NASA MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific NASA data sources or services, with no overlap in functionality. For example, jpl_sbdb provides asteroid/comet data, while nasa_apod fetches daily astronomy images, and nasa_firms offers fire data—all serve unique roles.
Naming Consistency5/5
Tool names follow a highly consistent pattern with a clear prefix (jpl_ or nasa_) indicating the source, followed by a descriptive identifier (e.g., cad, fireball, apod). This structured naming makes it easy to identify and categorize tools by domain and origin.
Tool Count4/5
With 23 tools, the count is slightly high but reasonable given the broad scope of NASA's diverse data services. It covers multiple domains like astronomy, Earth science, and space weather without being excessive, though it may feel heavy for some use cases.
Completeness5/5
The tool set provides comprehensive coverage across NASA's data offerings, including astronomy (apod, exoplanet), Earth observation (eonet, epic, firms), solar system objects (jpl tools), and specialized services (donki, power). No obvious gaps exist for accessing NASA's public data resources.
Average 2.7/5 across 23 of 23 tools scored. Lowest: 1.7/5.
See the Tool Scores section below for per-tool breakdowns.
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but offers none. It doesn't indicate whether this is a read-only query, a computational tool, or a data source; it lacks information on rate limits, authentication needs, output format, or any behavioral traits like pagination or 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.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is under-specified rather than concise—it's a single phrase that fails to convey essential information. While it's brief, it doesn't earn its place by being informative; it lacks structure and front-loading of key details, making it inefficient for agent understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of 11 parameters, no annotations, and no output schema, the description is severely incomplete. It doesn't explain what the tool does, how to use it, or what to expect in return, failing to compensate for the lack of structured data and leaving the agent with insufficient 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?
The schema description coverage is 100%, with detailed descriptions for all 11 parameters, so the description doesn't need to add parameter semantics. The description itself provides no additional parameter information, but the schema adequately covers meanings, making a baseline score of 3 appropriate as it doesn't compensate or detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'JPL Three-Body Periodic Orbits Database' is a tautology that restates the tool name 'jpl_periodic_orbits' without specifying what action the tool performs. It doesn't clarify whether this tool retrieves, searches, lists, or analyzes orbits, nor does it differentiate from sibling tools like 'jpl_horizons' or 'nasa_exoplanet' which might also involve orbital data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 doesn't mention any context, prerequisites, or exclusions, and it fails to reference sibling tools that might handle related data (e.g., 'jpl_horizons' for ephemeris or 'nasa_exoplanet' for exoplanet orbits), leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. However, the description only names the database without explaining what the tool does (e.g., whether it retrieves, searches, or lists data), what the output looks like, any rate limits, authentication needs, or side effects. It fails to provide any behavioral traits beyond the name, leaving the agent with insufficient information to understand the tool's operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single phrase that is under-specified rather than concise. It does not front-load critical information (e.g., the tool's action) and wastes space by merely expanding the acronym 'donki'. A more helpful description would use this limited space to clarify the tool's purpose, but it fails to do so, making it inefficient despite its brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a database query tool with 3 parameters), no annotations, and no output schema, the description is incomplete. It does not explain what the tool returns, how to interpret results, or any behavioral context. The description alone is inadequate for an agent to understand how to use this tool effectively, especially compared to more descriptive sibling tools.
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 100% description coverage, with clear documentation for all three parameters ('type', 'startDate', 'endDate'). The description adds no additional meaning beyond what the schema provides, as it does not mention parameters at all. According to the rules, with high schema coverage (>80%), the baseline score is 3 even without param info in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Space Weather Database Of Notifications, Knowledge, Information' is a tautology that restates the tool name 'nasa_donki' (which stands for 'Database Of Notifications, Knowledge, Information'). It does not specify what action the tool performs (e.g., 'retrieve', 'search', 'list') or what resource it operates on beyond the database name. While it hints at space weather events, it lacks a clear verb+resource statement that distinguishes it from sibling tools like 'nasa_apod' or 'nasa_neo'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 does not mention any context, prerequisites, or exclusions for usage. Given the sibling tools include various NASA data sources (e.g., 'nasa_apod' for astronomy pictures, 'nasa_neo' for near-Earth objects), there is no indication of how this tool differs or when it should be selected over others for space weather-related queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. However, it only states 'fire data' without explaining what the tool does (e.g., returns fire detection points, historical data), any constraints (e.g., rate limits, data freshness), or output format. This is inadequate for a tool with parameters and no output schema.
