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monzo-mcp

by partymola
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Python
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monzo_spending

Analyze Monzo spending from cached transactions, with filters for month, category, and account type. Returns category summaries or individual transactions to identify spending patterns.

Instructions

Analyse spending from cached Monzo transactions.

Auto-syncs if the cache is stale (last sync before today).

Every result carries account_type, echoing the filter applied and null when unfiltered, so a zero total says which account it measured.

Args: month: Month in YYYY-MM format (default: current month) category: Filter by category, e.g. "groceries", "eating_out", "transport" account_type: "personal" or "joint" (default: all) detail: If true, return individual transactions instead of category summary

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
monthNo
detailNo
categoryNo
account_typeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.9.0
    • changedInput schema / properties / account_type / anyOf
      Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "enum": [
+      "personal",
+      "joint"
+    ],
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
  2. First observedv0.1.0

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 behavioral burden. It discloses the auto-sync side effect, the output behavior (account_type echo, null when unfiltered), and the detail switch affecting the return format. This goes beyond the schema and provides useful context, though it does not mention potential side effects like rate limits or error handling. The disclosure is substantial and does not contradict any annotations.

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 well-structured: a clear summary sentence, a brief note on caching, an explanation of output behavior, and a concise Args list. It is front-loaded with the core purpose, and every sentence adds value. It is appropriately sized for a tool with four parameters and behavioral nuances, with no redundant phrasing.

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?

The description covers the tool's purpose, caching behavior, output characteristics, and all parameter semantics. It does not explicitly detail the return schema, but an output schema is available, so that is acceptable. It lacks explicit guidance on when to use this versus siblings, but the purpose is clear enough. Overall, it is nearly complete for an analysis tool, with only minor gaps.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. The Args section provides format (YYYY-MM for month), examples (category values), default behavior (current month, all accounts), and the effect of detail (individual transactions vs summary). This adds rich meaning beyond the bare schema titles and types, fully satisfying the requirement.

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 opens with 'Analyse spending from cached Monzo transactions,' which states a specific verb (analyse), a resource (spending from Monzo transactions), and the caching behavior. This clearly distinguishes it from siblings like monzo_list_transactions (which would list raw transactions) and monzo_get_balance (which reads a balance). The purpose is unambiguous.

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?

The description explains what the tool does and mentions auto-sync when the cache is stale, but it does not explicitly state when to prefer this tool over alternatives like monzo_search_transactions or monzo_list_transactions. The usage context is implied by the purpose (spending analysis) but not directly contrasted with siblings, leaving some room for ambiguity.

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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