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decisionmatrix-mcp

by io.github.inity13

score_options

Score options against criteria when the score matrix is supplied separately. Returns the full normalized scored matrix (per-option, per-criterion) plus a ranking, without the narrative winner explanation. Use create_decision if you want a winner + explanation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
methodNoweighted_sum
scoresYesScore matrix. Object form: {"Option A": {"Criterion 1": 8, ...}, ...}. Array form: [{"option":"Option A","scores":{...}}]. Or inline scores on each option object.
optionsYesNamed alternatives. Strings ["A","B"] or objects [{"name":"A","scores":{...}}].
criteriaYesWeighted criteria. Each: {name, weight (relative, >=0), direction: 'benefit' (higher better, default) | 'cost' (lower better)}.

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description must explain behavioral traits. It discloses what is returned (full normalized scored matrix, ranking) and what is omitted (narrative winner explanation). This is meaningful behavioral context beyond the minimal function statement, though it doesn't detail side effects or permission requirements (likely none needed).

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 concise sentences: the first defines purpose and output, the second clarifies the alternative. No redundancy, front-loaded with the key functionality. Every sentence earns its place.

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 4-parameter tool with nested objects and no output schema, the description explains the return value format (normalized matrix + ranking) and contrasts with a sibling. It doesn't explain the scoring methods (e.g., weighted_sum vs. topsis), but the schema partially covers that. Overall, it provides sufficient context for an agent to invoke correctly.

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?

The input schema covers 75% of parameters with descriptions, leaving only 'method' without a description. The tool description adds little parameter-specific detail beyond what the schema provides, but it does mention the matrix is supplied separately, which aligns with the 'scores' parameter. Since coverage is moderate, a score of 3 is appropriate.

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's function: scoring options against criteria using a separately supplied matrix. It specifically says it returns a normalized scored matrix and ranking, and contrasts with create_decision which provides a narrative winner. This distinguishes it effectively from siblings.

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

Usage Guidelines5/5

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

Explicitly states when to use this tool ('when the score matrix is supplied separately') and provides an alternative ('Use create_decision if you want a winner + explanation'). This gives clear guidance on tool selection compared to at least one sibling, satisfying the 'when/when-not/alternatives' criterion.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: compare_two is specifically for two-option head-to-head, create_decision is the main ranking tool with explanation, score_options returns the scored matrix without narrative, and sensitivity_analysis tests weight robustness. Health check and list_methods serve auxiliary but separate roles. No two tools are likely to be confused.

Naming Consistency4/5

Most tools follow a verb_noun pattern (create_decision, list_methods, score_options, compare_two), but sensitivity_analysis is a noun phrase without a verb, and compare_two uses a number instead of a noun. The snake_case style is consistent throughout, making the set readable overall.

Tool Count5/5

With 6 tools, the server is well-scoped for its decision-analysis purpose. Each tool covers a distinct aspect of the workflow: creation, pairwise comparison, scoring, sensitivity analysis, and method discovery. The count feels neither sparse nor bloated.

Completeness5/5

The tool set provides comprehensive coverage of multi-criteria decision making: creating a full decision, comparing exactly two options, scoring without the narrative, and testing robustness. Including list_methods for methodology details and health_check for server status rounds out the surface. No significant gaps are apparent for the stated domain.

Resources