Aerospace MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Tools are generally distinct with clear descriptions, but there are several overlapping categories (e.g., multiple optimization methods, multiple orbital mechanics tools) that could cause confusion if descriptions are not read carefully.
Naming Consistency2/5
Tool names follow inconsistent patterns: some start with verbs (calculate_, get_, search_), others with nouns (airfoil_polar_analysis, hohmann_transfer). Underscores are used but no uniform verb_noun structure, making it harder to predict tool names.
Tool Count3/5
46 tools is high for a single server, covering a very broad domain. Includes several meta-tools (format_data_for_tool, select_aerospace_tool) that inflate the count. Could be streamlined, but many tools are necessary for the wide scope.
Completeness4/5
Covers most key aerospace domains: airfoil, wing, propeller, aircraft performance, rocket trajectory, orbital mechanics, coordinate transforms, atmospheric models, etc. Missing some niche areas but overall very comprehensive.
Average 3.9/5 across 46 of 46 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on January 17, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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?
With no annotations, the description carries full burden for behavioral disclosure. It mentions that errors are returned as strings (no exceptions) and returns a JSON string, but lacks details on side effects, required data formats, or performance implications, offering minimal 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 concise, with a clear structure (purpose, args, returns, raises), and front-loads the action. Every sentence adds value, though the args list could be slightly more compact.
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 (5 params, nested objects, output schema), the description covers basic functionality but omits details about optimization method, constraints, or typical use cases. It is adequate but not thorough.
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 description coverage is 0%, so the description adds meaning to all 5 parameters with brief clarifications (e.g., 'Burn time in seconds'). However, 'rocket_geometry' is vaguely described as 'Rocket geometry parameters' without specifying expected fields, leaving ambiguity.
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 tool optimizes rocket thrust profiles using trajectory optimization, specifying the resource and action. It does not explicitly differentiate from sibling tools like 'optimize_launch_angle' or 'genetic_algorithm_optimization', but the name and specificity make the purpose clear.
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 is provided on when to use this tool versus alternatives, nor are there any prerequisites, limitations, or contextual hints. This leaves the agent without direction for selection among similar optimization 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 mentions no exceptions are raised (errors as strings) and returns a JSON plan, but does not disclose constraints, input validation, or behavior for 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?
Description is concise with a clear first sentence. The docstring format adds structure, though some reformatting could reduce verbosity. Overall, every part earns its place.
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?
Given the complexity (three object parameters, nested objects) and lack of schema descriptions, the description is insufficient. It does not explain what constitutes valid orbital elements or rendezvous options, leaving the agent without enough detail to prepare inputs correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but description adds that chaser/target_elements are 'orbital elements' and rendezvous_options is 'optional rendezvous planning parameters', providing some context beyond the bare schema. However, it does not specify the required structure or available options.
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 plans orbital rendezvous maneuvers between two spacecraft, using a specific verb and resource. This distinguishes it from sibling tools like hohmann_transfer and lambert_problem_solver.
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. The description does not mention when not to use it or provide context for selection among many aerospace planning tools.
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?
Annotations are absent, so the description carries the full burden. It discloses that errors are returned as formatted strings and that an ImportError is caught. However, it does not mention possible limitations such as altitude range bounds or computational performance.
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 uses a clear docstring format with Args, Returns, and Raises sections. It is concise without extraneous information, though it could be slightly shorter 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?
For a tool with two parameters and no annotations, the description adequately covers input meanings, return format (formatted string), and error handling. It lacks explicit altitude range constraints but remains sufficiently complete for basic usage.
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 description explains 'altitudes_m' as a list of altitudes in meters and 'model_type' as ISA or enhanced, adding meaning beyond the schema's type and enum definitions. Despite 0% schema description coverage, the description compensates well.
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?
Clearly states the tool gets atmospheric properties (pressure, temperature, density) at specified altitudes using the ISA model. The verb 'Get' and specific resources are unambiguous. However, it does not explicitly differentiate from the sibling tool 'density_altitude_calculator', which may have overlapping functionality.
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 information is provided about when to use this tool over alternatives like density_altitude_calculator or other atmospheric tools. There is no mention of prerequisites or contextual cues for selection.
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 errors are returned as strings, not raised, and that output is a JSON string with results. However, it does not disclose computational cost, side effects, or whether the optimization is deterministic or stochastic.
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: a one-line summary, then bullet-pointed args, returns, and raises. Every sentence is informative with no redundancy.
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 description covers basic purpose and parameters, and the output schema exists to inform return structure. However, it lacks details on optimization algorithm, assumptions, and when to prefer this over similar tools. It is minimally complete but leaves gaps for a complex optimization 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?
Schema coverage is 0%, so the description adds value by explaining each parameter briefly (e.g., 'Rocket geometry parameters', 'Optional target range in meters'). However, it does not detail the expected structure of 'rocket_geometry' (which is a flexible object) or specify units for angle_bounds_deg.
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 purpose: 'Optimize rocket launch angle for maximum altitude or range.' This distinguishes it from siblings like 'optimize_thrust_profile' and 'rocket_3dof_trajectory' which handle different aspects of rocket optimization and simulation.
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 explicit guidance on when to use this tool versus alternatives like 'trajectory_sensitivity_analysis' or 'particle_swarm_optimization'. It does not mention prerequisites or situations where the tool is inappropriate.
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, the description fully discloses behavioral traits: it explains the iterative BEMT convergence process, describes the theoretical foundation, mentions that errors are returned as formatted strings, and notes that ImportError is caught. This goes beyond minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy, with a detailed Note section that is more theoretical than practical for tool usage. While structured with Args/Returns/Raises sections, it could be trimmed without losing essential guidance.
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 complexity (3 params, no annotations, output schema exists), the description covers return format, error handling, and some parameter hints. However, it lacks full specification of expected object keys and does not reference the output schema. It is adequate but not 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?
Schema coverage is 0%, so the description must compensate. It provides brief hints for each parameter (e.g., 'propeller_geometry: Propeller geometry (diameter_m, pitch_m, num_blades, etc.)'), but does not fully specify the expected structure or constraints. The examples help but are incomplete.
