mcp-server-cantera

by StepFunctionLLC

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct operation on mixtures, species, or mechanisms. No two tools have overlapping purposes; for example, calculate_adiabatic_flame_temperature and compute_ignition_delay are clearly different, and get_species_production_contributors is specific to pathway analysis.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_lab_mixture, get_reaction_rates, run_batch_reactor). The only slight deviation is 'equilibrate' which is a single verb, but it remains clear and fits the pattern.
Tool Count5/5
With 15 tools covering mixture creation, property queries, reaction analysis, equilibrium, and reactor simulation, the count is well-proportioned for a chemistry kinetics server. Each tool has a clear role without redundancy.
Completeness4/5
The tool set covers the core workflow: mixture creation, property retrieval, equilibrium, ignition delay, flame temperature, and batch reactor simulation. Minor gaps exist (e.g., no direct tool to set specific temperature/pressure on a mixture, or to modify composition arbitrarily), but the main lifecycle is well-supported.
Average 3.6/5 across 15 of 15 tools scored. Lowest: 2.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 85 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the calculation without mentioning any behavioral traits such as assumed steady-state, reliance on a mechanism file, or that it uses Cantera. The description fails to disclose potential pitfalls like convergence issues or the need for initial temperature assumptions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one sentence), but given the tool's complexity (6 parameters, output schema, multiple siblings), it is under-specified. While brevity is desirable, the lack of structure and missing details makes it less helpful. It fails to earn its place by providing only minimal information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a complex input schema with 6 required parameters and an output schema, yet the description mentions none of these. It does not explain the required inputs (e.g., mechanism, fuel composition) or hint at return values. For a tool with such depth, the description is critically incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the tool description provides no information about parameters. Although the input schema itself has detailed descriptions, the description adds no value. It does not compensate for the low coverage by summarizing key parameters or their roles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Calculate' and the resource 'adiabatic flame temperature', making the primary purpose obvious. However, it does not distinguish this tool from its siblings, such as 'compute_ignition_delay' or 'equilibrate', which might also involve temperature calculations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. For example, it does not clarify that this tool is specifically for steady-state adiabatic flame temperature, while 'compute_ignition_delay' is for transient ignition. There are no exclusions or alternative recommendations.
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. It says 'detailed thermodynamic properties' but does not specify what properties (e.g., enthalpy, entropy, heat capacity) or any side effects, permissions, or limitations. Minimal disclosure 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no fluff. Efficient but could incorporate more contextual info without losing conciseness.
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?
With an output schema, return values need no explanation. However, the tool is among 15 similar siblings and lacks differentiation. No when-to-use or prerequisites, making it incomplete for agent decision-making.
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%, meaning the tool description does not repeat or enhance parameter info. The schema itself describes parameters well, but the description adds no value beyond the schema. Baseline 3 reduced because no added context.
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 action ('Get') and the resource ('detailed thermodynamic properties for a specific species from a mechanism file'). It distinguishes from siblings like 'get_species_thermo' which might return simpler data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like 'check_species_availability' or 'get_species_thermo'. Context is implied by name but not clarified.
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?
Description discloses the tool updates the mixture state and returns final state, heat release, and Gibbs free energy. Without annotations, it carries full burden but does not detail side effects, permissions, or reversibility. Adequate but not thorough.
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?
Two sentences, no wasted words. However, could be better structured to front-load key information. Still concise enough.
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?
Description lacks details on the 'basis' parameter's impact on equilibrium type, no comparison to siblings, and minimal return value explanation despite output schema presence. Incomplete for a simulation tool with multiple modes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (no parameter info in description). The description omits the two effective parameters (name, basis) entirely. Schema descriptions exist but the description adds no value, failing to compensate for low coverage.
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 creates a state of chemical equilibrium for a lab bench mixture, with specific verbs and resource (equilibrate, mixture). It distinguishes from siblings like calculate_adiabatic_flame_temperature which are different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. Does not mention scenarios or exclusions, leaving the agent without decision support for selecting this over other tools.
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. It lists returned properties but discloses no behavioral traits (e.g., read-only nature, rate limits, error behavior). Lacks context like 'does not modify state.'
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?
Description is a single paragraph that concisely lists properties without unnecessary repetition. Could be slightly shorter, but no fluff.
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?
