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revenium

Revenium MCP Server

Official
by revenium

manage_metering

Submit AI transaction metering metadata and retrieve existing transaction records. Access integration guides for Python and JavaScript to implement AI usage metering.

Instructions

Submit AI transaction metering metadata and lookup existing AI transactions metered by Revenium. Receive guidance for writing new integrations to Revenium's AI metering API using python and typescript. Key actions: submit_ai_transaction, lookup_transactions (requires transaction IDs), lookup_recent_transactions (browse without IDs), get_integration_guide, list_ai_models, validate. Supports Python and JavaScript integration examples. Use get_capabilities() for full guidance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionNoget_capabilities
modelNo
providerNo
input_tokensNo
output_tokensNo
duration_msNo
organization_idNo
subscription_idNo
product_idNo
pageNo
sizeNo
queryNo
dry_runNo
example_typeNo
languageNo
use_caseNo
textNo
descriptionNo
transaction_idNo
transaction_idsNo
wait_secondsNo
return_transaction_dataNo
max_retriesNo
retry_intervalNo
search_page_rangeNo
page_sizeNo
early_terminationNo
subscriberNo
trace_idNo
task_typeNo
agentNo
is_streamedNo
response_quality_scoreNo
stop_reasonNo
time_to_first_tokenNo
Behavior2/5

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

No annotations exist, and the description fails to disclose behaviors like idempotency, error handling, or side effects. It mentions integration guides but not operational traits.

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

Conciseness3/5

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

The description is moderately concise but includes a potential inconsistency (python/typescript vs. Python/JavaScript) and redundant enumeration of actions.

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

Completeness2/5

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

With 35 undocumented parameters, no output schema, and no annotations, the description is insufficient for an agent to invoke the tool correctly. It relies on get_capabilities() for full guidance.

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 coverage is 0%, and the description provides no explanation of any of the 35 parameters. It only mentions high-level actions, leaving parameter usage completely unspecified.

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 clearly states it submits and looks up AI transaction metering metadata, listing key actions. It distinguishes from siblings by focusing on AI metering, but the name 'manage_metering' is vague.

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

Usage Guidelines3/5

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

It provides some guidance for specific actions (e.g., lookup_transactions requires IDs, lookup_recent_transactions browses without), but lacks when-not-to-use or alternatives relative to sibling tools.

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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