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 purpose: 'Analyze propeller performance using Blade Element Momentum Theory.' This is a specific verb+resource combination and distinguishes it from sibling tools like get_propeller_database, which retrieves data rather than performing analysis.
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 explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or what other tools might be more appropriate (e.g., for simpler performance estimates). There are no context signals about usage.
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 provided, the description carries full behavioral disclosure. It discloses that errors are returned as formatted strings, not thrown, and mentions 'database or advanced methods' but does not elaborate on computational cost, side effects, or whether the tool is read-only. This provides basic but incomplete 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 relatively concise: a one-sentence purpose followed by structured Args/Returns. It front-loads the key action. Minor redundancy could be removed, but overall it is efficiently structured.
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 complexity (4 parameters, output schema exists), the description covers purpose, parameters, returns format, and error handling. However, it lacks details like the source of 'advanced methods', typical use cases, or computational intensity, which would make it more complete for agent decision-making.
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 an 'Args' section explaining each parameter with examples (e.g., 'NACA2412' for airfoil_name) and defaults (e.g., alpha_range_deg defaults to [-10,20] deg). This adds significant meaning beyond the raw schema, though there is a slight discrepancy with the schema default of null for alpha_range_deg.
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 'Generate airfoil polar data (CL, CD, CM vs alpha) using database or advanced methods', providing a specific verb ('generate'), resource ('airfoil polar data'), and the coefficients involved. It distinguishes from sibling tools like 'get_airfoil_database' (retrieval) and 'wing_vlm_analysis' (3D wing analysis) by focusing on 2D polar generation.
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 does not provide explicit guidance on when to use this tool versus alternatives like 'get_airfoil_database' or 'wing_vlm_analysis'. While it implies usage for generating polar data, it lacks criteria for selection, exclusions, or prerequisites, leaving the agent to infer usage context.
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?
Explicitly describes error handling ('errors returned as formatted strings') and return format (JSON string). Since no annotations provided, the description effectively communicates read-only nature and safety profile.
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?
Structured with Args, Returns, Raises sections. Each sentence serves a purpose; no redundancy or fluff.
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?
Covers purpose, parameters, return type, and error behavior. Output schema exists, so return details are sufficient. Could mention typical usage or coordinate format but not essential.
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 coverage is 0%, but description adds meaning: clarifies orbital_state can be elements or state vector, and provides units for duration and time step. Lacks constraints or examples.
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?
Clear verb 'calculate' and resource 'ground track' from orbital state vectors. Implicitly distinct from sibling tools like 'propagate_orbit_j2' but lacks explicit differentiation.
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 'propagate_orbit_j2' or 'plan_flight'. Description only states what it does, not context.
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 discloses that errors are returned as JSON strings and no exceptions are raised directly, but it does not mention any behavioral traits like computational cost, required authority, or side effects. The description adds minimal transparency beyond stating what the tool computes.
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 with a clear introductory sentence, an Args section, and brief Returns and Raises sections. It efficiently conveys the necessary information without unnecessary verbosity.
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 description covers parameters and return values adequately given the presence of an output schema. However, it omits important contextual details such as the coordinate frame for position vectors (e.g., ECI) or a list of valid central_body names, which could lead to ambiguity for users unfamiliar with astrodynamics conventions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. The Args section provides clear, informative descriptions for each parameter: units for vectors and time, explanation of direction and revolutions, and a note on central_body. This adds significant meaning beyond the schema's type and default 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?
The description clearly identifies the tool as solving Lambert's orbital boundary value problem, specifying the inputs (two position vectors and time-of-flight) and the output (orbit connecting them). It also mentions its application in interplanetary mission design and rendezvous planning, which distinguishes it from sibling tools like hohmann_transfer or orbital_rendezvous_planning.
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 does not provide guidance on when to use this tool versus alternatives such as hohmann_transfer or porkchop_plot_analysis. It states the general purpose but lacks explicit context for when to choose this tool, nor does it mention situations where it should not be used.
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 is responsible for all behavioral disclosure. It mentions numerical integration and J2 perturbations, and notes that errors are returned as strings rather than exceptions. However, it omits any discussion of side effects, performance, or required permissions, leaving gaps.
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 with Args and Returns sections. Every sentence contributes useful information, and the front-loading of the purpose sentence makes it immediately clear what the tool does.
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, the description need not detail return values. It covers the three parameters adequately and mentions error handling. For a moderately complex orbital propagation tool, the description provides sufficient context for an AI agent to use it correctly, though more detail on initial state format would improve completeness.
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 coverage is 0%, so the description must compensate. It lists the three parameters with brief explanations (e.g., 'Initial orbital state (elements or state vector)') which adds meaning beyond the parameter names. However, it does not specify the expected format of the initial_state object or any constraints on the time step.
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 purpose: propagate orbit with J2 perturbations via numerical integration. The verb 'propagate' and resource 'orbit' are specific, and the mention of 'J2 perturbations' distinguishes it from other orbital tools on the server.
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. It lists arguments but does not specify scenarios, prerequisites, or cases where other tools (e.g., hohmann_transfer) would be more appropriate.
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 carry the full burden. It mentions no direct exceptions but returns errors as formatted strings, which is useful. However, it does not disclose if the operation is read-only, requires authentication, or has 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 structured as a docstring with Args, Returns, and Raises sections. It is informative without being verbose, but the Returns section could be more concise since it essentially repeats the purpose.
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 return format (JSON string with position and velocity in J2000 frame) and error handling. Given the complexity of a conversion tool with a single nested parameter, it is fairly complete, though it could explicitly mention the coordinate frame in the Returns.
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 has one parameter 'orbital_elements' as an object with no property definitions (0% schema coverage). The description adds examples like 'semi_major_axis_m, eccentricity, etc.', which provides some meaning, but it's still vague and does not list all expected fields or their types.
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 converts orbital elements to a state vector in the J2000 frame. It uses specific verb ('Convert') and resource ('orbital elements to state vector'), and is distinct from its inverse sibling 'state_vector_to_elements'.
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?
No explicit guidance on when to use this tool versus alternatives like 'state_vector_to_elements' or other orbital tools. The description lacks context for ideal usage scenarios or prerequisites.
