Track your session usage and measure LLM costs. View totals for local LLM calls, tokens consumed on the cheap model, and bytes saved through server-side delegation.
Session totals: local LLM calls, tokens consumed on the cheap model, and delegated_bytes (bytes of file content processed server-side that never entered your context — i.e. the savings).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses the meaning of delegated_bytes (server-side processed content that never entered context, i.e. the savings), which is genuinely useful context. However it is silent on read-only guarantees, session scope boundaries, or reset behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single dense sentence that front-loads 'Session totals' and enumerates its three metrics with a clarifying parenthetical for the only non-obvious one. No filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description must carry the return-value burden, and it defines the three headline metrics including delegated_bytes. It is nearly complete for a zero-parameter read tool, though it does not state the session/time window precisely or the exact shape of the returned values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so the baseline of 4 applies. The schema has no parameters to document and the description appropriately focuses on what the metrics mean rather than inventing input semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the resource (session totals) and enumerates the specific metrics returned: local LLM calls, tokens on the cheap model, and delegated_bytes. It is clearly a read-only stats retrieval tool, distinguishable from siblings like count_tokens or get_system_resources, though it does not explicitly contrast itself with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no statement of when to call this tool, when not to, or which sibling to prefer for related questions (e.g. count_tokens, get_system_resources, get_job). The agent must infer usage purely from the metric names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.