mcp-cost-optimizer
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| estimate_llm_costB | Estimate one LLM request from supplied per-million token rates. Input tokens include cached tokens. |
| analyze_llm_usageB | Analyze normalized LLM usage; report costs, unknown models and conditional response-cache opportunities. No network or file access. |
| compare_llm_modelsC | Compare a target model with known-price baseline. Scenario only; requires quality evaluation and does not change routing. |
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 3 tools
Each tool targets a different scope: single-request estimation, aggregate usage analysis, and model comparison. They all relate to cost, but the descriptions make the boundaries clear enough for an agent to select correctly.
All three names follow the same verb_llm_noun pattern: estimate_llm_cost, analyze_llm_usage, compare_llm_models. The convention is predictable and consistent.
Three tools are well-scoped for a narrow cost-analysis server, and each serves a distinct purpose. No unnecessary tools are present.
The surface covers estimation, usage analysis, and model comparison, but an optimizer would benefit from a recommendation or pricing-catalog operation. As named, it analyzes costs rather than completing an optimization lifecycle.