quantum-resource-estimator

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: listing, estimating, comparing, generating frontiers, explaining, and custom modeling. No overlapping functionality.
Naming Consistency5/5
All tools follow a consistent verb_noun snake_case pattern (e.g., list_qubit_models, estimate_resources). No mixing of conventions.
Tool Count5/5
8 tools is well within the ideal range for a specialized domain like quantum resource estimation. Each tool serves a clear role without being excessive.
Completeness5/5
The tool set covers the full lifecycle: exploration (list functions), estimation (main and custom), comparison, tradeoff analysis (frontier), and explanation. No obvious gaps.
Average 4.2/5 across 8 of 8 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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This repository is licensed under MIT License.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions input format for gate times and algorithm constraints but does not disclose whether the tool is read-only, destructive, or any other behavioral traits. As an estimation tool, it is likely safe, but the description fails to confirm this or describe side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is five sentences and front-loads the purpose, followed by usage guidance and details. Every sentence adds value, with no wasted words. However, the structure could be improved by grouping related information (e.g., algorithm input constraints together).
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?
Given the tool's complexity (14 parameters, no schema descriptions, no annotations), the description is adequate but incomplete. It provides essential context for when to use the tool and how to specify gate times and algorithm input. However, it does not describe the output format or explain the remaining parameters, relying on user expertise.
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 0%, so the description must compensate. It adds meaning for gate time parameters (accepts strings like '50 ns') and mentions instruction_set options. It also states that exactly one of algorithm_template, logical_counts, or qsharp_code must be provided. However, the remaining 9 parameters (error rates, qec_scheme, error_budget) are not described, leaving significant gaps.
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 it estimates resources using custom qubit parameters, distinguishing from sibling tools by specifying 'when modeling novel hardware not covered by the 6 predefined qubit models'. It uses a specific verb ('Estimate') and resource ('custom physical qubit parameters'), and the sibling tools include 'list_qubit_models' and 'estimate_resources', reinforcing differentiation.
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 when to use this tool: 'Use this when modeling novel hardware not covered by the 6 predefined qubit models.' It also instructs on providing the algorithm via exactly one of three parameters. However, it does not explicitly state when not to use it or name alternative tools, though the context of siblings implies alternatives exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description bears full responsibility. It discloses the output format and input constraints but does not mention side effects, read-only nature, error behavior, or performance implications. This is adequate but lacks explicit safety guarantees.
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 description is well-structured with a clear first sentence, bullet-like presentation of input constraints and output explanation, and separate mention of optional overrides. No redundant sentences; every part adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite an output schema existing, the description does not cover many essential parameters (qubit_model, qec_scheme, error_budget) which are left to defaults without explanation. For a complex tool with 11 parameters, this is a significant gap.
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 coverage is 0%, so the description must compensate. It explains the algorithm inputs and a few override parameters (qubit_model_overrides, qec_*), but leaves common parameters like qubit_model, qec_scheme, error_budget unexplained. Only about 30% of parameters get meaningful description.
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 generates a Pareto frontier balancing qubit count and runtime, and specifies that the algorithm must be provided via one of three input fields (algorithm_template, logical_counts, qsharp_code). It distinguishes the output as a list of optimal points, and the context of siblings like estimate_resources suggests it is for trade-off analysis.
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 instructs to provide exactly one of three algorithm inputs, mentions optional overrides similar to estimate_resources, and explains the output order (first and last points). However, it does not explicitly contrast with siblings like estimate_resources or explain when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Only describes return values but does not disclose behavioral traits (e.g., read-only, no side effects, auth needs). Missing critical context for mutation-agnostic tools.
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?
Extremely concise: one main sentence, a bullet list, and a usage hint. Every sentence adds value with no redundancy.
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?
Completely covers necessary details: lists schemes with compatibility notes and links to downstream tool. With output schema present, return value explanation is sufficient.
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?
No parameters exist; schema coverage is 100%. Baseline 4 is appropriate as description does not need to add parameter information.
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?
Description clearly states 'List available Quantum Error Correction (QEC) schemes' with verb and resource. Differentiates from siblings by focusing specifically on QEC schemes rather than algorithm templates or qubit models.
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?
Directly states 'Use this to understand which qec_scheme to select for estimate_resources().' Provides clear context for when to use, though no explicit when-not-to or alternative tools mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not explicitly state side effects or permissions, but the nature (estimation) implies read-only behavior. A more explicit 'does not modify any state' would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (algorithm input, hardware parameters, QEC overrides, constraints, return). It is somewhat lengthy but every sentence adds value; could be slightly trimmed but highly functional.
