Tokenomics MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ANTHROPIC_API_KEY | No | Optional API key for Anthropic's exact token counting. If not set, a tiktoken-based approximation is used. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| count_tokensA | Count how many tokens a piece of text would use for a given model. Args: text: Your text/prompt to tokenize model: Model name, e.g. "claude-sonnet-5", "gpt-5". Call list_supported_models to see all options. |
| estimate_costA | Estimate the API cost of sending this text as input, plus optional expected output. Args: text: The input text/prompt model: Model name, e.g. "claude-sonnet-5", "gpt-5" expected_output_tokens: Rough guess at response length (default 0 = input only) |
| compare_models_costA | Compare token count and estimated cost for the same text across several models. Args: text: The input text/prompt models: List of model names to compare. Defaults to a mix of current Anthropic and OpenAI models if not specified. expected_output_tokens: Rough guess at response length, applied to all models |
| list_supported_modelsA | List all models this server has pricing data for, with their current rates. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: counting tokens, estimating cost, comparing costs across models, and listing supported models. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern: count_tokens, estimate_cost, compare_models_cost, list_supported_models. The naming convention is uniform and predictable.
Four tools is well-scoped for this server's purpose. Each tool addresses a core aspect of tokenomics (counting, costing, comparing, and model discovery) without unnecessary redundancy.
The tool set covers the full lifecycle of token cost analysis: list available models, count tokens, estimate cost, and compare across models. There are no obvious missing operations for the declared purpose.