Token Guardian 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 |
|---|---|
| token_guardian_usage_snapshotA | Read local Claude Code and Codex usage, identify large contexts and expensive routing, and return evidence-backed quick wins. Never changes settings or sessions. |
| token_guardian_recommend_routeA | Recommend a conservative Claude, Codex, or local model and effort for one task. Returns advice only and never switches the active client. |
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 2 tools
Each tool serves a distinct, non-overlapping purpose: one provides a usage snapshot with improvement suggestions, the other recommends a routing choice for a task. There is no ambiguity between them.
Both tool names follow a consistent 'token_guardian_<verb>_<noun>' pattern using snake_case, with descriptive verbs ('usage_snapshot', 'recommend_route') that clearly indicate their function.
With only 2 tools, the server feels minimal for a domain that might benefit from additional diagnostics (e.g., cost breakdown, model listing) or configuration advice. The count is on the low end of acceptable for a focused utility.
The server explicitly avoids any action tools, being read-only. It covers snapshot analysis and routing recommendations but lacks tools for detailed queries (e.g., by time period, by model) or applying any changes, leaving notable gaps for hands-on usage optimization.