Skip to main content
Glama

Generate pain XML from records

generate_message
Read-onlyIdempotent

Generate validated ISO 20022 pain XML messages from in-memory payment records. Returns XSD-validated XML string or error.

Instructions

Generate a validated ISO 20022 pain XML message from in-memory records.

This is the primary generation tool: pass records you already hold in
memory. Use ``generate_message_from_file`` when the data lives in a CSV
on disk, and ``generate_message_async`` for very large batches you want
to run off the event loop. The result is XSD-validated before return; no
file is written.

Returns the validated XML document as a string, or a JSON-encoded
``{"error": ...}`` payload if generation fails.

Args:
    message_type: A supported ISO 20022 pain message type.
    records: One or more flat payment records.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recordsYesOne or more flat payment records (each a dict of field name → value) to render into the XML; validate them first with validate_records. See get_input_schema for the fields.
message_typeYesA supported ISO 20022 pain message type. Must be exactly one of: 'pain.001.001.03', 'pain.001.001.04', 'pain.001.001.05', 'pain.001.001.06', 'pain.001.001.07', 'pain.001.001.08', 'pain.001.001.09', 'pain.001.001.10', 'pain.001.001.11', 'pain.001.001.12', 'pain.008.001.02' (see list_message_types).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that the result is XSD-validated before return and no file is written, which provides helpful behavioral context beyond the 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 concise, well-structured with clear sections, and front-loads the core purpose. Every sentence adds value without verbosity.

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?

Given the tool's simplicity (2 params, clear output as string or error), the description provides complete context: inputs, behavior, alternatives, return format, and validation steps. The existence of an output schema further complements completeness.

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

Parameters4/5

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

Schema coverage is 100% with good parameter descriptions. The description adds value by pointing to list_message_types for valid enum values and suggesting pre-validation with validate_records and consulting get_input_schema, which aids correct usage.

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 generates a validated ISO 20022 pain XML from in-memory records, with a specific verb and resource. It distinguishes itself from siblings by explicitly naming alternatives (generate_message_from_file, generate_message_async) and their use cases.

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?

The description provides explicit guidance on when to use this tool versus siblings: pass in-memory records here, use generate_message_from_file for CSV disk data, and generate_message_async for large batches. This is clear and actionable.

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/sebastienrousseau/pain001-mcp'

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