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 single phrase, but it is under-specified rather than efficiently informative. It wastes no words but fails to provide essential details, making it more of a placeholder than a helpful description. It is front-loaded but lacks substance.
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 tool's complexity (3 parameters, no output schema, no annotations), the description is incomplete. It does not explain what the tool returns, how to interpret results, or any behavioral aspects like data sources or limitations. Without annotations or output schema, the description should compensate but does not.
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 100% description coverage, with clear parameter definitions (latitude, longitude, days). The description adds no parameter-specific information beyond implying fire data retrieval, which is already suggested by the tool name. According to the rules, with high schema coverage (>80%), the baseline score is 3, as the schema carries the burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'NASA Fire Information for Resource Management System - fire data' restates the tool name 'nasa_firms' and adds only generic context about fire data. It lacks a specific verb (e.g., 'retrieve', 'query', 'fetch') and does not distinguish this tool from its many NASA-related siblings, such as 'nasa_eonet' or 'nasa_donki', which also handle environmental or space data. The purpose is vague and tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 does not mention any specific use cases, prerequisites, or exclusions, nor does it reference sibling tools like 'nasa_eonet' (which might also handle fire or environmental data) for comparison. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It fails to do so—it doesn't mention whether this is a read-only query, a data processing tool, or something else. There's no information on permissions, rate limits, output format, or potential side effects. For a tool with 9 parameters and no annotations, this is a critical omission.
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 a single, efficient phrase: 'Human-accessible NEOs (Near-Earth Objects) data'. It's front-loaded and wastes no words, though it could be more informative. It earns a 4 for being concise, but loses a point because it under-specifies rather than being optimally informative.
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 (9 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool does, how to use it, or what to expect in return. The schema handles parameter details, but the description fails to provide necessary context about the tool's operation, making it inadequate for an agent to use effectively.
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 100% description coverage, providing clear details for all 9 parameters (e.g., dv as 'Minimum total delta-V', plot as 'Include base-64 encoded plot image'). The description adds no additional parameter semantics beyond what the schema already documents. According to the rules, with high schema coverage (>80%), the baseline score is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Human-accessible NEOs (Near-Earth Objects) data' states the general resource (NEO data) but lacks a specific verb or action. It doesn't clarify whether this tool retrieves, filters, analyzes, or visualizes NEO data, nor does it differentiate from sibling tools like 'jpl_sbdb' or 'nasa_neo' that likely handle similar data. The purpose is vague but not tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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. With multiple sibling tools related to NEOs and JPL data (e.g., jpl_sbdb, jpl_sentry, nasa_neo), there is no indication of context, prerequisites, or exclusions. This leaves the agent guessing about appropriate use cases.
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 full burden for behavioral disclosure. It mentions 'File Input' but doesn't explain what this entails (e.g., does it generate a downloadable file, require file uploads, or output file-like data?). It lacks details on rate limits, authentication needs, error handling, or output format implications beyond the 'format' parameter. The description is insufficient for a tool with 11 parameters and no annotations.
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 a single, efficient phrase that states the core function. It's front-loaded with the key information (JPL Horizons ephemeris data). However, it could be more structured by clarifying 'File Input' to improve usefulness without adding unnecessary length.
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 tool's complexity (11 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain the behavioral aspects (like file handling), usage context, or how results are returned. For a data retrieval tool with many configuration options, more guidance on output expectations and tool behavior is needed to be fully helpful.
