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Nityaflow GST credit match

Check an AI agent's payment against the mandate and the instruction

agent_payment_check
Read-onlyIdempotent

Before an AI agent pays, checks the proposed payment against the person's mandate (who allowed it, limits per payment and per period, allowed shops and UPI IDs, end date) and against what the person actually asked for (items, quantities, budget, shop). It works the total out again from the cart lines, and flags lookalike payees, extra items, too much quantity, going over a limit and repeat payments. Returns every check and a receipt signed by Nityaflow. It never pays, holds or moves money: a licensed bank or payment app does that, only after a person approves.

Input Schema

NameRequiredDescriptionDefault
actionYesThe payment the agent proposes: {merchant, vpa, lines: [{name, qty, unit, price}], fees, total}.
intentYesWhat the person asked for: {text, items: [{name, qty, unit}], budget, merchant}. Units: kg, g, l, ml, pc, pack, dozen.
historyNoEarlier payments under this mandate: [{at, total, vpa, status: paid}]. Used for the period limit and repeat check.
mandateYesThe permission: {authorised_by, purpose, per_payment, per_period, period (day, week, month or 90 days), merchants: [{name, vpa, category}], approve_above, valid_from, valid_to}.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the read-only and idempotent annotations, the description discloses concrete behavior: it recomputes totals from cart lines, flags lookalike payees, extra items, quantity issues, limit violations, and repeat payments, and returns every check plus a signed receipt. It also explicitly says the tool never pays, holds, or moves money.

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 front-loaded with the purpose and usage context, then moves through checks, outputs, and the no-money-movement boundary. Every sentence carries useful information, and there is no redundant or wasted text.

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?

Although there is no output schema, the description explains that the tool returns every check and a signed receipt. For a nested-object, read-only verification tool, the combination of annotations, rich schema descriptions, and description-level behavioral detail is complete enough to call it 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 description coverage is 100%, and the nested objects for mandate, intent, action, and history are already well documented. The prose adds useful context about what is checked, but it does not add much parameter-level syntax or meaning beyond what the schema already provides.

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 states a specific verb and resource: checking a proposed AI-agent payment against a mandate and intent. It clearly distinguishes the tool from money-moving operations by saying it never pays, holds, or moves money, and it is understandable independently of its siblings.

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 clearly establishes the usage context: 'Before an AI agent pays.' It also sets a strong capability boundary by explaining that a licensed bank or payment app moves money after human approval. It does not explicitly compare against sibling tools such as verify_receipt, so it falls short of a 5.

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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