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 the description must carry the full burden. It mentions external dependency OpenAP, internal error handling, and that errors are returned as strings. However, it does not state whether the tool is read-only or other behavioral traits.
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 fairly concise with clear sections for Args, Returns, Raises. It could be slightly more terse, but overall well-structured.
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 presence of an output schema, the description adequately covers inputs, return format, and error handling. It does not detail the output fields, but that is handled by the output schema.
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%, so the description adds essential meaning: aircraft_type is an ICAO code, distance_km in kilometers, cruise_altitude_ft in feet. It does not mention the default value for cruise_altitude_ft.
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 performance estimates for an aircraft type, with a specific verb and resource. It also notes the requirement for OpenAP, distinguishing it from siblings like get_airfoil_database.
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. The mention of OpenAP requirement implies a prerequisite but does not help an agent decide between this and other performance tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavioral traits: it uses RK4 integration, explains the equations of motion, handles errors by returning formatted strings (no exceptions), and depends on rocketry packages. This exceeds typical expectations.
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-organized (Args, Returns, Raises, Note) and front-loaded with purpose. However, the equations section is verbose; some repetition could be trimmed. Still, it is efficiently structured for clarity.
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 description explains the output (formatted string with key results) and the physics, but lacks detailed input guidance for the two required complex objects. Given the tool's complexity and minimal schema, more input context is needed for complete usage.
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 only provides vague labels like 'Rocket geometry parameters' and one example ('launch_angle_deg, launch_site, etc.'). It does not list expected keys, types, or constraints, leaving significant ambiguity.
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 a 3DOF rocket trajectory using numerical integration. It specifies the resource (rocket trajectory) and the method (numerical integration), distinguishing it from siblings like trajectory_sensitivity_analysis or optimize_launch_angle.
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 explicit guidance on when to use this tool versus alternative trajectory tools. It does not mention prerequisites, when not to use it, or compare to other tools like trajectory_sensitivity_analysis.
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, the description carries the full burden. It discloses that errors are returned as formatted strings (no exceptions raised), which is key behavioral info. However, it does not explicitly state the tool is read-only or safe for repeated calls, but the nature of analysis implies no side effects. The error handling disclosure is valuable.
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 moderately concise and well-structured with 'Args', 'Returns', and 'Raises' sections. It avoids fluff and adds value through structured documentation, though the docstring format slightly increases length. Overall, efficient.
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 (3 parameters, nested objects, no annotations) and rich sibling set, the description covers basic purpose and return format but omits input structure details. An output schema exists but is not described. The description is adequate for simple use but lacks depth for correct invocation.
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%, requiring the description to compensate. The description provides minimal one-line comments for each parameter (e.g., 'Baseline rocket geometry,' 'Parameters to vary and their ranges'), but lacks details on expected structure, required keys, or formats. For complex objects like 'rocket_geometry', this is insufficient.
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 'Perform sensitivity analysis on rocket trajectory parameters,' using a specific verb and resource. It distinguishes from siblings like 'rocket_3dof_trajectory' (which runs a trajectory) and 'monte_carlo_uncertainty_analysis' (probabilistic), making the purpose unambiguous.
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 use for understanding parameter impact on trajectory, but provides no explicit guidance on when to use this tool versus alternatives like 'monte_carlo_uncertainty_analysis' or 'optimize_launch_angle'. No when-not-to-use or exclusion criteria are given.
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?
The description discloses error handling (returns formatted strings, no exceptions), output structure (JSON string with derivative definitions), and the nature of the calculation. With no annotations, it provides sufficient behavioral context, though it omits potential limitations 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with Args, Returns, Raises sections and is front-loaded with purpose. While slightly verbose in repeating derivative definitions, it remains clear and structured, earning its length.
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 no annotations, the description covers inputs, outputs, and error behavior adequately. It explains nested object structures and includes defaults, though it could mention more about the derivation method or assumptions. Overall, it equips the agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description extensively details each parameter with keys, types, defaults, and optional fields (e.g., 'span_m: Wing span in meters', 'alpha_deg: Reference angle of attack (optional, default 2.0)'). This compensates fully for the sparse schema.
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 it calculates basic longitudinal stability derivatives for a wing, specifying the scope and output. However, it does not explicitly distinguish from sibling tools like wing_vlm_analysis, but the purpose is still specific and actionable.
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 lacks guidance on when to use this tool versus alternatives. It does not mention prerequisites, ideal scenarios, or when other tools might be more appropriate, leaving the agent without decision support.
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 burden. It states that errors are returned as formatted strings and lists key parameters, but lacks details on computational behavior, side effects, or limitations (e.g., convergence, state dimension compatibility).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description contains redundant phrases ('Extended Kalman Filter' and 'Kalman filter') and is lengthy due to the Args block. While structured and front-loaded with purpose, it could be more succinct without losing clarity.
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 complex with 7 parameters and no output schema, but the description covers inputs well and specifies the return type as a formatted string. However, it lacks examples, preconditions (e.g., state vector size consistency), and assumptions, which narrows completeness for a state estimation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no property descriptions (title only), but the description's Args section provides detailed explanations for all 7 parameters, including the mechanics of the 'measurements' array and enum options for 'dynamics_model'. This fully compensates for the schema's lack.
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 'Extended Kalman Filter for aircraft/spacecraft state estimation' and elaborates on sensor fusion and state estimation from noisy measurements. This distinguishes it from sibling tools like 'calculate_distance' or 'estimate_rocket_sizing'.
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 mentions 'sensor fusion' and 'state estimation' as typical use cases but does not explicitly differentiate from alternatives or provide when-not-to-use guidance. The dynamics_model options hint at different contexts but no comparative advice.
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?
The description discloses the return format (JSON string with categories and total count) and implies it is a read-only operation ('list'). Since no annotations are provided, the description carries the full burden, and it clearly communicates the output structure. However, it does not explicitly state that the tool is non-destructive or free of side effects, which would warrant a 5.
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, no extraneous information. It front-loads the core purpose and then specifies the return value. Every sentence is necessary and efficient.
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 listing tool with no parameters and an output schema (even if not shown), the description fully explains the input (none) and output (categories with counts). It covers the essential information an agent needs to invoke and interpret the result.
