mcp.xynaptic

bank-transaction-categorize

Xynaptic Bank Transaction Categorize — categorize raw bank transactions (CSV or objects) into standard categories with merchant and income/expense detection. POST { transactions: [{date, label, amount}] } or { csv }. Usage: Use ONLY for raw unlabelled transactions. If they are already categorized, skip this. Deeper analytics (bank-cashflow, bank-financial-health, bank-payment-risk) are available through the xynaptic-catalog tool. [price: $0.020 per call, x402/USDC]

Input Schema

Name	Required	Description	Default
`body`	Yes	JSON request body (POST endpoint)
`params`	No	optional query parameters

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedOct 3, 2026

changedInput schema / properties / body / properties / transactions / description

Previous value: -"example: [{\"date\":\"2026-09-01\",\"label\":\"CAFE DE LA GARE PARIS\",\"amount\":-4.2},{\"date\":\"20"New value: +"required. example: [{\"date\":\"2026-09-01\",\"label\":\"CAFE DE LA GARE PARIS\",\"amount\":-4.2},{\"date\":\"20"

addedInput schema / properties / body / required
```
Added value: +[
+  "transactions"
+]
```

changedInput schema / required

Previous value: -[]New value: +[
+  "body"
+]

Changed4 schema fields changedOct 2, 2026

changedInput schema / properties / body / description

Previous value: -"JSON body for POST endpoints (e.g. insurance, ai-chat, production-risk)"New value: +"JSON request body (POST endpoint)"

addedInput schema / properties / body / properties

Added value: +{
+  "output": {
+    "description": "example: [{\"category\":\"food_and_drinks\",\"merchant\":\"Cafe de la Gare\",\"kind\":\"expense\",\"re",
+    "type": "string"
+  },
+  "transactions": {
+    "description": "example: [{\"date\":\"2026-09-01\",\"label\":\"CAFE DE LA GARE PARIS\",\"amount\":-4.2},{\"date\":\"20",
+    "type": "string"
+  }
+}

changedInput schema / properties / params / description

Previous value: -"query parameters, e.g. {city: 'Paris', type: 'Appartement'}"New value: +"optional query parameters"

addedInput schema / required
```
Added value: +[]
```

First observedOct 1, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the cost ($0.020 per call, x402/USDC) and the accepted input formats (JSON transactions array or CSV). It also implies a stateless compute operation but does not detail output structure, error handling, or idempotency—minor gaps for a simple categorization tool.

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 front-loaded with the core purpose and usage, then alternatives and cost. It is efficient overall, though the opening restates the tool name and the bracketed price at the end feels tacked on, slightly breaking flow.

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 tool with no annotations, no output schema, and nested input, the description covers purpose, usage constraints, alternatives, pricing, and input formats. It stops short of describing the exact return shape or failure modes, but those are less critical for this simple categorization operation.

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%, so the schema already documents both parameters. The description clarifies the main payload shape (transactions array or CSV alternative), which adds marginal value, but it does not explain the 'output' or 'params' fields—leaving the structured schema to do the heavy lifting as expected at baseline 3.

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 (categorize) and resource (raw bank transactions), and specifies the outcome: standard categories with merchant and income/expense detection. It also distinguishes itself from deeper analytics tools by pointing to xynaptic-catalog, so an agent can tell what it is and is not for.

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?

It explicitly says 'Use ONLY for raw unlabelled transactions. If they are already categorized, skip this,' giving both a when-to-use and a when-not-to-use condition. It also names the alternative (deeper analytics via xynaptic-catalog), leaving no ambiguity about routing.

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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