Output schema exists, so description needn't explain return values, but it does anyway. Lacks information on prerequisites, error conditions, or state changes. Adequate but not comprehensive given complexity.
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?
Input schema for 'name' parameter includes a description, so baseline is 3 despite context signal claiming 0% coverage. Tool description adds no additional parameter meaning beyond the 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?
Description clearly states the tool retrieves comprehensive thermodynamic and transport properties of a lab bench mixture, listing specific properties. This distinguishes it from siblings like get_species_properties (species-level) and get_reaction_rates (reaction-level).
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?
Description implies usage when mixture properties are needed, but provides no explicit guidance on when to use this tool versus alternatives (e.g., get_species_properties for species-specific data) or prerequisites (e.g., mixture must 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?
With no annotations, the description carries the full burden. It discloses that the tool builds a dynamic mechanism from NASA databases and handles gas and condensed phases, but does not mention side effects, authentication needs, rate limits, or computational intensity. Some behavioral context is present but incomplete.
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 3 concise sentences: purpose, method, supported metals. It is front-loaded and contains no superfluous information. Every sentence earns its place.
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?
The tool is moderately complex with an output schema, but the description omits usage context, prerequisite (NASA database availability), error conditions, and typical output interpretation. Adequate but not fully complete.
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% (the tool description does not explain parameters individually). The description only lists supported metals and implies the oxidizer and other parameters, but does not add meaning beyond the schema's property descriptions. Given low coverage, the description should compensate more by summarizing key parameters and constraints.
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 calculates equilibrium temperature and products for metal-oxygen/air combustion. It specifies it builds dynamic mechanisms from thermodynamic databases and handles both gas and condensed phases. This distinguishes it from sibling tools like calculate_adiabatic_flame_temperature or equilibrate.
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 usage for metal combustion equilibrium but does not explicitly state when to use this tool versus alternatives like equilibrate or calculate_adiabatic_flame_temperature. No guidance on when not to use or prerequisites is provided.
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 are provided, so the description carries the full burden. It only states that the tool lists available mechanisms with descriptions, but does not disclose whether it is read-only, requires any permissions, or any other behavioral traits like rate limits or 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 a single, short sentence that conveys the essential purpose without any wasted words. It is front-loaded and immediately understandable.
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 that the tool has no parameters and an output schema exists (though not shown), the description is sufficient for an agent to understand what the tool does. It mentions that the output includes descriptions, which adds value. However, it could be more complete by specifying the output format or that it lists all available mechanisms, but overall it is adequate.
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?
The input schema has 0 parameters, so the baseline is 4. The description adds no parameter information, which is expected since there are none.
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 action ('List') and the resource ('commonly available Cantera mechanism files'), and it distinguishes from sibling tools like 'list_species_in_mechanism' which lists species within a mechanism, and 'list_lab_mixtures' which lists mixtures. The verb-resource combination is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage context or guidelines are provided. The description does not indicate when to use this tool versus alternatives such as 'list_species_in_mechanism' or 'list_lab_mixtures'. There is no explicit 'when to use' or 'when not to use' information.
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 are provided, so the description carries the full burden. It implies a read-only operation, but does not disclose any behavioral traits beyond listing, such as whether the list is ordered, paginated, or includes metadata.
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 a single sentence that is concise and front-loaded, with no unnecessary words or information.
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 that there are no parameters and an output schema exists (though not shown), the description is complete enough for a list tool. It could mention the expected output format, but the schema likely covers that.
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?
The tool has zero parameters, so no additional parameter information is needed. The description is sufficient for an empty input 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 'List' and the resource 'all mixtures currently stored on the lab bench.' It is specific and distinguishes from siblings like 'create_lab_mixture' and 'get_mixture_properties'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as 'get_mixture_properties' or 'create_lab_mixture'. The description is minimal and provides no context for appropriate usage.
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 are provided, so the description must carry full behavioral disclosure. It mentions that the tool searches two databases but does not disclose aspects like side effects (none, it's read-only), performance, or error handling. The description is insufficient to fully understand the tool's behavior.
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 composed of three concise sentences with no redundant information. It front-loads the primary purpose and immediately follows with usage context and specific databases. Every sentence adds value.
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 simple tool with one parameter and an output schema (though not detailed in the description), the description is largely complete. It covers the purpose, usage context, and databases. However, it lacks detail about the output format, which limits completeness slightly. Still, it is adequate given the tool's simplicity.
