A
licenseA
qualityA
maintenanceProvides a transparent, deterministic multi-criteria decision analysis engine that ranks options against weighted criteria with exact, explainable results.
6
16 npm
MIT
Clarity Decision Matrix
Server Details
Decision planning tool for a weighted decision matrix.
If you are the author of this connector, you can claim ownership by verifying the domain or GitHub account it belongs to. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
A4.8/5.0
Scored across 1 tool
Disambiguation5/5
There is only one tool, so there is no possibility of selection confusion. Its description clearly delimits scope, inputs (2-20 options, 1-8 criteria) and outputs, and explicitly states what it does not do.
Naming Consistency5/5
The single name calculate_decision_matrix follows a clear verb_noun snake_case convention that matches the server's domain name. No competing conventions exist to clash with it.
Tool Count4/5
The server is a focused calculation utility, so a single tool is defensible and the tool is substantive rather than trivial. It sits below the typical 3-15 range, but adding more tools would only fragment one coherent operation.
Completeness4/5
Within its stated domain the tool covers the full workflow: normalization, ranking with ties, break-even analysis, weight scenarios and CSV export. Minor gaps remain (e.g. no persistence or multi-matrix comparison), but these fall outside the declared scope.
Available Tools
1 toolcalculate_decision_matrixCalculate decision matrixARead-onlyIdempotentInspect
Use this when the user wants a weighted decision matrix, scored comparison, alternative ranking, or weight sensitivity analysis with explicit criteria, weights and scores. Accepts two to twenty options and one to eight criteria. Scores use a fixed zero-to-one-hundred scale; benefit means higher is better and cost means lower is better. Returns normalized weights, exact rankings and ties, pairwise break-even shares, up to four explicit weight scenarios and CSV. Does not research options, invent scores, infer preferences or predict outcomes.
| Name | Required | Description | Default |
|---|---|---|---|
| title | No | Comparison title, e.g. Supplier shortlist. | |
| options | Yes | Alternatives with supplied ratings, e.g. [{"name":"A","scores":[80,40]},{"name":"B","scores":[60,20]}]. | |
| criteria | Yes | Criteria in the same order as each option score array, e.g. [{"name":"Quality","weight":60,"direction":"benefit"},{"name":"Expense","weight":40,"direction":"cost"}]. | |
| scenarios | No | Optional explicit weight scenarios, e.g. [{"label":"Cost emphasis","weights":[20,80]}]. |
Output Schema
| Name | Required | Description |
|---|---|---|
| tied | Yes | |
| notes | Yes | |
| title | Yes | |
| ranking | Yes | |
| winners | Yes | |
| criteria | Yes | |
| downloads | Yes | |
| scenarios | Yes | |
| sensitivity | Yes | |
| option_count | Yes | |
| preview_only | Yes | |
| winner_count | Yes | |
| winners_preview_only | Yes | |
| sensitivity_preview_only | Yes |
TDQS
A4.9/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/idempotent/non-destructive annotations, the description discloses the fixed 0-100 scoring scale, the benefit-vs-cost interpretation, enforced limits (2-20 options, 1-8 criteria, up to 4 scenarios), and that output includes normalized weights, exact rankings and ties, break-even shares and CSV. This is unusually rich behavioral context for a read-only calculation tool.
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?
Front-loaded with the trigger condition, then scales, then return contents, then exclusions. Dense but no filler sentence; every clause carries information an agent needs.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Even though an output schema exists, the description summarizes return contents, and it covers scoring semantics, cardinality limits, and out-of-scope behaviors. Nothing needed to invoke it correctly is missing.
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?
With 100% schema description coverage the baseline is 3; the description reinforces the critical semantics an agent must get right—fixed 0-100 scale and benefit/cost direction—and states the option/criteria count bounds. Most of these are also in the schema, so it adds value but not substantial new parameter detail.
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 states a specific verb and resource (weighted decision matrix / scored comparison) and enumerates the output kinds it produces. An agent can tell exactly what the tool computes without opening the schema.
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?
It gives an explicit trigger ('Use this when the user wants a weighted decision matrix, scored comparison, alternative ranking, or weight sensitivity analysis') and explicit exclusions ('Does not research options, invent scores, infer preferences or predict outcomes'). That is a complete when-to-use / when-not-to-use contract.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
calculate_decision_matrix
Related MCP Connectors
Deterministic multi-criteria decision analysis for AI agents — score, rank & explain options.
Reviewed decision matrices and buying guides: ranked options and weighted criteria in 6 languages
Work a decision through SwingFactor’s method: build and challenge a model of what could change it.
A decision model for the calls that don't have a right answer. The better option, with a reason.
Related MCP Servers
- AlicenseAqualityBmaintenanceMCP server that enables structured decision-making using weighted decision matrices, sensitivity analysis, and robust recommendations.82MIT
decisio-mcp-serverofficial
AlicenseAqualityDmaintenanceEnables AI assistants to perform structured decision-making using the Analytic Hierarchy Process (AHP), allowing users to define criteria, compare options pairwise, and calculate ranked results with consistency validation.6MIT- AlicenseAqualityDmaintenanceA decision structure analysis engine that transforms emotional dilemmas into structured frameworks by identifying variables, constraints, and strategy paths. It helps users evaluate risk distributions and cognitive biases without offering subjective advice or definitive answers.22 npm1MIT
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd
Glama MCP Gateway
Add one secure layer between your agents and this server.