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@margint-ai/mcp

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by margint-ai

margint_check_budget

Validate a customer's budget before triggering an LLM call. Returns whether the call is allowed, with any warnings or breaches. Cached server-side for 60 seconds.

Instructions

Check whether a customer is still within their configured monthly budgets. Agents can call this before making an LLM call to self-gate. Returns { allowed: true | false } with any warnings or breaches. Cached 60s server-side.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
customerYesThe customer's external ID.
featureNoOptional feature for feature-scoped budgets.
Behavior4/5

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

With no annotations, the description discloses the return shape ({ allowed, warnings, breaches }) and caching behavior (60s). It implies a read-only check, though it does not explicitly state absence of side effects or authentication needs. Still, it provides key behavioral traits beyond basic purpose.

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?

The description is brief (2.5 lines) and front-loaded with the core purpose. Each sentence adds value: purpose, usage, return, caching. No extraneous text.

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 simple budget-check tool with two parameters and no output schema, the description covers purpose, usage context, return shape, and caching. It lacks error handling details (e.g., missing customer) but is largely complete for the agent to understand invocation.

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% with descriptions for both parameters ('customer external ID' and 'optional feature-scoped'). The description adds no additional meaning beyond what the schema provides, meeting the baseline for covered parameters.

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 checks whether a customer is within monthly budgets, with a specific verb ('check') and resource ('customer budget'). It also distinguishes from sibling tools (cost_overview, get_customer, list_customers) by focusing on budget allowance for self-gating.

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 explicitly tells agents to call this before making an LLM call to self-gate, providing clear when-to-use context. It does not mention when not to use or alternatives, but the self-gating context suffices.

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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