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 100%, with all parameters well-documented in the schema (e.g., COMMAND specifies target objects, EPHEM_TYPE lists types). The description adds no parameter-specific information beyond the tool's general purpose. According to guidelines, with high schema coverage (>80%), the baseline is 3 even without param details in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool provides 'Solar system objects ephemeris data (File Input)', which indicates it retrieves positional/observational data for celestial bodies. However, it's vague about what 'File Input' means specifically and doesn't distinguish this tool from its sibling 'jpl_horizons' (without 'file' in name). The purpose is understandable but lacks specificity about the file aspect.
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. Given the sibling tools include 'jpl_horizons' (likely a similar tool without file input), there's no indication of when this file-based version is preferred or what distinguishes it. No context about prerequisites or exclusions is mentioned.
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 of behavioral disclosure. It mentions 'tracker' and 'data,' which implies a read-only operation, but doesn't specify whether it's a query, search, or list function, nor does it detail rate limits, authentication needs, or response formats. The description is too minimal to adequately inform behavior beyond basic intent.
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 very concise with a single phrase, 'Earth Observatory Natural Event Tracker - natural events data,' which is front-loaded and wastes no words. However, it may be overly brief, bordering on under-specification, but it efficiently conveys the core idea without redundancy.
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 of a 5-parameter tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, how results are structured, or provide enough context for effective use. The description fails to compensate for the lack of structured data, leaving significant gaps in understanding.
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 100% description coverage, with all 5 parameters clearly documented in the schema itself. The description adds no additional meaning or context about the parameters beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline score is 3, as the schema does the heavy lifting and the description doesn't compensate or add value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Earth Observatory Natural Event Tracker - natural events data' states what the tool does at a high level but lacks specificity. It mentions tracking natural events data but doesn't specify the exact action (e.g., 'retrieve' or 'search') or clearly distinguish it from sibling tools like 'nasa_firms' or 'jpl_fireball' which might also handle natural events. The purpose is vague rather than precise.
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. With many sibling tools available (e.g., 'nasa_firms' for fires, 'nasa_neo' for near-Earth objects), there's no indication of context, prerequisites, or exclusions. Usage is implied only by the tool name and title, but no explicit guidelines are given.
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 of behavioral disclosure. It mentions 'views of Earth' but does not specify if this is a read-only operation, what data format is returned, or any constraints like rate limits or authentication needs. This is inadequate for a tool with no annotation coverage.
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 a single, efficient phrase with no wasted words. However, it is under-specified rather than concise, as it lacks essential details about the tool's function, which slightly reduces its effectiveness.
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 of a NASA data tool with no annotations and no output schema, the description is incomplete. It fails to explain what the tool returns, how to interpret results, or any behavioral traits, leaving significant gaps for the agent to understand its use.
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 100%, so the schema fully documents the parameters (collection and date). The description adds no additional meaning or context about these parameters, such as example values or usage tips, resulting in the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Earth Polychromatic Imaging Camera - views of Earth' identifies the resource (Earth images from EPIC) but lacks a specific verb or action, making the purpose vague. It does not clearly state what the tool does (e.g., retrieve, list, or analyze images), and it does not differentiate from siblings like 'nasa_images' or 'nasa_gibs'.
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 context, prerequisites, or exclusions, leaving the agent without direction on tool selection among the many NASA-related siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It fails to describe any behavioral traits such as whether the tool is read-only or mutative, authentication requirements, rate limits, error handling, or response format. The description only states what data is accessed, not how the tool behaves operationally.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized for a simple data access tool, though it could be slightly more informative without losing conciseness. The structure is front-loaded with the key information.
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 tool's complexity (5 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral aspects, usage context, and output expectations, which are critical for an AI agent to invoke the tool correctly. The description does not compensate for the absence of annotations or output schema, leaving significant gaps in understanding.
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 100% description coverage, with all 5 parameters documented in the schema itself. The description adds no additional meaning beyond the schema, such as examples of valid 'table' names or 'where' clause syntax. However, since schema coverage is high, the baseline score of 3 is appropriate as the schema adequately handles parameter semantics.
