Deterministic multi-criteria decision analysis for AI agents — score, rank & explain options.
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Average 4.2/5 across 6 of 6 tools scored. Lowest: 3.6/5.
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.
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.
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.
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.
compare_twoAInspectDirect head-to-head comparison of exactly two options. Returns the winner, the score margin, how many criteria each option wins, and a per-criterion breakdown of who each criterion favours. Pass option_a and option_b (names) or a 2-element options array, plus criteria and scores.
| Name | Required | Description | Default |
|---|---|---|---|
| method | No | weighted_sum | |
| scores | Yes | Score matrix. Object form: {"Option A": {"Criterion 1": 8, ...}, ...}. Array form: [{"option":"Option A","scores":{...}}]. Or inline scores on each option object. | |
| options | No | Named alternatives. Strings ["A","B"] or objects [{"name":"A","scores":{...}}]. | |
| criteria | Yes | Weighted criteria. Each: {name, weight (relative, >=0), direction: 'benefit' (higher better, default) | 'cost' (lower better)}. | |
| option_a | No | ||
| option_b | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the return contract: winner, score margin, criteria win counts, and per-criterion breakdown. This goes beyond a simple 'compares two options' and gives the agent a clear picture of what to expect. It does not discuss edge cases or method-specific behavior, but the core output transparency is solid.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the comparison purpose and output details, then concise input instructions. Every word earns its place; no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 6 parameters, nested objects, and no output schema, the description covers the essential aspects: what it does, what it returns, and how to supply inputs. It omits details about the method parameter and direction field, but those are well-documented in the schema. The description is suitably complete for an agent to select and invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50%, so the description needs to compensate. It clarifies the alternative ways to pass options: 'option_a and option_b (names) or a 2-element options array, plus criteria and scores.' This adds meaningful guidance beyond the schema. It doesn't explain the method parameter, but the schema provides enum values; overall, it sufficiently disambiguates the input formats.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's purpose: 'Direct head-to-head comparison of exactly two options.' It also enumerates specific outputs (winner, score margin, criteria wins, per-criterion breakdown). The phrase 'exactly two options' distinguishes it from sibling tools like score_options, which handle multiple options.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly states the scope: 'exactly two options,' which implies it should be used for pairwise comparison, not for multiple options. It does not explicitly name alternatives (e.g., 'use score_options for more than two'), so it falls short of a 5, but the context is unmistakable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_decisionAInspectRank named options against weighted criteria and return the winner, full ranking, per-criterion score breakdowns, methodology, the weights used, and a plain-language explanation. This is the main tool. Provide options, criteria [{name, weight, direction}], and a scores matrix. method defaults to weighted_sum (also: weighted_product, topsis). 100% deterministic.
| Name | Required | Description | Default |
|---|---|---|---|
| method | No | weighted_sum | |
| scores | Yes | Score matrix. Object form: {"Option A": {"Criterion 1": 8, ...}, ...}. Array form: [{"option":"Option A","scores":{...}}]. Or inline scores on each option object. | |
| options | Yes | Named alternatives. Strings ["A","B"] or objects [{"name":"A","scores":{...}}]. | |
| criteria | Yes | Weighted criteria. Each: {name, weight (relative, >=0), direction: 'benefit' (higher better, default) | 'cost' (lower better)}. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It states '100% deterministic', which is a key behavioral trait, and enumerates the returned information types. It does not mention side effects or error handling, but for a pure computation tool, these are less critical. This adds meaningful behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with the first sentence stating purpose and outputs, and the second sentence providing essential input guidance and method default. Every sentence earns its place; no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description explicitly lists all compute outputs, compensating for the lack of structured return value documentation. It covers input structure and method choices. However, it does not address edge cases or error conditions, which could be relevant for an agent handling complex nested objects, but overall it is sufficiently complete for typical usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 75% of parameters with descriptions, but the description adds value by summarizing the input structure and explaining the 'method' parameter's default and alternatives (weighted_sum, weighted_product, topsis). It also clarifies the flexible score matrix formats, which is not fully decipherable from the schema alone. This goes a step beyond the structured fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Rank named options against weighted criteria') and lists detailed outputs (winner, full ranking, per-criterion breakdowns, methodology, weights, plain-language explanation). This distinguishes it from sibling tools like compare_two or sensitivity_analysis, which likely focus on narrower comparisons or analyses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'This is the main tool' implies it is the default choice for decision ranking, but it does not explicitly state when to use it versus alternatives, nor does it mention exclusions or when a sibling tool would be more appropriate. It provides input guidance but lacks explicit when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
