Skip to main content
Glama
albeorla

financial-agent

by albeorla

scan_charge_onboarding_candidates

Scan transaction history to discover reviewable charge-pattern candidates, grouping related transactions by merchant and account, and storing candidates in a review queue.

Instructions

Scan transaction history and discover reviewable charge-pattern candidates.

This is the background-discovery entry point: it groups related transactions by merchant and account, proposes schedule/amount/cash-impact/review policies, and stores durable candidates in a review queue. It is idempotent and never writes canonical obligations or moves cash flow. Options support min_evidence, include_inflows, and link_existing_obligations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
optionsNo
db_pathNo
Behavior4/5

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

Discloses important behaviors: idempotent, never writes canonical obligations, never moves cash flow, and stores durable candidates. Without annotations, it provides good transparency, though it could elaborate on what exactly 'durable candidates' entails in terms of storage and side effects.

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?

Concise and well-structured: first sentence states the action, second provides context, and the last line lists options. No extraneous information, every sentence adds value.

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?

Given the tool's complexity and lack of output schema, the description covers purpose, key behaviors, and options. It could mention how to retrieve the generated candidates (e.g., via 'list_charge_onboarding_queue'), but it's largely complete for a background-discovery tool.

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?

The description adds meaning to the 'options' parameter by listing supported fields (min_evidence, include_inflows, link_existing_obligations). However, with 0% schema coverage and two parameters, the 'db_path' parameter remains unexplained, so the description only partially compensates for the missing schema descriptions.

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?

Description clearly states the tool scans transaction history to discover charge-pattern candidates, groups transactions, and stores candidates in a queue. It identifies itself as the background-discovery entry point, distinguishing it from sibling tools like 'list_charge_onboarding_queue' and 'apply_charge_onboarding_candidate'.

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?

Describes the tool as an entry point and notes idempotency and no writes to obligations or cash movement, implying it's a safe first step. However, it does not explicitly state when to use versus alternatives or provide any when-not guidance, leaving some ambiguity.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/albeorla/personal-finance-agent'

If you have feedback or need assistance with the MCP directory API, please join our Discord server