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's purpose as accessing 'NASA Exoplanet Archive - data about planets beyond our solar system,' which specifies the resource (NASA Exoplanet Archive) and data domain (exoplanets). However, it lacks a specific verb (e.g., 'query' or 'retrieve') and does not differentiate from sibling tools like 'nasa_neo' or 'nasa_apod,' which also access NASA data but for different topics.
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 does not mention any specific contexts, prerequisites, or exclusions, nor does it refer to sibling tools that might handle related data (e.g., 'jpl_cad' for comet/asteroid data). Usage is implied only by the resource name, with no explicit instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description only states what data is provided without mentioning any behavioral traits: no information about rate limits, authentication requirements, data freshness, response time, error conditions, or what format the 'prediction' actually takes. For a data retrieval tool with 7 parameters, this leaves critical behavioral aspects undocumented.
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 at just 7 words, with zero wasted language. It's front-loaded with the core purpose and doesn't contain any unnecessary elaboration. Every word earns its place by establishing the tool's domain and data type.
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 of a 7-parameter meteorological data tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'prediction' means in this context, what timeframes or resolutions are available, what the output looks like, or any limitations of the data. For a tool that presumably returns complex meteorological predictions, more contextual information is needed.
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 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain what 'RE, SB, AG' communities represent, what meteorological parameters are available, or provide examples of valid parameter combinations. With complete schema coverage, the baseline score of 3 is appropriate.
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's purpose as providing 'Prediction of Worldwide Energy Resources - meteorological data', which is a specific verb+resource combination. It distinguishes itself from siblings by focusing on energy resource predictions rather than astronomy, imagery, or other NASA data domains. However, it doesn't explicitly differentiate from potential meteorological data tools within the NASA suite.
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. With multiple NASA data tools available (like nasa_cmr, nasa_firms, nasa_gibs), there's no indication of when meteorological energy resource data is preferable to other environmental or imagery data sources. The description offers no context about use cases or exclusions.
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 full burden. It mentions temporal scope ('past and future') but doesn't disclose critical behavioral traits: whether this is a read-only query, what data sources it uses, potential rate limits, authentication requirements, or what format/volume of results to expect. For a 10-parameter tool with no annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently communicates the core purpose. It's appropriately sized and front-loaded with essential information, with zero wasted words or redundant phrasing.
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 tool's complexity (10 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what kind of data is returned, how results are structured, whether there are pagination limits, or what planetary bodies are supported beyond the 'body' parameter hint. For a data query tool with rich parameters, more context is needed.
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 100%, so the schema already documents all 10 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline score is 3 even with no param info in description.
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's purpose: finding asteroid and comet close approaches to planets in past and future. It specifies the resource (asteroids/comets) and action (finding close approaches), though it doesn't explicitly differentiate from sibling tools like jpl_sentry or nasa_neo which might 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?
The description provides no guidance on when to use this tool versus alternatives. With multiple JPL and NASA tools available (like jpl_sentry for impact risk or nasa_neo for near-Earth objects), there's no indication of this tool's specific use case, prerequisites, or limitations compared to siblings.
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 of behavioral disclosure. The description only states what the data is ('fireball data - atmospheric impact events') without explaining how the tool behaves—such as whether it queries a database, returns historical or real-time data, requires authentication, has rate limits, or what the output format might be. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational characteristics.
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 ('Fireball data - atmospheric impact events'), consisting of a single phrase that efficiently conveys the core purpose. It is front-loaded with the key information and contains no redundant or unnecessary details, making it highly efficient for quick understanding.
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 of querying fireball data with three parameters and no annotations or output schema, the description is incomplete. It lacks information on behavioral traits, output format, and usage context, which are crucial for an agent to effectively invoke the tool. The high schema coverage helps with parameters, but other aspects remain underspecified, making it inadequate for full contextual understanding.
