Token-Efficient MCP Server
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 |
|---|---|
| execute_codeB | Execute code in sandboxed environment. Supports Python, Bash, Node.js. Now with heredoc support for bash scripts (<<EOF, <<'EOF', <<EOT). Achieves 98%+ token savings by processing in execution environment. |
| process_csvA | Process CSV files efficiently with filters, groupby aggregation, and pagination. Use offset for large files (>10K rows) to achieve 99% token savings. |
| process_logsC | Process log files efficiently with pattern matching and pagination. Use offset to skip previous matches. |
| list_token_efficient_toolsB | List available token-efficient tools with progressive disclosure (names_only, summary, full) |
| get_token_savings_reportB | Get detailed token savings report and optimization best practices |
| search_toolsC | Search available tools by keyword with optional category filter. Achieves 95% token savings vs loading all tool definitions. |
| batch_process_csvB | Process multiple CSV files in a single call with consistent filtering. Achieves 80% token savings for multiple files vs individual calls. |
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
Most tools have distinct purposes, such as batch_process_csv for multiple files, process_csv for single files, and execute_code for code execution. However, list_token_efficient_tools and search_tools could be confused as both involve tool discovery, though search_tools adds keyword filtering.
All tool names follow a consistent verb_noun pattern using snake_case, such as batch_process_csv, execute_code, and get_token_savings_report. This uniformity makes the set predictable and easy to navigate.
With 7 tools, the count is well-scoped for a server focused on token-efficient operations. Each tool serves a clear purpose, such as processing files, executing code, and reporting savings, without being overly sparse or bloated.
The tool set covers key areas like file processing (CSV/logs), code execution, and tool discovery, with a focus on token efficiency. A minor gap is the lack of tools for updating or deleting processed data, but agents can work around this given the server's optimization-centric domain.