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 already provides a description and examples for the only parameter 'species_list.' The tool description does not add any additional meaning or clarification about the parameter, but given that the schema coverage includes a description, a baseline score of 3 is appropriate. No extra value is added.
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: 'Check which database contains specific species.' It also specifies the databases searched (GRI-Mech 3.0 and NASA Gas Database), distinguishing it from sibling tools like list_species_in_mechanism which list species in a single mechanism.
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 context: 'Useful for planning simulations and verifying species availability before calculations.' This implies the tool should be used before other computational tools. However, it does not explicitly state when not to use or mention alternatives, so there is room for improvement.
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?
With no annotations, the description carries the full burden. It discloses that the tool simulates over time and updates the lab bench mixture to the final reacted state, indicating a mutation. However, it does not detail side effects, reversibility, or any required permissions. The description adds moderate transparency but lacks depth.
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 two sentences long, efficient, and front-loaded with the main action. Every sentence provides essential information: what it does and why to use it. No redundancy or wasted words.
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?
The description explains the core purpose and effect (updating lab bench state). Since an output schema exists, the lack of explicit output explanation is acceptable. However, the description could briefly mention that results are returned for each time step, but overall it is fairly complete.
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 already provides descriptions for all parameters (name, duration, steps). Since schema description coverage is 0% (meaning the description adds no parameter info), the baseline is 3. The tool description does not explain how parameters are used or add any additional meaning beyond the 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?
Description clearly states the tool simulates a Constant Pressure Ideal Gas Batch Reactor, with specific action 'Simulate' and resource 'Batch Reactor', and explains the purpose: to see evolution of temperature and composition, and that it updates lab bench mixture state. This distinguishes it from siblings like equilibrate or compute_ignition_delay.
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 provides usage context ('Use this to see how temperature and composition evolve during combustion or other chemical reactions') but does not specify when not to use it or mention alternative tools. Given the number of sibling simulation tools, more explicit guidance would be beneficial.
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 provided, the description carries the full burden. It explicitly states the tool does not update the mixture state, a key behavioral trait. This compensates well for the lack of annotations, though more detail on required permissions or side effects could be added.
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 with only four sentences, each adding essential information: action, definition, use case, and a crucial side-effect note. It is front-loaded with the primary action.
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?
The description covers the tool's purpose and a key behavioral note, but given the complexity of combustion chemistry and the presence of an output schema (not shown), it is sufficient. Minor gaps: no mention of preconditions (e.g., mixture must exist) or typical simulation time ranges.
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% according to signals, meaning the description should add parameter meaning. However, the description does not elaborate on the parameters 'name' and 'max_time' beyond what the schema already provides (e.g., mixture ID and max time). This leaves a gap for an agent to understand parameter usage without schema context.
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 calculates auto-ignition delay time, defines it as the point of steepest temperature rise, and distinguishes it from sibling tools by specifying a unique metric (dT/dt max) and application (fuel reactivity, mechanism validation).
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 usage for fuel reactivity characterization but does not explicitly state when to use this tool versus siblings like run_batch_reactor or calculate_adiabatic_flame_temperature. No when-not-to-use or alternative guidance is provided.
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?
The description discloses that the mixture retains its state until modified or replaced, indicating persistence. This is useful beyond schema. However, it does not address whether creating with an existing name overwrites or errors, or any destructive behavior. Since no annotations exist, the description carries the burden and could be more explicit.
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 concise: two sentences that front-load the purpose and key behavioral note. No redundant or extraneous information. Could be more structured with bullets, but it is efficient and clear.
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 output schema exists, return values need not be explained. The description explains the purpose and state persistence, linking to sibling tools. It covers the essential context for creating a mixture, though it omits error conditions or prerequisites like valid mechanism files. Adequate for the tool's complexity.
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 provides detailed descriptions, examples, and constraints for all 5 parameters. The tool description adds no additional parameter information beyond mentioning the mixture is named. With high schema coverage, a baseline 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 verb 'Create' and the resource 'named mixture on the lab bench for kinetic analysis'. It also mentions storing a Cantera Solution object. This distinguishes it from sibling tools like list_lab_mixtures or get_mixture_properties which are for listing or reading.
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 context for when to use this tool: for kinetic analysis and subsequent reaction rate/pathway analysis. It implies that this is the creation step before using other analysis tools. However, it does not explicitly state when not to use it or alternatives.