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 the schema is empty and coverage is trivial. Per guidelines, zero-parameter tools receive a baseline of 4. The description does not need to add parameter details.
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 tool lists tool categories with counts. The verb 'list' and resource 'tool categories' are explicit. However, it does not differentiate from siblings like 'search_aerospace_tools', which could also return category-related info, but the unique focus on categories and counts makes it distinct enough. A 5 would require explicit contrast.
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 is provided on when to use this tool versus alternatives (e.g., 'select_aerospace_tool'). The description only explains what it does, leaving the agent to infer usage context without exclusions or recommendations.
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 discloses that errors are returned as formatted strings and no exceptions are raised, which adds some transparency. However, it does not discuss side effects, authorized states, or any other behavioral traits beyond the return format.
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 the purpose front-loaded. The Args and Returns sections are clearly separated, but there is a minor redundancy in stating the return type both in the first line and the Returns section. Still, it is well-structured and avoids unnecessary words.
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 that the tool has an output schema (not shown) and handles a complex conversion, the description covers the basic input format and return structure. However, it omits details such as the units of the orbital elements (e.g., a in meters or km?) and what constitutes a valid state vector input (e.g., size of arrays). The error handling description is basic.
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 describes 'state_vector' as an object with no explicit structure, but the description adds significant meaning: it specifies it expects a dict with 'position_m' and 'velocity_ms' arrays, including units. This compensates for the 0% schema description coverage and clarifies the required keys.
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 starts with a clear verb and resource: 'Convert state vector to classical orbital elements.' It clearly specifies the action and the output, and the sibling tool 'elements_to_state_vector' provides a direct contrast, making the purpose unambiguous.
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 in orbital mechanics contexts ('convert state vector to classical orbital elements') but does not explicitly state when to use this tool versus alternatives, nor does it mention any prerequisites or special cases.
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 errors are returned as formatted strings and that ImportError is caught. It explains the VLM theory, which adds useful context beyond the schema. However, it doesn't mention side effects, performance constraints, or whether the tool is read-only. With no annotations, the description partially covers behavioral aspects but has gaps.
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 (Args, Returns, Raises, Note). It is somewhat verbose with the VLM technical explanation, but that adds educational value. Overall, it is reasonably concise and front-loaded with key 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 the tool's complexity and the presence of an output schema (formatted string), the description adequately explains return format and error handling. It covers parameter details thoroughly. Minor improvement would be to include example output or units, but current content is sufficient for interpretation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description compensates by detailing all keys, their types, defaults, and optionality for wing_config and flight_conditions. It adds significant meaning beyond the raw schema, which only defines top-level object types. This strongly helps an agent understand parameter usage.
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 analyzes wing aerodynamics using VLM or lifting line theory. It specifies the resource (wing configuration) and distinguishes from sibling tools like airfoil_polar_analysis or propeller_bemt_analysis, which focus on different aspects.
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 does not provide explicit guidance on when to use this tool over alternatives. It mentions two methods (VLM and lifting line) but doesn't explain when to choose one. No when-not-to-use or sibling comparisons are included.
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, the description explains the GA process (selection, crossover, mutation), error handling, and return structure (JSON with best solution, convergence history). This provides good behavioral insight beyond basic functionality.
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 well-structured with sections (Args, Returns, Raises, Note) and front-loaded purpose. While slightly lengthy, every paragraph adds value without redundancy.
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 an output schema exists, description adequately covers return format and algorithm internals. However, it could detail the expected structure of the optimization_problem object (e.g., required keys).
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 adds meaning to both parameters: 'optimization_problem: Problem definition (objective, constraints, variables)' and 'ga_parameters: Optional GA parameters (population_size, generations, etc.)'. This compensates for the missing schema descriptions.
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 explicitly states 'Optimize spacecraft trajectory using genetic algorithm', which is a specific verb-resource pair and clearly distinguishes from siblings like particle_swarm_optimization.
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 lacks guidance on when to use GA vs other optimization tools (e.g., particle swarm). No context on problem types suited for GA or 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses key behaviors: sampling from specified distributions, running n_samples simulations, statistical analysis, and error handling ('errors are returned as formatted strings'). It also mentions Latin Hypercube Sampling for efficiency.
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 a clear first sentence, then a parameter list, return description, and additional notes. It is concise for a complex tool, though the parameter list uses a docstring style that could be more compact.
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 and lack of annotations, the description covers purpose, parameters, return value (including statistical summaries), and sampling method. Output schema exists but its content is not shown; the description adequately summarizes expected output.
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%, so the description must compensate. It provides brief but clear parameter descriptions (e.g., 'uncertainty_parameters: Parameters with uncertainty distributions'), adding meaning beyond the schema's empty property definitions.
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 purpose: 'Perform Monte Carlo uncertainty analysis on spacecraft trajectory.' This is a specific verb ('perform') and resource ('Monte Carlo uncertainty analysis on spacecraft trajectory'), distinguishing it from siblings like 'trajectory_sensitivity_analysis'.
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 (e.g., sensitivity analysis, Kalman filtering). The description implies use for uncertainty propagation but does not provide when-not-to-use or list of 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?
Given no annotations, the description effectively discloses key behaviors: the PSO update formula, error handling (errors returned as formatted strings), and algorithmic details. However, it does not explicitly state whether the tool has side effects (it is likely read-only), nor does it mention prerequisites or rate limits, which would elevate 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 front-loaded with the purpose, then systematically covers args, returns, errors, and a note. The inclusion of the full update formula is dense but valuable. It is appropriately sized for a complex optimization tool, though some redundancy exists (e.g., repeating parameters in args and later).
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 presence of an output schema and the input schema's genericity, the description provides sufficient context for a scientific tool: it explains the algorithm, return format, and error handling. Minor gaps include no elaboration on variable types or constraints in the problem definition, but overall it meets the complexity level.
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 compensates by explaining both parameters: optimization_problem as 'Problem definition (objective, constraints, variables)' and pso_parameters as 'Optional PSO parameters (n_particles, iterations, etc.).' This adds meaningful structural hints beyond the raw schema, though it does not enumerate all subfields.
