cashflow-mcp-server

Get a contract template

cashflow_model_template

Read-onlyIdempotent

Return a ready-to-edit, valid contract model for cash flow analysis. Choose managed_service, subscription, or equipment_lease to avoid schema errors and speed up modeling.

Instructions

Return a filled-in, valid contract model to copy and edit.

Call this first when you are about to build a model and are unsure how the fields fit together. Editing a working example is faster and safer than assembling one from the schema, and the returned object is guaranteed to validate.

Three shapes are available:

'managed_service': exercises every field, including metered streams, staged investments, a financed asset with interest, maintenance and working capital.
'subscription': flat fee, no metered volume, no financed asset.
'equipment_lease': fully metered revenue, interest-free installments, residual value.

Args:

template ('managed_service' | 'subscription' | 'equipment_lease'): Which shape. Default 'managed_service'.

Returns: model (the object to pass to the other tools), notes (what the shape is for), and conventions (the rules that govern every model).

Examples:

Use when: "Help me model a service contract" and no model exists yet.
Use when: you need to check what a field is called or how it is shaped.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`template`	No	Which shape to return.	managed_service

Output Schema

TableJSON Schema

Name	Required	Description	Default
`model`	No
`notes`	Yes
`template`	Yes
`conventions`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 22, 2026

TDQS

A5/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds valuable behavioral context: the returned object is 'guaranteed to validate,' includes model/notes/conventions, and each template shape's coverage is described. No contradiction exists.

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 with a lead sentence, a 'when to use' rationale, bulleted shape explanations, args/returns sections, and examples. Every sentence adds useful information, and the most important guidance is front-loaded.

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?

For a single-parameter, high-level template tool with an output schema, the description is complete: it covers purpose, expected return shape, template options, defaults, and usage context. Nothing an agent needs to know before calling it is missing.

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?

Although the schema already documents the single template parameter at 100% coverage, the description enriches each enum value with meaning: 'managed_service' exercises every field, 'subscription' has flat fee/no metered volume, 'equipment_lease' has fully metered revenue. It also clearly states the default.

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 a specific verb and resource: 'Return a filled-in, valid contract model to copy and edit.' It then distinguishes itself from sibling analysis/evaluation tools by positioning itself as the first step when building a model, not analyzing one.

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?

Explicit usage guidance is provided: 'Call this first when you are about to build a model and are unsure how the fields fit together.' It also gives concrete examples ('Help me model a service contract' and no model exists yet) and compares to assembling from schema, making when-to-use vs alternatives clear.

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

Deploy Server