agentflow-mcp

arch_pattern_lookup

Match your enterprise requirements to a curated reference architecture pattern. Provides components, data zones, integration notes, and confidence scores along with diagram-ready data based on industry, data stack, cloud, and constraints.

Instructions

Match an enterprise ask (industry, data stack, cloud, constraints) to a curated reference architecture pattern with components, data zones, integration notes, confidence, and diagram-ready data.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "industry": "<industry>",
      "data_stack": "<data_stack>",
      "constraints": "<constraints>"
    },
    "name": "arch_pattern_lookup"
  }
}

Input Schema

Name	Required	Description
`cloud`	No	Cloud preference, e.g. GCP, AWS, Azure
`latency`	No	Latency expectation: batch or real-time
`industry`	Yes	Industry code, e.g. media_agency, healthcare, retail, financial_services
`data_stack`	Yes	Candidate platforms/tools, e.g. ["BigQuery", "Snowflake"]
`constraints`	Yes	Governance/compliance constraints, e.g. ["SAML SSO", "EU data residency"]

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 23, 2026

TDQS

A4.1/5.0

Behavior4/5

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

No annotations are provided, so the description carries the burden of explaining behavior. It discloses that the result is a curated, confidence-scored pattern rather than an unranked list. It does not detail no-match behaviors or side-effect safety, but the lookup-oriented naming and output-focused description make behavior reasonably transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one information-dense sentence with no filler. It front-loads the core matching behavior, then lists the key outputs. It is compact and scannable, though the full list of outputs makes the sentence slightly long.

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?

For a lookup-oriented tool with no output schema and no annotations, the description adequately explains what inputs shape the match and what the caller receives. It does not cover edge cases like no matching pattern, confidence representation, or return structure, preventing a 5.

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?

All five parameters are already described in the schema with 100% coverage, so a baseline 2 is appropriate. The description only lightly reinforces the input dimensions and does not add material relationships or format details beyond the schema.

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 uses a specific verb, 'Match', and a resource, 'reference architecture pattern', and identifies the output categories it returns. This clearly distinguishes it from sibling lookup tools like brand_context_lookup and tool_selection_lookup, which target different lookups.

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 clearly frames the use case: matching an enterprise ask with industry, data stack, cloud, and constraints to a reference architecture. It does not explicitly exclude or compare against sibling tools, but the intended context is clear enough for an agent to select it appropriately.

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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