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 purpose: 'Optimize spacecraft trajectory using particle swarm optimization.' It specifies the verb (optimize), resource (spacecraft trajectory), and method (PSO), distinguishing it from sibling optimization tools like genetic algorithm or Monte Carlo.
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 like genetic_algorithm_optimization or monte_carlo_uncertainty_analysis. It neither suggests appropriate scenarios nor warns about limitations, leaving the agent without decision-making context.
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?
Without annotations, the description carries the full burden. It discloses the use of the ideal rocket equation, states that no exceptions are raised directly (errors are returned as strings), and describes the calculation methodology. It could mention that the tool is read-only, but overall it is transparent.
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 Args, Returns, Raises, and Note sections. The inclusion of the full Tsiolkovsky equation adds value but slightly increases length. Each sentence serves a purpose.
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 (four parameters, physics-based calculation) and the presence of an output schema, the description covers inputs, output format, and methodology. It lacks explicit constraints (e.g., positive values) but is otherwise complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description's 'Args' section provides explicit meanings for all four parameters, including defaults for propellant_type and design_margin. This fully compensates for the schema's lack of descriptions.
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 begins with 'Estimate rocket sizing requirements for target altitude and payload,' clearly stating the verb (estimate), resource (rocket sizing), and input conditions. It distinguishes from sibling tools like rocket_3dof_trajectory or optimize_launch_angle by focusing on mass and dimension estimation.
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 the governing equation (Tsiolkovsky) but offers no explicit guidance on when to use this simplified model versus alternatives like trajectory simulation. It lacks when-not-to-use advice or comparisons with sibling tools.
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 states it returns a JSON string and that errors are returned as formatted strings, not exceptions. With no annotations, it partially covers behavioral traits but lacks details on data freshness, availability, or performance impact.
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?
Description is extremely concise: two sentences plus a note about exceptions. No redundant information; 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 parameterless tool with an output schema, the description covers purpose, return type, and error handling. It is reasonably complete given the low complexity.
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 (input schema empty). Schema description coverage is 100% (by default). The description correctly states the resource and return format, which is sufficient for a parameterless tool.
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 the verb 'get' and specifies the resource 'propeller database' with geometric and performance data. It clearly distinguishes from sibling tools like propeller_bemt_analysis (which performs analysis) and get_airfoil_database (a different database).
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?
No explicit guidance on when to use this tool versus alternatives. The description implies usage for retrieving the database, but does not mention when to avoid or compare to siblings like propeller_bemt_analysis.
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 exist, so the description carries full burden. It discloses the return type as a JSON string with system status. Though it doesn't explicitly state read-only, the verb 'get' implies non-destructive 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?
Single sentence, no fluff, directly explains what the tool does. 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 the output schema exists, the return format note is sufficient. The description is complete for a simple, parameterless 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?
No parameters exist, and schema coverage is 100%. Baseline for 0 parameters is 4. Description adds no extra param info, but none is 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?
The description clearly states 'Get system status and capabilities', which is a specific verb+resource. It distinguishes itself from sibling tools that are domain-specific by being a general system utility.
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. For a generic tool among many specialized ones, explicit context would help the agent decide.
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, and the description does not disclose any behavioral traits beyond the calculation itself. It lacks information on side effects, resource usage, or error handling behavior beyond a brief note on error returns.
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 follows a clear docstring format with Args, Returns, Raises, and a Note. It is well-structured but includes detailed formulas, which while informative, add length. It could be slightly more concise without losing 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?
The description mentions that the tool returns a JSON string with specific parameters (delta-V, semi-major axis, time of flight) and notes error handling. Given the presence of an output schema and the simplicity of the tool (two parameters), the description is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description fully explains the two parameters (r1_m and r2_m) as initial and final orbit radii in meters, and provides the underlying formulas. This adds significant meaning beyond the schema, which has 0% description 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 'Calculate Hohmann transfer orbit parameters between two circular orbits,' specifying the verb, resource, and scope. It distinguishes the tool from siblings like 'orbital_rendezvous_planning' by focusing on the specific Hohmann transfer method.
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 explains when to use the tool via the note on Hohmann transfer being the minimum-energy two-impulse transfer between coplanar circular orbits. However, it does not explicitly state when not to use it or mention alternatives, slightly limiting guidance.
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 full burden. It mentions error handling (errors returned as strings) and return format (JSON string), but lacks details on computational complexity, assumptions (e.g., two-body problem), or required ephemeris 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 concise and well-structured, using a clear docstring format with summary, args, returns, and raises. Every sentence serves a purpose, and the key information is front-loaded in the first sentence.
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 (5 parameters, date ranges, optional settings) and an existing output schema (not shown), the description covers the essentials: input descriptions, return type, and error handling. It could specify allowed celestial body names or date format constraints more precisely, but is largely 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?
Despite 0% schema description coverage, the description provides meaningful docstring-style explanations for each parameter (e.g., 'Departure celestial body name', 'Range of departure dates (ISO format)'). This adds significant value beyond the schema's bare types and titles. However, 'analysis_options' is described vaguely as 'Optional analysis settings'.
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 starts with a specific verb and resource: 'Generate porkchop plot for interplanetary transfer opportunities.' It clearly distinguishes from sibling tools like hohmann_transfer or lambert_problem_solver by focusing on delta-V contour plots for launch window identification.
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 analyzing interplanetary transfer opportunities via a date-grid approach, but does not explicitly state when to use this tool versus alternatives (e.g., hohmann_transfer for impulsive maneuvers). No when-not or alternative suggestions are 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 it uses simplified equations for education and that errors are returned as strings, but lacks details on limitations or side effects. No annotations exist to contradict or support.
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 a purpose line, Args list, Returns, and Raises. Each sentence is informative, though the Args list could be slightly more integrated.
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?
Covers input, output (formatted string), and error handling. With an output schema present, the return format is sufficient. Minor gap: lacks units for output V-speeds or specific performance assumptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description provides a full Args list with clear, concise meanings for all 12 parameters (e.g., 'Takeoff weight in kg'), fully compensating for the schema gap.
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 'Calculate takeoff field length and V-speeds' which is a specific verb+resource. It distinguishes from siblings like 'landing_performance' by specifying takeoff.
