@argosvix/mcp-server

raise_llm_budget

Adjust your monthly LLM feature budget between $5 and $500 in $0.01 increments. Set a new budget to manage spend and avoid overruns.

Instructions

Raise or lower the monthly LLM feature budget (Pro+ only). Range $5 - $500 (hard cap against runaway spend), in $0.01 increments. Existing spend carries over; auto-resets at month boundaries. Example phrasing: "we hit 80% of the budget — raise it to $30 just for this month" / "we overspent — lower next month to $10". A new value below current spend is accepted (remaining simply becomes 0; counting restarts from 0 next month).

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": {
      "budgetUsd": "<budgetUsd>"
    },
    "name": "raise_llm_budget"
  }
}

Input Schema

Name	Required	Description	Default
`budgetUsd`	Yes	New monthly budget in USD (5-500, $0.01 increments). E.g. 30 / 50.5 / 100

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.30.0-alpha.14Jul 8, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Since no annotations exist, the description carries full burden. It discloses the range ($5-$500), increments, carry-over of existing spend, monthly resets, and behavior when setting below current spend. No contradictions.

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 well-structured: purpose first, then details, then examples. Every sentence adds value with no redundancy. Slightly longer but appropriate for the content.

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

Completeness4/5

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

For a single-parameter tool with no output schema, the description covers purpose, constraints, behavioral nuances, and usage examples. It is sufficient for an AI agent to select and invoke correctly.

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

Parameters4/5

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

Schema coverage is 100% with a description for budgetUsd. The description adds context like 'hard cap against runaway spend', carry-over behavior, and concrete examples, which enrich understanding beyond the schema.

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?

The description clearly states the tool's action ('Raise or lower') and the resource ('monthly LLM feature budget'). It distinguishes from siblings like 'get_llm_budget' by specifying it modifies the budget, and the 'Pro+ only' constraint adds specificity.

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

Usage Guidelines4/5

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

The description provides clear usage context with examples ('we hit 80% of the budget') and mentions the 'Pro+ only' eligibility. It lacks explicit when-not-to-use but the examples guide appropriate scenarios.

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

Deploy Server