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 100% description coverage, with clear documentation for 'limit', 'date_min', and 'date_max'. The description adds no additional parameter semantics beyond what the schema provides, such as explaining default behaviors or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract from the well-documented 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 'Fireball data - atmospheric impact events' clearly indicates this tool retrieves data about fireballs (atmospheric impact events). It specifies the resource ('fireball data') and the domain ('atmospheric impact events'), making the purpose understandable. However, it doesn't explicitly state the action verb (e.g., 'retrieve' or 'query') or differentiate from sibling tools like 'nasa_neo' or 'jpl_sentry' which might also handle impact-related data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any specific contexts, prerequisites, or exclusions, nor does it reference sibling tools like 'jpl_sentry' or 'nasa_neo' that might handle similar impact data. Without such information, an agent must infer usage from the tool name and description alone.
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 full burden for behavioral disclosure. While 'ephemeris data' implies a read-only query operation, the description doesn't specify whether this requires authentication, has rate limits, returns real-time or historical data, or what happens with invalid inputs. For an 11-parameter tool with no annotation coverage, this is a significant gap in behavioral context.
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 at just 6 words: 'JPL Horizons - Solar system objects ephemeris data'. Every word earns its place - it identifies the system (JPL Horizons), the domain (Solar system objects), and the function (ephemeris data). There's zero waste or redundancy, and the information is front-loaded effectively.
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?
For an 11-parameter tool with no annotations and no output schema, the description is inadequate. It doesn't explain what ephemeris data includes, what formats are returned, whether this is for observational planning or research, or how results are structured. The description should provide more context about this being a professional astronomy tool for precise positional calculations, not just 'ephemeris data'.
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 100%, so the schema already documents all 11 parameters thoroughly with descriptions and enums where applicable. The description adds no additional parameter information beyond what's in the schema - it doesn't explain relationships between parameters (like how EPHEM_TYPE affects other parameters) or provide usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.
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's purpose: 'JPL Horizons - Solar system objects ephemeris data'. It specifies the verb ('ephemeris data') and resource ('Solar system objects'), making it clear this tool retrieves positional/observational data for celestial bodies. However, it doesn't differentiate from siblings like 'jpl_sbdb' (Small-Body Database) or 'jpl_nhats' (Near-Earth Object hazard assessment), which may also involve solar system objects.
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. With 22 sibling tools including other JPL tools (like 'jpl_sbdb' for small-body data) and NASA tools (like 'nasa_neo' for near-Earth objects), there's no indication of when this ephemeris-focused tool is appropriate versus those alternatives. The description doesn't mention any prerequisites, constraints, or typical use cases.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the conversion function without mentioning any behavioral traits such as error handling, input validation, rate limits, or output format. This is inadequate for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any redundant information. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., conversion results, error messages) or provide any context about the conversion process, which is essential for a tool performing date/time transformations.
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 100% description coverage, clearly documenting both parameters ('jd' and 'cd') with their purposes and formats. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.
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's purpose as a 'Julian Day number to/from calendar date/time converter', specifying the conversion direction (to/from) and the data types involved. However, it doesn't differentiate from sibling tools like 'jpl_horizons' or 'nasa_apod', which appear to serve different astronomical purposes.
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 doesn't mention any specific contexts, prerequisites, or exclusions, leaving the agent to infer usage based solely on the purpose statement.
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 full burden but offers minimal behavioral insight. It mentions data retrieval but doesn't cover rate limits, authentication needs, response format, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its operation.
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—a single phrase that efficiently conveys the core purpose without waste. It's front-loaded with the key information (SBDB and data type), making it easy to parse quickly.
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 no annotations, no output schema, and multiple sibling tools, the description is incomplete. It fails to address behavioral traits, usage context, or output details, leaving the agent with insufficient information to effectively invoke this tool in a complex environment.
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 100%, with clear parameter docs in the schema. The description adds no additional parameter semantics beyond implying search functionality through 'asteroid and comet data', but doesn't elaborate on usage examples or constraints. Baseline 3 is appropriate as the schema handles documentation adequately.
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 accesses 'asteroid and comet data' from the 'Small-Body Database (SBDB)', specifying both the resource (SBDB) and data type (asteroid/comet). However, it doesn't differentiate from siblings like 'jpl_sentry' or 'nasa_neo' which also handle asteroid data, missing explicit distinction.