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 mentions 'for educational purposes' implying a use case but does not explicitly state when to use this tool vs alternatives like 'get_aircraft_performance' or 'landing_performance'. No exclusions or alternatives are provided.
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, the description carries the full burden. It discloses that no exceptions are raised directly (errors returned as formatted strings) and specifies the return type (formatted string with results). This adds useful 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with Args, Returns, and Raises sections. Every sentence is informative with no unnecessary 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?
Given the tool's moderate complexity (3 params, nested objects, return is a formatted string), the description covers purpose, parameters, return value, and error handling. It is nearly complete, though more detail on parameter structure would improve it.
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 description coverage is 0%, so the description must compensate. It provides brief descriptions for each parameter (e.g., 'UAV configuration parameters') but lacks detail on the fields within these nested objects, limiting its value.
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: 'Estimate UAV flight time and energy consumption for mission planning.' This is specific and distinguishes it from sibling tools like airfoil_polar_analysis or calculate_distance.
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 use for mission planning but does not provide explicit guidance on when to use this tool versus alternatives, nor does it 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Details the two models with formulas and references, and states that errors are returned as formatted strings (no exceptions). With no annotations, this covers key behavioral aspects adequately.
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?
Well-structured with sections for args, returns, raises, and notes. Contains necessary formulas, but could be trimmed slightly 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?
Covers purpose, parameters, error handling, and references. With an output schema present (per context), not needing full return description. Lacks examples or typical usage scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description independently explains all 5 parameters with units and context (e.g., 'Wind speed at 10m reference height', model_type enum explained).
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 calculates wind speeds at different altitudes using specific models (logarithmic or power law). Distinguishes from siblings like get_atmosphere_profile by focusing on wind only.
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 alternative tools like get_atmosphere_profile or density_altitude_calculator. Lacks 'when not to use' or explicit context for selection.
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 provided, so description carries full burden. It explains the output format (JSON with km and NM plus bearings) and error handling (returns formatted strings, no exceptions). However, it does not disclose assumptions like Earth radius or formula used.
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?
Description is well-structured with Args, Returns, and Raises sections. Every sentence is informative, no fluff.
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 tool's simplicity and the presence of an output schema, the description covers purpose, all parameters, return format, and error handling. It is complete for its context.
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 coverage is 0%, so description must add value. The description lists the parameters by name (lat1, lon1, etc.) but adds no additional detail beyond what the schema provides (e.g., valid ranges, units). 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 purpose: 'Calculate great circle distance between two points.' It uses a specific verb (calculate) and resource (distance), and distinguishes itself from sibling tools which cover different aerospace calculations.
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?
No explicit guidance on when to use this tool versus alternatives. The description implies it is for great circle distance, but does not state when other distance methods might be preferred.
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 the conversion action, input parameters with units, return format (JSON with lat/lon/alt), and error handling (returns formatted strings). However, it lacks important behavioral details like the reference ellipsoid (e.g., WGS84) and precision, which are relevant for coordinate transformations.
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 with clear sections (Args, Returns, Raises). Every sentence serves a purpose, no fluff.
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 (3 parameters, no nested objects), the description covers inputs, outputs, and error handling. The presence of an output schema reduces the need to detail return structure, though missing reference ellipsoid is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining each parameter (x_m: X coordinate in meters, etc.), adding units and meaning beyond the schema names. This is highly valuable for correct usage.
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 explicitly states the tool converts ECEF coordinates to geodetic (lat/lon/alt) coordinates. It clearly identifies the verb 'Convert' and the resource 'ECEF to geodetic', and is easily distinguishable from sibling tools like geodetic_to_ecef.
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 does not provide explicit guidance on when to use this tool versus alternatives (e.g., transform_frames). Usage is implied by the name and description, but no context or exclusions are given.
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?
Without annotations, the description reveals key behaviors: uses GPT-5-Medium for formatting, returns JSON strings or error objects, and does not raise exceptions directly. It does not mention potential latency or costs of the LLM call, but covers the core functionality.
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 a clear docstring format (Args/Returns/Raises). It is mostly concise, though the opening sentence is somewhat redundant with the docstring.
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 no annotations, the description covers the tool's purpose, parameters, and return format adequately for an agent to decide when to use it. It explains the meta-tool nature relative to sibling tools, though more details on LLM behavior could enhance completeness.
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 description's Args section adds meaning beyond the schema: tool_name as the target tool name, user_requirements as user intent, raw_data as optional raw data. This compensates for the 0% schema 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 formats data for another aerospace-mcp tool using GPT-5-Medium. It specifies the verb 'format' and the resource 'data for a specific aerospace-mcp tool', distinguishing it from siblings that perform direct calculations or analyses.
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 when raw data or requirements need formatting for an aerospace tool, but does not explicitly state when not to use it or provide alternatives. Context suggests it is a helper tool, but no direct exclusion criteria are given.
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?
The description discloses the return format (JSON string) and error handling (errors as formatted strings). Since no annotations are provided, these details are essential. It does not mention side effects, but for a read-only tool this is acceptable.
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 extremely concise with a clear structure: a purpose sentence followed by Returns and Raises sections. Every sentence adds value and no unnecessary words are present.
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 tool with no parameters and an output schema, the description provides sufficient information: it states the output type, content, and error behavior. No gaps remain for the agent to use it correctly.
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 the schema is already clear. The description adds no additional parameter meaning, but none is needed. Baseline score of 4 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 an airfoil database with aerodynamic coefficients. The verb 'Get' and specific resource 'airfoil database' make the purpose unambiguous, distinguishing it from sibling tools like get_propeller_database or get_aircraft_performance.
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 is provided on when to use this tool versus alternatives. The description does not mention any preconditions, limitations, or alternatives, leaving the agent to infer usage context.
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?
Discloses return values (gain matrix, eigenvalues, stability, controllability) and error handling (returns formatted strings or JSON errors). No annotations, so description bears full burden; it covers key behaviors without missing critical info.
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?
Well-structured with intro, cost function, Args, Returns, Raises sections. Slightly verbose but all sentences earn their place. Could be more concise.