health_checkAInspectServer health, version, and capabilities. No parameters.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It only lists output topics (health, version, capabilities) and does not state whether it is read-only, requires auth, or has side effects. Minimal beyond the name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences with no waste. The key information (what it returns) is front-loaded, and the parameter statement is succinct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple health check tool with no params and no output schema, the description covers the essential facts. It could be more detailed about return format, but it is sufficient for standard health-check usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has zero parameters, so the baseline is 4. The description says 'No parameters,' which is redundant with the schema but does not conflict. No additional parameter meaning is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool provides: server health, version, and capabilities. It is specific and distinguishes from sibling tools that focus on analysis, decisions, and scoring.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. It does not mention scenarios, prerequisites, or any exclusions, leaving the agent to infer usage from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_methodsAInspectList the available scoring methods (weighted_sum, weighted_product, topsis) with descriptions, normalization details, score ranges, and when to use each. No parameters.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the output content (descriptions, normalization details, score ranges, and when to use each) and explicitly notes 'No parameters', indicating a safe, read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The single sentence is front-loaded with the verb 'List' and the resource, and it efficiently packs the key information about the methods and output without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with no parameters or output schema; the description covers the purpose and the content of the listing comprehensively, though it doesn't specify the exact return format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and the description explicitly states 'No parameters', which is sufficient. Baseline 4 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists available scoring methods and explicitly enumerates them (weighted_sum, weighted_product, topsis), distinguishing it from sibling tools like score_options that apply methods.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when needing to understand scoring methods, but lacks explicit guidance on when to choose this tool over siblings like score_options or compare_two.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
score_optionsAInspectScore 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.
| Name | Required | Description | Default |
|---|---|---|---|
| method | No | weighted_sum | |
| scores | Yes | Score matrix. Object form: {"Option A": {"Criterion 1": 8, ...}, ...}. Array form: [{"option":"Option A","scores":{...}}]. Or inline scores on each option object. | |
| options | Yes | Named alternatives. Strings ["A","B"] or objects [{"name":"A","scores":{...}}]. | |
| criteria | Yes | Weighted criteria. Each: {name, weight (relative, >=0), direction: 'benefit' (higher better, default) | 'cost' (lower better)}. |
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.
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.
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.
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.
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.
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.
sensitivity_analysisAInspectTest how robust the winner is to changes in criteria weights. Sweeps each criterion's weight +/- 'variation' (default 0.2 = 20%) over 'steps' (default 10) increments, recomputes the ranking, and reports a robustness score, which criteria are most likely to flip the result, and the flip points.
| Name | Required | Description | Default |
|---|---|---|---|
| steps | No | Number of weight steps per criterion (2-100). | |
| method | No | weighted_sum | |
| scores | Yes | Score matrix. Object form: {"Option A": {"Criterion 1": 8, ...}, ...}. Array form: [{"option":"Option A","scores":{...}}]. Or inline scores on each option object. | |
| options | Yes | Named alternatives. Strings ["A","B"] or objects [{"name":"A","scores":{...}}]. | |
| criteria | Yes | Weighted criteria. Each: {name, weight (relative, >=0), direction: 'benefit' (higher better, default) | 'cost' (lower better)}. | |
| variation | No | Fractional weight sweep, 0<v<=1. 0.2 = +/-20%. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It clearly discloses the algorithm (sweeping +/- variation in steps, recomputing ranking) and the kinds of outputs (robustness score, flippable criteria, flip points). It does not state side effects, but 'test' and 'recompute' imply non-mutating behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single dense sentence delivers purpose, mechanism, defaults, and output summary without filler. Every phrase earns its place, and the most important action is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is complex (six params, nested objects, no output schema), but the description only lists result categories qualitatively and omits the output format, edge-case behavior, and any dependencies on prior calls. It is adequate for a basic understanding but leaves gaps in return-value details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is high (83%), giving a baseline of 3. The description adds value by explaining that variation and steps control the weight sweep range and increment count, and it mentions their defaults. Other parameters are well-covered by the schema, so no further compensation is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb ('Test') and a clear resource: robustness of the winner to criteria weight changes. It explains the sweeping mechanics and outputs, making it easily distinguishable from siblings like score_options and compare_two.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The context that this follows a ranking decision is implied by 'the winner', but the description never explicitly says when to use sensitivity_analysis versus alternatives, nor provides exclusions. No alternative tools are mentioned, so guidance is implied rather than stated.
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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