tokens-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 | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| tsm_classifyA | Classify text into one of the provided labels using a budget model. Returns label, confidence score, and a brief reason. Useful for intent classification, routing decisions, and tagging. |
| tsm_extract_jsonA | Extract structured fields from long text according to a schema description. Returns a JSON object with extracted data and a list of missing fields. Useful for parsing documents, issues, logs. |
| tsm_summarizeB | Compress long text into a concise summary with bullet points and risk flags. Useful for compressing long conversations, logs, documents, or diff context before passing to the main model. |
| tsm_rewriteA | Rewrite text in a different style (concise, formal, technical, friendly, or translate between Chinese and English) without changing the core facts. |
| tsm_codegen_small_patchA | Generate small code snippets or function-level patches using a budget model. Scoped to single functions, regex, SQL, scripts, or unit test samples. NOT for multi-file or architectural designs. |
| tsm_diff_digestA | Compress a git diff into a structured summary of changed areas, behavior changes, risks, and a one-paragraph overview. Helps the main model quickly understand large diffs. |
| tsm_task_extractB | Extract an actionable task list from unstructured text (meeting notes, daily reports, requirements). Returns tasks with optional owner, due date, status, and notes. |
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 7 tools
Each tool has a distinct, non-overlapping purpose: classification, code patch generation, diff summarization, JSON extraction, rewriting, text summarization, and task extraction. No ambiguity between tools.
All tools use the 'tsm_' prefix and snake_case. Most follow a verb_noun pattern (classify, summarize, rewrite), though 'tsm_codegen_small_patch' is slightly less consistent with its compound noun. Overall, naming is clear and predictable.
With 7 tools, the server is well-scoped for a utility focused on token-saving and text processing. Each tool addresses a common need without being excessive or insufficient.
The tool set covers major text operations: classification, extraction, transformation, and summarization. Minor gaps exist (e.g., no data masking or bulk processing), but the core functionality for offloading to budget models is complete.