ai-usage-metrics-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 | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| log_model_callA | Log a model call for tracking AI usage metrics. Call this after each model invocation to record the interaction. |
| search_model_callsA | Search for logged model calls with optional filters. Returns calls sorted by timestamp (most recent first). |
| list_sessionsB | List session summaries showing aggregated metrics for each session. Sessions are groups of related model calls. |
| get_session_callsB | Get all model calls for a specific session, sorted by timestamp (oldest first). |
| get_aggregate_metricsB | Get aggregate metrics (call count, total tokens, average latency) across model calls with optional filters. |
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 5 tools
Each tool has a distinct primary purpose: write (log_model_call), search raw calls (search_model_calls), list session summaries (list_sessions), fetch session-scoped calls (get_session_calls), and aggregate stats (get_aggregate_metrics). However, get_session_calls overlaps with search_model_calls since a session filter could achieve the same result, and search/aggregate both operate on the same call set with filters.
All names follow a consistent snake_case verb_noun pattern (log_, search_, list_, get_, get_). Verbs vary appropriately by operation while the structure stays predictable and readable.
Five tools are well-scoped for a focused usage-metrics server: one write path, three read/query paths, and one aggregation path. Each tool earns its place without redundancy or gaps in count.
The core lifecycle is covered: logging calls, retrieving them, session grouping, and aggregation. Minor gaps exist around deletion/retention and explicit session creation, but agents can work around these for typical metrics workflows.