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 bears the full burden. It discloses the fallback behavior and database coverage (~1000+ species including noble gases, metals), but does not specify what thermodynamic properties are calculated (e.g., enthalpy, entropy) or behavior on species not found. Output schema may cover return values, but the description could be clearer about edge cases.
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 concise with two brief paragraphs; the first sentence captures the essential purpose, and the second provides operational detail. It is well-structured and front-loaded, though it could be slightly more terse without losing clarity.
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 tool's complexity (thermodynamic calculations with fallback) and the presence of an output schema, the description covers the main workflow adequately. It mentions the databases and their scope, but does not specify the temperature range validity or list example output properties, which would improve completeness for a scientific tool.
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 already provides descriptions for each parameter, giving examples and default values. The tool description adds context about the fallback databases but does not enhance parameter meaning beyond what the schema offers. With schema coverage effectively high (all parameters documented), the description adds marginal semantic value, placing it at baseline 3.
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 calculates thermodynamic properties for a specific species and highlights the automatic database fallback behavior. It uses specific verbs ('Calculate') and identifies the resource ('thermodynamic properties for a specific species'), distinguishing it from siblings like 'get_species_properties' which might return different property types.
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 context on when to use this tool: for species not in GRI-Mech 3.0, it falls back to a broader database. It implies use cases (common combustion species vs. exotic species like metals), but does not explicitly state when not to use it or list alternatives, though the fallback mechanism gives some guidance.
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 provided, the description must disclose behavior. It states 'list all species', which implies a read-only operation with no side effects. This is transparent enough for a simple listing tool, though it could explicitly mention that it does not modify data.
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 a single, clear sentence with no superfluous information. It earns its place by being highly concise.
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 tool's simplicity (one parameter, existing output schema), the description is mostly adequate. However, it does not clarify what constitutes a 'mechanism file' (e.g., path, content) or that the mechanism must be pre-loaded, leaving minor ambiguity.
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?
The tool description does not elaborate on the parameter beyond repeating that it is a 'Cantera mechanism file'. The input schema has 0% description coverage for the top-level 'params' property, and the description does not compensate by explaining the parameter's format, constraints, or accepted values.
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 ('List') and resource ('species defined in a Cantera mechanism file'), clearly distinguishing it from siblings like 'list_available_mechanisms' which lists mechanisms, not species.
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 is used to list species from a mechanism file but provides no explicit guidance on when to use it versus alternatives like 'get_species_properties' or 'check_species_availability'. No when-not-to-use context is given.
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 provided, so description carries full burden. It states current conditions but does not disclose read-only behavior, side effects, or error handling (e.g., if mixture not found). Adequate but not fully transparent.
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 sentences, no fluff, front-loaded with purpose and usage. Every word 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?
Given output schema exists, return values are covered. Description includes prerequisite and usage context. Could mention output format briefly, but not required.
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?
Schema descriptions cover both parameters (name and threshold) fully, so description adds no new parameter info. Baseline 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 gets a list of fastest reactions in a mixture, with specific verb and resource. It distinguishes itself from sibling tools like 'get_mixture_properties' or 'compute_ignition_delay' by focusing on reaction rates.
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?
Provides explicit context: use to understand dominant reactions, and prerequisite (mixture must be created with create_lab_mixture). Does not list alternatives or when not to use, but still clear.
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 must convey behavioral traits. It states the tool identifies production/consumption reactions but does not mention side effects, permissions, or constraints beyond the limit parameter. The description is adequate but not exhaustive.
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 three sentences: purpose, examples, and prerequisite. It is concise, front-loaded with the core action, and contains no superfluous information.
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 that an output schema exists (per context signals), the description does not need to detail return values. It covers the tool's purpose, usage context, and a key prerequisite, making it sufficiently complete for an agent.
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?
The input schema provides detailed descriptions for all parameters, covering name, species, and limit. The description adds valuable context by linking the 'name' parameter to a mixture created via create_lab_mixture, enhancing understanding.
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 identifies the tool's function: identifying reactions that create or consume a specific species. The examples ('Where is the NO coming from?') provide concrete use cases, and the tool is distinct from siblings like get_species_properties.
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 states a critical prerequisite ('mixture must first be created using create_lab_mixture') and frames usage for pathway analysis with example questions. It does not explicitly exclude scenarios, but the context is clear.
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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