_context, mode.

get_model_detailsA

Look up pricing, context window, and capabilities for an LLM model. Uses fuzzy matching so you don't need the exact model key.

Inputs: model_name (required).

refresh_pricesA

Force a re-fetch of pricing data from the LiteLLM registry. Use this if you suspect the cached data is stale.

Inputs: no named parameters.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

    Deploy Server
    Claude Code, inside the Godot 4 editorFlockbay puts an AI agent in the real Godot 4 editor: it writes GDScript, runs your game and fixes what breaks. Free, or bring your own Claude Code or Codex.flockbay.comAd

    TDQS

    A4.1/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool has a distinct, non-overlapping purpose: estimate cost, compare models, get model details, and refresh pricing data. No confusion possible.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (e.g., calculate_estimate, compare_models), making them predictable and easy to understand.

    Tool Count5/5

    With 4 tools, the set is appropriately scoped for a token cost estimation server. Each tool serves a necessary function without bloat.

    Completeness4/5

    The tool set covers the core lifecycle: estimate costs, compare models, retrieve details, and refresh data. Minor gap: no explicit listing of all available models, though get_model_details uses fuzzy matching.

    Maintenance

    ActivitySlowing
    ResponsivenessNo recent community issues

    Resources

    Related MCP Connectors

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