mcp-agrus-ai

scope_poc

scope_poc

Draft a structured 3-week Discovery Sprint scope for an AI use case, including team, price band, build estimate, open questions, and recommended services for human review.

Instructions

Drafts a structured Discovery Sprint scope for an AI use case. Returns a 3-week plan, team composition, price band, follow-on Build Engagement estimate, open questions Agrus would ask, and recommended services. Use this to convert a hypothetical use case into a concrete engagement proposal that can be reviewed by a human buyer.

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>",
      "vertical": "<vertical>"
    },
    "name": "scope_poc"
  }
}

Input Schema

Name	Required	Description
`use_case`	Yes	One or two paragraphs describing the AI use case the buyer wants to scope. Be specific about workflow, users, and integration surface where possible.
`vertical`	Yes	Which Agrus vertical the use case sits in.
`data_types`	No	Data the AI would touch (e.g. ['PHI', 'patient demographics', 'EHR notes']).
`timeline_hint`	No	Free-form timeline (e.g. 'exploring', 'this quarter', 'production by Q4', 'this is blocking board commitment').
`target_outcomes`	No	What success looks like (e.g. ['reduce adjuster review time by 50%', 'production pilot with 3 clinicians by Q3']).
`compliance_constraints`	No	Regulatory regimes the deployment must satisfy. Drives compliance overlay in the scope.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 16, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations provided, the description carries full burden. It describes what outputs are returned (plan, team, price, etc.) but does not explicitly state side effects or safety (e.g., whether it's read-only or creates anything). The verb 'Drafts' suggests generation without alteration, but this is implicit.

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?

Two sentences efficiently convey purpose, outputs, and usage. No redundant or filler content. Each sentence adds value.

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 6 parameters and no output schema, the description adequately covers purpose, inputs (implied by 'use case'), and outputs listed. It lacks explicit details on return format or structure, but the list of returned items gives sufficient context for an agent to understand what the tool provides.

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 100%, so baseline is 3. The description does not add additional meaning to parameters beyond what the schema already provides. Each parameter has a clear description in the schema, and the tool description focuses on output, not input semantics.

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 drafts a structured Discovery Sprint scope for an AI use case and lists specific outputs (3-week plan, team, price band, etc.). It distinguishes from sibling tools like get_case_study and request_proposal by focusing on scoping a sprint rather than providing a case study or full proposal.

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 says 'Use this to convert a hypothetical use case into a concrete engagement proposal' which gives clear guidance on when to use. It does not explicitly exclude other scenarios, but the context of sibling tools (e.g., request_proposal for final proposals, get_case_study for examples) implies boundaries.

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

Deploy Server