agentflow-mcp

tool_selection_lookup

Recommend data platforms for enterprise use cases by analyzing data stack, compliance constraints, and latency needs, with cloud fit, reasoning, and alternatives.

Instructions

Recommend a data platform based on use case, data stack, constraints (HIPAA, PII, data residency, SSO/SAML), and latency needs — with cloud fit, reasoning, and alternatives.

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": {
      "use_case": "<use_case>",
      "data_stack": "<data_stack>",
      "constraints": "<constraints>"
    },
    "name": "tool_selection_lookup"
  }
}

Input Schema

Name	Required	Description
`latency`	No	Latency need: batch or real-time
`use_case`	Yes	What the enterprise wants to build, e.g. "AI-powered patient insights"
`data_stack`	Yes	Platforms/tools in play or under consideration
`constraints`	Yes	Governance/compliance constraints

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 23, 2026

TDQS

A4.2/5.0

Behavior3/5

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

No annotations exist, so the description carries the burden. It discloses that the tool will return 'cloud fit, reasoning, and alternatives,' which gives some behavioral shape, but it does not clarify whether it performs external calls, returns mock versus curated answers, or has any side effects. It describes inputs/outputs but not deeper behavior.

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?

A single, dense sentence lists all inputs and outputs without waste. It front-loads the verb and target, and every phrase earns its place. The semi-colon-separated output list is clear to parse.

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?

The description is sufficient for an agent to prepare the required fields and know what kind of response to expect (recommendation with cloud fit, reasoning, alternatives). Since there is no output schema, describing the response at this level helps. It could add the expected result shape or a mention of staleness provenance, but this is minor.

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 description coverage is 100%, so the baseline is 3. The description adds meaning by expanding 'constraints' with examples (HIPAA, PII, data residency, SSO/SAML) and by flagging latency as an additional decision factor. This goes beyond the literal schema descriptions and helps an agent populate parameters accurately.

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 names a concrete verb, 'Recommend', and a specific resource, 'data platform', and enumerates the decision inputs and outputs (cloud fit, reasoning, alternatives). This clearly differentiates the tool from siblings like arch_pattern_lookup and risk_policy_lookup, which concern different domains.

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 explicitly frames when to use this tool: when the agent needs a data platform recommendation given use case, data stack, and constraints. It doesn't explicitly mention alternative tools or exclusions, but the context is clear enough. Sibling names also make the separation obvious.

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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