mcp-context-condenser
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 |
|---|---|
| condense_sourceA | Extracts an AST-aware structural skeleton of a source file (preserves imports, interfaces, types, and function/method signatures while stripping implementation bodies) to slash token consumption by up to 85%. |
| extract_symbolA | Surgically extracts only the exact implementation of a named function, class, method, or interface along with top-level imports, omitting the rest of the file to save tokens. |
| compress_logA | Filters and compresses verbose test runs, build logs, and compiler outputs (Jest, Vitest, tsc, cargo) to extract only the root-cause failures and actionable file locations, discarding passing suites and noise. |
| token_budget_viewA | Analyzes the line count and estimated LLM token weight of a source file, providing actionable recommendations to avoid context saturation. |
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
Most tools target clearly distinct inputs: source files, individual symbols, logs, and token analysis. Condense_source and extract_symbol both reduce source token usage, but their different approaches (structural skeleton vs. exact symbol extraction) are described well enough to distinguish them.
Three tools follow a clean verb_noun snake_case pattern (condense_source, extract_symbol, compress_log). Token_budget_view breaks the pattern by leading with the noun rather than an action verb, creating a minor inconsistency.
Four tools is an ideal size for a focused context-condensing utility. Each tool serves a distinct, practical purpose and none feel redundant or unnecessary.
The set covers source reduction, symbol extraction, log compression, and token-budget analysis, which addresses the main context-overload scenarios. Minor gaps exist, such as no multi-file or project-wide condensation tool, but the core workflows are well covered.