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. With siblings like 'jpl_sentry' (impact risk) and 'nasa_neo' (near-Earth objects), the description lacks context for selection, offering only a basic function statement 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.
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 of behavioral disclosure. It mentions retrieving data but does not specify whether this is a read-only operation, if it requires authentication, rate limits, or what the response format looks like (especially given no output schema). This leaves significant gaps for a tool with 6 parameters.
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—a single phrase that front-loads the core purpose. There is no wasted language, and it efficiently communicates the tool's scope without unnecessary elaboration.
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 (6 parameters, no annotations, no output schema), the description is insufficient. It does not explain return values, error conditions, or behavioral traits. For a data retrieval tool with multiple options like file types and plots, more context is needed to guide effective use.
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 description adds no parameter-specific information beyond what the input schema provides. Since schema description coverage is 100%, the baseline score is 3. The description does not explain relationships between parameters (e.g., how 'file' interacts with 'plot' or 'summary'), nor does it provide examples or contextual usage for parameters like 'tdes'.
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's purpose: to retrieve NEOCP orbits, ephemerides, and impact risk data. It specifies the verb ('Scout') and resource types, but does not explicitly differentiate from sibling tools like jpl_sentry or nasa_neo, which might also handle asteroid/impact data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like jpl_sentry (impact risk monitoring) and nasa_neo (near-Earth object data), there is no indication of when jpl_scout is preferred, nor any prerequisites or exclusions mentioned.
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 full burden for behavioral disclosure. It states the tool provides 'data' but doesn't describe what format the data comes in (e.g., structured risk metrics, tables, or raw API responses), whether it's real-time or cached, any rate limits, authentication requirements, or error conditions. For a data retrieval tool with 10 parameters and no annotations, this leaves significant behavioral 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 a single, efficient sentence that directly states the tool's purpose. It's appropriately sized for a data retrieval tool and front-loaded with the key information. However, it could be slightly more structured by explicitly mentioning it's a query/retrieval tool rather than just 'data'.
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 (10 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the returned data looks like (critical since there's no output schema), how results are formatted, or any behavioral aspects like pagination or default behaviors when parameters are omitted. For a tool with rich filtering options, more context about typical use patterns would be helpful.
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 100%, so all 10 parameters are documented in the schema with clear descriptions (e.g., 'Maximum number of results to return', 'Start date (YYYY-MM-DD)'). The description adds no additional parameter semantics beyond what's in the schema. According to scoring rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.
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 provides 'NEO Earth impact risk assessment data' from JPL Sentry, which is a specific purpose with a defined resource (NEO risk data). It distinguishes from siblings like 'jpl_cad' (close approach data) or 'nasa_neo' (general NEO data) by focusing specifically on impact risk assessment. However, it doesn't explicitly mention the verb 'retrieve' or 'query', leaving the action slightly implicit.
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. With siblings like 'jpl_sbdb' (small body database) and 'nasa_neo' (general NEO data), there's no indication of when Sentry's specialized risk assessment data is preferred over broader NEO tools. No context about use cases, prerequisites, or exclusions is mentioned.
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 of behavioral disclosure. It only states what the tool does ('fetch') without detailing aspects like rate limits, authentication needs, response format, or potential errors. This is inadequate for a tool with multiple parameters and no output 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 a single, efficient sentence that front-loads the core purpose without any wasted words. It's appropriately sized for a simple fetch operation, making it easy for an agent to parse quickly.
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 tool has 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain how parameters interact (e.g., using 'date' vs. 'start_date'/'end_date'), what the return values look like, or any behavioral constraints, leaving significant gaps for agent understanding.
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 description adds no parameter-specific information beyond the input schema, which has 100% coverage with clear descriptions for all 5 parameters. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'fetch' and the resource 'NASA's Astronomy Picture of the Day', making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'nasa_images' or 'nasa_epic', which might also fetch NASA images, so it lacks explicit sibling distinction.