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?
Given the complexity (18 parameters, three algorithm input modes, optional constraints), the description covers all critical aspects including defaults, relationships (e.g., max_duration/max_physical_qubits mutual exclusivity), and return structure. With an output schema present, this is sufficiently complete.
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 0% schema description coverage, the description thoroughly explains all major parameters including algorithm selectors, qubit model, QEC scheme, error budget, and overrides, with syntax examples and valid keys. However, it omits three parameters from the schema: error_budget_logical, error_budget_t_states, and error_budget_rotations.
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 'Estimate the physical quantum resources needed to run a quantum algorithm', using a specific verb and resource. It distinguishes from sibling tools by referencing list_algorithm_templates() and list_qubit_models() for predefined options, setting this apart as the primary estimation tool.
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 to provide the algorithm as exactly one of three options, with examples and defaults for hardware parameters. It does not directly contrast with siblings like compare_configurations, but the guidance on algorithm input is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the return format (side-by-side comparison table with specific columns) and default behavior (compare all 4 gate-based models). No destructive effects or auth needs are mentioned, which is appropriate for a non-mutating tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for algorithm and hardware selection, and front-loads the main purpose. While slightly verbose, every sentence adds value and the structure aids readability.
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 the complexity of 8 parameters with no schema descriptions or annotations, the description covers algorithm specification, hardware selection methods, and output details. It lacks detail on qec_scheme and error_budget parameters, but the presence of an output schema reduces the need to explain return values.
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?
Schema description coverage is 0%, but the description adds significant meaning for algorithm parameters (exactly one of three) and hardware selection parameters (explains four approaches with examples). However, it does not elaborate on qec_scheme or error_budget beyond their defaults.
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 it compares resource estimates across multiple hardware configurations, with specific verb 'Compare' and resource 'resource estimates.' It distinguishes from siblings like estimate_resources (likely single config) and list_qubit_models (just lists) by focusing on comparative analysis.
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 provides clear context: algorithm must be specified one of three ways, and hardware selection has four explicit options with defaults. While it doesn't explicitly state when not to use it vs alternatives, the guidelines are detailed and practical.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool returns parameter descriptions, recommended configurations, and templates. It implies read-only behavior but does not explicitly state idempotency or lack of side effects.
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 description is concise and front-loaded. The first sentence captures the essence, and the bullet list of use cases is efficient and easy to parse.
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?
The tool has one optional parameter and an output schema. The description covers purpose, usage context, and return content adequately. It is complete for a simple explainer tool.
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?
Schema coverage is 0%, but the description compensates by listing all valid values for use_case and explaining their meaning. This adds significant semantic value beyond the raw schema.
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 verb 'Explain' and the resource 'resource estimation parameters' and 'recommend configurations'. It differentiates from sibling tools like estimate_resources by focusing on explanation and configuration guidance.
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 provides specific valid values for use_case and indicates that providing it gives targeted guidance. However, it does not explicitly state when not to use the tool or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description discloses that it returns logical resource counts and source citations. Indicates templates are from published research. No contradictions.
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?
Concise and well-structured with bullet points for categories. Front-loaded with main purpose. Every sentence adds value.
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?
Given zero parameters and presence of output schema, description fully explains the tool's purpose, return values, and usage. No gaps.
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?
No parameters; baseline 4 applies. Schema coverage is 100% and description adds no parameter info, but none needed.
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?
Clearly states it lists quantum algorithm templates with logical resource counts. Provides examples and explains they are from published papers. Differentiates from sibling tool estimate_resources by indicating templates can be passed to it.
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?
Explicitly says the tool lists templates and that each template can be passed to estimate_resources. Provides clear context but does not explicitly mention when not to use or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It describes the return information transparently, but does not explicitly state read-only nature; however, it is implied by 'list'.
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 description is concise and well-structured, front-loading the main purpose and providing additional details in bullet-like format without redundancy.
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?
For a simple list tool with no parameters and an output schema, the description adequately covers what the tool does and how it helps the user, with no gaps.
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?
No parameters exist, so schema coverage is 100%. Baseline for 0 params is 4, and the description adds context about the models listed.
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 lists all 6 predefined physical qubit models with their attributes, which is specific and distinguishes it from sibling tools like estimate_resources.
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?
It explicitly tells the user to use this tool to understand which qubit_model to select for estimate_resources(), providing clear context and purpose, though it does not mention when not to use it.
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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