claude-token-saver-mcp
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| offload_workA | Offload coding/text tasks to a local LLM (Ollama) to save Claude API tokens. Use for code generation, refactoring, formatting, boilerplate, and other routine tasks. |
| compress_contextA | Compress/summarize large text content using a local LLM to reduce cloud token usage. Use for summarizing logs, large files, or verbose context before sending to Claude. |
| cost_dashboardA | View cumulative cost savings and model usage statistics. |
| batch_offloadA | Submit multiple coding tasks as a batch to the local LLM. Tasks are processed sequentially or in parallel. Supports partial failure. |
| get_metricsB | Get server metrics in Prometheus text format or JSON. Includes request counts, latency, queue stats, cost savings, and health status. |
| recommend_modelA | Recommend the best local LLM model for a given task category based on system specs and installed models. Returns prioritized list with installation status and license info. |
| preload_modelA | Preload a model into VRAM for warm inference. Sends an empty chat request with keep_alive to keep the model loaded during the session. |
| list_loaded_modelsA | List all models currently loaded in VRAM with usage details. Shows VRAM usage, expiry time, and available slots. |
| pull_modelA | Download a model from the Ollama registry to local storage. Use this to install recommended models before preloading them into VRAM. |
| configure_model_selectorB | View or modify model selector settings at runtime. Manage blocked models, license filters, and custom model recommendations. |
| auto_setupA | Automate the full model setup flow: recommend the best model for a task category, download it if needed, and preload it into VRAM — all in one step. |
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 11 tools
Each tool has a clearly distinct purpose: metrics, model listing, downloading, task offloading (single and batch), context compression, cost dashboard, model recommendation, VRAM preloading, configuration, and automated setup. No two tools overlap in functionality.
All tools follow the verb_noun snake_case pattern without exception. Names like 'get_metrics', 'offload_work', 'batch_offload', and 'auto_setup' are predictable and consistent.
11 tools is appropriate for a server focused on saving tokens via local LLMs. Each tool addresses a specific aspect (model management, task offloading, monitoring, configuration) without being excessive or insufficient.
The tool set covers the full workflow: model installation, recommendation, preloading, offloading, context compression, and monitoring. Minor gaps exist: there is no tool to unload models from VRAM or delete models, but these are not critical for the primary use case.