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, such as other NASA image tools in the sibling list. It doesn't mention any prerequisites, exclusions, or specific contexts for usage, leaving the agent to infer based on the tool name alone.
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 of behavioral disclosure. It only states what the tool accesses (satellite imagery) without detailing operational traits such as rate limits, authentication needs, output format (e.g., image URL vs. raw data), or error handling. This leaves significant gaps for a tool with 4 parameters and no output 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 extremely concise with a single phrase ('Global Imagery Browse Services - satellite imagery'), front-loaded and free of unnecessary words. Every part earns its place by identifying the service and resource, making it efficient and well-structured.
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 tool's complexity (4 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavior, output (e.g., image URL or data), usage context, and differentiation from siblings. This leaves the agent with insufficient information to effectively invoke the tool beyond basic parameter input.
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 100%, so the schema fully documents all parameters (layer, date, format, resolution). The description adds no additional meaning beyond what's in the schema, such as examples for layer names or resolution ranges. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
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 'Global Imagery Browse Services - satellite imagery' clearly states the tool's purpose: accessing satellite imagery through NASA's GIBS system. It specifies the resource (satellite imagery) and service (GIBS), but lacks a specific verb like 'retrieve' or 'fetch' and doesn't differentiate from sibling tools like nasa_images or nasa_epic, which also provide imagery.
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 doesn't mention context, prerequisites, or exclusions, and with sibling tools like nasa_images and nasa_epic offering similar imagery services, the agent has no basis for choosing between them.
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 of behavioral disclosure. It mentions searching an archive but doesn't describe key behaviors such as pagination handling (implied by the 'page' parameter), rate limits, authentication needs, or what the search results look like (e.g., format, size limits). For a search tool with 5 parameters and no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('search NASA's media archive') with no wasted words. It's appropriately sized for a search tool and gets straight to the point.
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 tool's complexity (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like result format, pagination, or error handling, and while the schema covers parameters, the description lacks context on how to effectively use the tool or interpret results, making it inadequate for full agent understanding.
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 100%, so the schema already documents all 5 parameters (q, media_type, year_start, year_end, page) with basic descriptions. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain search syntax, media_type options, or pagination details), resulting in a baseline score of 3.
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's purpose as searching NASA's media archive, which is a specific verb ('search') and resource ('NASA's media archive'). It distinguishes itself from most siblings (e.g., nasa_apod for astronomy pictures, nasa_mars_rover for Mars data) by focusing on general media search, though it doesn't explicitly differentiate from all siblings like nasa_cmr or nasa_gibs which might also involve media/data search.
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 doesn't mention any prerequisites, exclusions, or compare it to sibling tools (e.g., nasa_apod for daily astronomy images or nasa_mars_rover for Mars-specific media), leaving the agent to infer usage based on the tool name and description alone.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what the tool does (retrieve photos) without mentioning any behavioral traits such as rate limits, authentication needs, pagination behavior (implied by the 'page' parameter but not explained), or what happens on errors. For a tool with 5 parameters and no output schema, this is a significant gap in transparency.
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—a single, clear sentence that directly states the tool's function without any unnecessary words. It's front-loaded with the core purpose, making it efficient and easy to parse, which is ideal for quick understanding.
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 tool's complexity (5 parameters, no output schema, and no annotations), the description is incomplete. It lacks details on behavioral aspects like how results are returned, pagination handling, or error conditions. Without annotations or an output schema, the description should provide more context to help the agent use the tool effectively, but it falls short.
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 100% description coverage, clearly documenting all 5 parameters (rover, sol, earth_date, camera, page) with their types and purposes. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline score of 3, as the schema does the heavy lifting.
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's purpose: retrieving photos from NASA Mars rovers. It specifies the resource (images) and the source (Mars rovers), making it easy to understand what the tool does. However, it doesn't differentiate from sibling tools like 'nasa_images' or 'nasa_apod', which might also involve image retrieval, so it doesn't reach the highest clarity level.
