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by bruhsb

paperclip_get_costs_by_agent

Read-only

Retrieve per-agent LLM token costs to pinpoint which agent consumes the most budget. Returns agent ID, name, total cost, and token counts.

Instructions

Get LLM token costs broken down by agent for the current company.

Args:

  • response_format: 'markdown' | 'json' (optional) — Output format (default: markdown)

Returns: Array of per-agent cost records: agentId, agentName, totalCents, tokenCounts.

Examples:

  • Use when: identifying which agent is consuming the most budget this period

  • Don't use when: you need project-level costs — use paperclip_get_costs_by_project instead

Error Handling:

  • 401: authentication failed → check PAPERCLIP_API_KEY

  • 403: permission denied → verify PAPERCLIP_COMPANY_ID is correct

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
response_formatYesOutput format: 'markdown' (default, human-readable) or 'json' (structured)markdown
Behavior4/5

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

Annotations already declare readOnlyHint=true, but description adds error handling details (401, 403) and return structure, which provides useful behavioral context beyond annotations.

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

Conciseness5/5

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

Description is concise with clear sections (Args, Returns, Examples, Error Handling). Only relevant information is included, no fluff.

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

Completeness5/5

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

Given the tool is simple (1 param, no output schema), annotations cover safety, and description covers usage, examples, and errors, everything is complete for an agent to use correctly.

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

Parameters3/5

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

Schema coverage is 100% and parameter is well-described in schema with enum. Description adds context about default and purpose, but does not significantly extend beyond schema information.

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

Purpose5/5

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

Description clearly states 'Get LLM token costs broken down by agent for the current company'. It uses specific verb+resource and distinguishes from sibling tools like paperclip_get_costs_by_project.

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

Usage Guidelines5/5

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

Explicitly says 'Use when: identifying which agent is consuming the most budget this period' and 'Don't use when: you need project-level costs — use paperclip_get_costs_by_project instead', providing clear context and alternative.

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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