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 LQR complexity and no annotations, the description covers purpose, parameters, return, errors, and even controllability assessment. Output schema exists but description already explains return well. Complete for agent selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description provides thorough explanations: matrix dimensions, roles (system dynamics, control influence, weighting), and optional use of state/input names. Adds meaning beyond schema types.
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 'Design Linear Quadratic Regulator (LQR) optimal controller' with a specific verb and resource. It explains the cost function and output, distinguishing it from sibling aerospace tools.
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 or prerequisites like system controllability. The description assumes user knowledge without context.
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?
The description discloses that errors are returned as formatted strings and no exceptions are raised. It also explains the return type (formatted string). Given no annotations, this provides adequate transparency for a calculation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose and well-structured with parameter list and return info. It is slightly verbose but not wasteful, earning a high score.
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?
All required parameters are explained, optional parameters are documented with defaults, and return value is described. Given the tool's complexity (10 params), the description is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description provides detailed documentation for each parameter, including units and optional keys for payload_items. This fully compensates for the schema's lack of descriptions.
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 aircraft center of gravity and verifies limits. The verb 'calculate' and resource 'aircraft center of gravity' are specific, and the tool is distinct from sibling tools like takeoff_performance or landing_performance.
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. While the purpose is clear, there is no indication of scenarios where weight_and_balance is preferred over other tools like takeoff_performance.
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 provided, so description bears full burden. Describes inputs, output format (formatted string), and error handling (errors returned as strings). Missing side effects or computational limits.
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?
Well-structured with a brief opening, definition, and docstring-style Args/Returns/Raises. Front-loaded with core purpose, though Returns section is slightly verbose.
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?
Covers purpose, parameters, output (formatted string with specific values), and error handling. Output schema exists, so return values are partly defined. Lacks output units and validity ranges.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but description adds units and meaning for all three parameters: pressure_altitude_ft in feet, temperature_c in Celsius, dewpoint_c optional for humidity correction. Goes well beyond 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?
Clearly states the tool calculates density altitude from pressure altitude and temperature, with a definition and significance. Distinct from siblings like stall_speed_calculator.
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?
Mentions essential for aircraft performance calculations but does not specify when to use vs alternatives or exclusion conditions. No guidance on when not to use.
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 return format (formatted string with components) and error handling (errors returned as formatted strings). For a read-only calculator, this is transparent, though no explicit statement about 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 well-structured docstring with purpose, args, returns, and raises. It is concise with no unnecessary information, and the purpose is front-loaded.
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 inputs, outputs, and error handling. With 6 parameters and an output schema (not visible but indicated), it is largely complete. However, specifics on how each regulation affects calculations could enhance completeness.
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 has 0% description coverage, but the description includes an Args section with brief explanations for all parameters (e.g., 'trip_fuel_kg: Planned trip fuel in kg'). This adds meaning beyond the schema titles.
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 'Calculate required fuel reserves per aviation regulations.' This provides a specific verb and resource, and the tool is distinct from siblings which are other aerospace calculations.
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 gives a clear context (aviation regulations) but does not provide explicit guidance on when to use this tool vs alternatives. Usage is implied but no exclusions or alternative tools are mentioned.
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 the full burden. It details the WGS84 ellipsoid parameters, conversion equations, and notes that exceptions are not raised but errors returned as strings. This is transparent about the mathematical model and error handling, though it omits any side effects or authorization needs (likely none).
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 Args, Returns, Raises, and Note. However, it includes detailed formulas and ellipsoid parameters which, while informative, could be more concise. The first sentence efficiently captures the purpose, and the structure aids readability.
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 tool's moderate complexity (3 parameters, no nested objects) and the presence of an output schema (indicated), the description covers parameters, return format, error handling, and the underlying model completely. No significant gaps remain.
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% description coverage, so the description is essential. It adds meaningful context beyond the schema: specifies valid ranges for latitude and longitude, explains altitude above WGS84 ellipsoid, and notes the default of 0 for altitude. This sufficiently compensates for the bare 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 'Convert geodetic coordinates (lat/lon/alt) to Earth-centered Earth-fixed (ECEF) coordinates.' This specifies the exact operation and resource, and the sibling list includes 'ecef_to_geodetic' which implicitly distinguishes the inverse direction.
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 does not explicitly state when to use this tool versus alternatives. It implies usage for geodetic-to-ECEF conversion, but does not mention the inverse tool or any conditions for choosing among sibling tools.
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 full responsibility. It mentions that errors are returned as formatted strings, which is helpful, but it does not disclose other behaviors such as performance characteristics or prerequisites.
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 well-structured docstring with clear sections for Args, Returns, and Raises. It is concise yet covers all necessary details without superfluous content.
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 tool's complexity (10 parameters) and lack of parameter descriptions in the schema, the description fully explains inputs and return format, making it complete for use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides a detailed explanation for each parameter, including units and roles (e.g., 'weight_kg: Landing weight in kg'). This adds significant value beyond the schema, which only has titles and types.
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 landing distance for given conditions. The tool name supports this purpose, and among siblings like 'takeoff_performance' and 'stall_speed_calculator', it is distinct.
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 lacks explicit guidance on when to use this tool versus alternatives. It only implies usage through the calculation purpose, but does not provide context for exclusion or mention sibling 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?
With no annotations, the description carries the full burden. It explains that errors are caught internally and returned as strings, and it outlines the return structure. This provides good insight into 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 well-structured with 'Args', 'Returns', and 'Raises' sections. Every sentence adds value, and there is no redundancy. It is appropriately sized for the tool's complexity.
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 (implied by the detailed return description), the description adequately covers return values. It also mentions error handling. However, it lacks information about prerequisites or required permissions, but these are not critical for this 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?
The schema has 0% description coverage, but the description adds meaning by specifying that 'departure' and 'arrival' are dicts with 'city', 'country', 'iata', and that 'aircraft' includes 'ac_type', 'cruise_alt_ft', 'route_step_km'. This significantly compensates for the schema's lack of detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Plan a flight route between two airports with performance estimates', which is a specific verb+resource combination. This clearly distinguishes it from sibling tools like 'calculate_distance' or 'get_aircraft_performance'.
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 flight route planning but does not explicitly state when to use this tool versus alternatives like 'calculate_distance' or 'get_aircraft_performance'. There is no when-not-to-use 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?
