MDAN

mdan_estimate_scope

Read-only

Determine the required planning level for a change based on its description, files, and downstream impact in the context graph, choosing between quick-dev, quick-spec, or full planning.

Instructions

Decide how much process a change needs (oneshot quick-dev / quick-spec first / full planning) from its description, files and the real downstream impact in the context graph

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": {
      "description": "<description>"
    },
    "name": "mdan_estimate_scope"
  }
}

Input Schema

Name	Required	Description
`files`	No
`artifacts`	No	Context graph node ids touched by the change
`description`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv4.1.1Sep 23, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, so the description does not need to repeat that this is non-mutating. It adds useful context that the decision is based on files and real downstream impact in the context graph, but it does not disclose further behavioral details such as return shape or analysis depth. No contradiction with the annotations.

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 entire description is one dense, front-loaded sentence with no filler. Every part adds semantic content: the decision outcome, the process categories, and the input sources.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only estimator the description conveys enough to understand the tool's basic purpose and likely output categories. However, without an output schema it could usefully state the result format, and the ambiguity around the 'files' parameter and the absence of usage boundaries leave gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33%; only 'artifacts' is explained in the schema. The description partially compensates by indicating that 'description' and 'files' feed the estimation, but it leaves 'files' undefined (file paths? content?) and does not clarify how the three parameters relate or how optional they are.

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 ('decide') and a concrete resource ('how much process a change needs'), enumerating the three outcome categories: oneshot quick-dev / quick-spec first / full planning. This clearly conveys the tool's decision-making role and distinguishes it from siblings such as mdan_run_workflow or mdan_graph_impact.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

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

The description implies the tool should be used when a change needs process-level estimation, and states the inputs it draws on. However, it never explicitly says when not to use it or names any alternative tool, leaving the selection logic to inference.

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