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Northside Intelligence WebMCP

ni_bridgeai_run

Get a plan to connect a legacy business workflow to modern AI agent endpoints without a code rebuild (Standard $33 or Agentic $49.50). Returns a checkout_url and order_id; after payment call ni_order_status with order_id to get the result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNostandard
workflow_typeYesType of workflow to modernize

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
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 that the tool initiates a paid process (pricing mentioned), returns a checkout_url and order_id, and that the actual result is only available after payment via another tool. This is a critical transactional trait. It does not mention failure modes, side effects, or rate limits, but the core behavior is transparently described.

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 a single, well-structured sentence that front-loads the purpose, then provides pricing, return values, and the next-step instruction. Every clause earns its place with no redundancy or filler. It is concise and efficient.

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 there is no output schema and no annotations, the description does a good job explaining the return (checkout_url, order_id) and the sequential workflow. It also names the follow-up tool. It lacks details on acceptable workflow_type values and potential error conditions, but for a tool that initiates a purchase, the core flow is adequately covered.

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 coverage is 50% (workflow_type has a minimal description, tier has none). The description adds meaning to the tier parameter by mapping 'standard' to $33 and 'agentic' to $49.50, which is helpful. However, workflow_type remains vague—only described as 'Type of workflow to modernize' without examples or constraints. The description partially compensates for the tier parameter but does not fully address workflow_type.

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 a specific action: 'Get a plan to connect a legacy business workflow to modern AI agent endpoints.' It names the resource (legacy workflow modernization), the verb (get a plan), and distinguishes itself by mentioning the return of a checkout_url and order_id, which is distinct from sibling tools like ni_order_status or ni_store_order. 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear use case (connecting legacy workflows to AI endpoints) and even specifies the follow-up action ('after payment call ni_order_status with order_id'). It does not explicitly state when not to use it or name alternatives for the same purpose, but the scenario is specific enough that an agent can infer appropriate usage. The pricing tiers (Standard vs Agentic) also help select parameters.

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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