No annotations are provided, so the description carries the full burden. It explains the function, behavior (no exceptions, errors returned as strings), and output format. However, it does not disclose any side effects or resource usage, which are unlikely for a calculator.
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 structured as a docstring with sections for args and returns, making it readable. It is somewhat lengthy but each sentence adds value. The main purpose is front-loaded in the first sentence.
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 7 parameters and an output schema (not shown), the description covers parameter meanings, return format (formatted string with specific speeds), and error handling. It lacks edge cases or performance notes but is sufficient for a calculator tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description includes a docstring that explains each parameter's purpose and units, e.g., 'weight_kg: Aircraft weight in kg.' This adds substantial semantic meaning beyond the schema's type-only 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?
The description starts with 'Calculate stall speeds for different aircraft configurations,' which clearly states the action and target resource. This differentiates it from sibling tools like landing_performance or takeoff_performance, which have different scopes.
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 stall speed calculations but does not explicitly state when to use this tool versus alternatives like takeoff_performance or landing_performance. There is no mention of use cases or exclusions, leaving it to the agent to infer context.
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 full burden. It discloses that errors are returned as strings, ImportError is caught, and the epoch is optional. It does not explicitly state read-only nature, but the function is clearly a pure transformation.
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 front-loaded with a clear one-sentence purpose, followed by concise Args/Returns/Raises sections. No unnecessary words; 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?
Given the presence of an output schema (not shown but indicated), the description adequately covers purpose, parameters, and error handling. It could be improved by noting that this tool subsumes specific sibling converters, but overall it is 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?
Schema coverage is 0%, so description compensates well. It explains coordinates as a dict with format dependent on frame, from/to frames as source/target, and epoch_utc as optional ISO format. This adds meaningful context beyond the schema's enums and generic object.
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 transforms coordinates between specific reference frames (ECEF, ECI, ITRF, GCRS, GEODETIC), using a specific verb (Transform) and resource (coordinates). It distinguishes from siblings like ecef_to_geodetic by covering all listed frames.
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 coordinate transformation between any of the listed frames but does not explicitly differentiate from specific sibling converters (e.g., ecef_to_geodetic). No when-not-to-use or alternative recommendations are 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?
No annotations provided, so description carries full burden. It discloses use of GPT-5-Medium and that errors are returned as strings, but does not mention side effects, authentication, or model reliability.
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, using a few sentences followed by clear args/returns/raises sections. Every part adds value 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?
Given the tool's meta-nature and presence of output schema (not shown), the description covers purpose, parameters, return structure, and error handling, providing a complete picture for usage.
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 description compensates by describing both parameters (user_task and user_context) in detail, adding meaning beyond schema titles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool selects the most appropriate aerospace-mcp tool for a given task, using GPT-5-Medium. It clearly distinguishes from sibling calculation tools by being a meta-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 implies when to use (when uncertain about tool choice) and provides guidance via args/returns, but lacks explicit when-not-to-use or alternatives, though no direct alternative exists on this server.
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 details the conversion process, input constraints (e.g., units), and the return format (formatted string with all equivalents). It also notes that no exceptions are raised directly, providing transparency on error handling.
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 structured with a clear docstring format listing arguments, returns, and raises. It is moderately concise with no wasted sentences, though some lines (e.g., the raises note) could be integrated.
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 5-parameter tool with an output schema, the description covers all inputs, explains the return value comprehensively, and mentions error handling. It fully addresses the complexity of airspeed conversions, leaving no major gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains each parameter with units and allowed values (e.g., 'speed_type' enum values, 'altitude_ft' in feet, 'temperature_c' optional with ISA default). Since the input schema has 0% description coverage, the docstring adds essential meaning 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 specifies the verb 'Convert' and the exact resources ('IAS, CAS, EAS, TAS, and Mach number'). This is a specific and distinct functionality among siblings, which include other aerospace calculators but not an airspeed converter.
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 explains what the tool does but does not explicitly state when to use it versus alternatives. Since there is no sibling tool for airspeed conversion, the context is implicit, but lack of usage scenarios or prerequisites reduces clarity.
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, the description carries the full burden. It discloses that errors are returned as formatted strings rather than exceptions, and explains query_type behavior. This is sufficient for a non-destructive lookup 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 docstring format with Args/Returns/Raises is well-structured and clear, though slightly verbose. The purpose is front-loaded. Minor redundancy does not detract significantly.
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 is simple (3 params, no nested objects), and the description covers all aspects: functionality, parameter usage, error handling, and return format. No additional context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description provides full meaning for all parameters: query accepts IATA or city, country is optional ISO filter, query_type controls detection mode. This far exceeds schema info.
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 'Search for airports by IATA code or city name,' which is a specific verb+resource. Among numerous aerospace calculation siblings, this lookup tool is distinct and easily differentiated.
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?
While no explicit when-to-use guidance is given, the tool's purpose is clear and siblings are unrelated. The description does explain optional filters and query types, aiding appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: search modes (regex, text, auto), max_results limit, category filter, return format (JSON with tool references), and error handling (errors returned in JSON, no exceptions). This provides complete behavioral 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 a clear purpose statement, followed by a usage benefit paragraph, then an Args section. Every sentence adds value, though it could be slightly more concise by merging the first two sentences. Overall, it is appropriately sized and front-loaded.
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 no annotations and the presence of an output schema (mentioned but not shown), the description covers all essential context: input parameters with details, return format, and error handling. It is complete for a search/discovery tool with 4 parameters and a clear result type.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description explains each parameter in detail: query (regex pattern or natural language), search_type (modes with examples), max_results (default 5, max 10), category (optional filter with examples). This fully compensates for the schema's lack of descriptions.
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 searches for aerospace-mcp tools by name, description, or functionality. It uses a specific verb-resource pair ('search for aerospace-mcp tools') and explicitly differentiates from sibling tools by noting it enables dynamic tool discovery without loading all 34+ definitions upfront.
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 explains the tool allows finding relevant tools without loading all definitions, implying use when tool discovery is needed. However, it does not explicitly state when not to use it or list alternative tools like select_aerospace_tool, so it lacks exclusion guidance.
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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