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tokencast

tokencast

by krulewis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

report_session

Report actual session cost to refine future calibration of LLM cost estimates. Provide turn count, review cycles, and step actuals for accurate adjustments.

Instructions

Report actual session cost to improve future calibration

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "actual_cost": "<actual_cost>"
    },
    "name": "report_session"
  }
}

Input Schema

NameRequiredDescriptionDefault
turn_countNo
actual_costYes
step_actualsNo
review_cycles_actualNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It states the action but does not disclose any behavioral traits like whether this is a read-only operation, data transmission, or any side effects. No detail is provided about how the report is stored or used.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no filler words, focusing on the core action and purpose. It is appropriately brief, though it under-specifies details that are penalized in other dimensions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has four parameters, one required, and a nested object, with no output schema or annotations. The description gives no information about required inputs, format, or expected behavior, making it inadequate for correct usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not mention any parameters. The tool has four parameters including a required actual_cost and a nested step_actuals object, but the description fails to explain their meaning or relationships.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Report') and resource ('actual session cost'), and notes the purpose of improving calibration. It doesn't explicitly distinguish from sibling tools like report_step_cost, but the session-level scope provides some differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives such as report_step_cost or estimate_cost. The phrase 'to improve future calibration' hints at a post-session reporting context, but no exclusions or alternatives are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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