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 doesn't mention any specific contexts, prerequisites, or exclusions, nor does it compare to sibling tools like 'nasa_images' that might also handle image-related queries. This leaves the agent with minimal usage direction.
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 of behavioral disclosure. It mentions it's a 'Web Service' but doesn't describe what kind of information is returned (e.g., orbital data, close approaches, physical characteristics), whether there are rate limits, authentication requirements, or error conditions. For a tool with 3 parameters and no output schema, this leaves significant behavioral 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 a single, efficient sentence that states the service name and purpose without unnecessary words. However, it could be more front-loaded by specifying the exact type of information provided (e.g., 'retrieves orbital and close-approach data for asteroids') to immediately clarify scope.
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 of asteroid data retrieval, no annotations, no output schema, and multiple sibling tools, the description is incomplete. It doesn't explain what information is returned, how results are structured, or how this tool differs from similar JPL tools. For a 3-parameter tool with no structured output documentation, more context is needed for effective agent use.
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 100%, with all parameters clearly documented in the schema itself. The description adds no additional parameter semantics beyond the generic 'asteroid search' context. This meets the baseline of 3 when the schema does the heavy lifting, but the description doesn't enhance understanding of how parameters interact (e.g., whether asteroid_id overrides date ranges).
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's purpose as providing 'information about asteroids' through a 'Near Earth Object Web Service', which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'jpl_sbdb' (Small-Body Database) or 'jpl_sentry' (impact risk assessment) that might also provide asteroid information, keeping it from a perfect score.
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. With multiple NASA and JPL sibling tools available (like jpl_sbdb for detailed asteroid data or jpl_sentry for impact risks), there's no indication of this tool's specific use case, context, or exclusions. The agent must infer usage from the tool name and parameters alone.
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 mentions 'Get data files' but doesn't disclose behavioral traits like authentication needs, rate limits, response format, or whether it's a read-only operation. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the key information ('NASA OSDR - Get data files for an OSD study'). There's no wasted text, making it highly concise and well-structured for quick understanding.
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 tool has no annotations, no output schema, and multiple sibling tools, the description is incomplete. It doesn't explain what 'data files' entail, how results are returned, or differentiate this from other NASA tools. For a retrieval tool in a crowded namespace, more context is needed to guide effective use.
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 100%, with the single parameter 'accession_number' fully documented in the schema. The description adds no additional meaning beyond implying it's for an OSD study, which is already covered by the tool name and description context. Baseline 3 is appropriate as the schema does the heavy lifting.
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 action ('Get data files') and resource ('for an OSD study'), specifying it's for NASA OSDR. However, it doesn't distinguish this tool from its many sibling tools (like nasa_apod, nasa_images, etc.), which all appear to be NASA data retrieval tools, so it misses sibling 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?
The description provides no guidance on when to use this tool versus alternatives. With multiple NASA-related sibling tools (e.g., nasa_apod, nasa_images, nasa_neo), there's no indication of context, prerequisites, or exclusions for selecting this specific tool over others.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'search' which implies a read-only operation, but doesn't specify whether this is a safe query, what authentication might be required, rate limits, or what happens with pagination. For a search tool with 6 parameters and no annotation coverage, this leaves significant behavioral 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 extremely concise - a single sentence that efficiently communicates the core purpose. It's front-loaded with the key information and contains no wasted words. Every element of the description earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description is minimally adequate. It identifies what the tool does but lacks behavioral context, usage guidance, and output information. The 100% schema coverage helps, but for a search tool that likely returns structured data, more context about result format would be beneficial.
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 100%, so the schema already documents all 6 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
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's purpose as 'search for NASA data collections' with a specific verb ('search') and resource ('NASA data collections'). It distinguishes itself from sibling tools by specifying NASA's Common Metadata Repository, but doesn't explicitly differentiate from similar search tools like nasa_images or nasa_firms beyond the repository name.
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 doesn't mention when to choose nasa_cmr over other NASA data tools like nasa_images or nasa_firms, nor does it specify any prerequisites or context for usage. The agent must infer usage from the tool name and description alone.
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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