nasa-exoplanet-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct operation: exact lookup (get_planet), filtered search (search_planets), name resolution (resolve_name), metadata discovery (list_tables, list_columns), and raw ADQL access (run_query). The descriptions clearly delineate when to use each, with explicit cross-references to prevent confusion.
Naming Consistency5/5
All tool names follow the verb_noun convention: get_planet, search_planets, resolve_name, list_tables, list_columns, run_query. The naming is consistent, lowercase with underscores, and each verb clearly indicates the action.
Tool Count5/5
With 6 tools, the server is well-scoped for its purpose of accessing NASA's exoplanet archive. Each tool serves a distinct and necessary role, covering basic and advanced querying, name resolution, and metadata exploration without unnecessary redundancy.
Completeness5/5
The tool set provides comprehensive coverage of the exoplanet archive domain: searching, retrieving detailed planet data, resolving aliases, exploring available tables and columns, and executing arbitrary ADQL queries for advanced needs. No obvious gaps exist—raw query access fills any niche not covered by the higher-level tools.
Average 4.6/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on May 17, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the operation as read-only, idempotent, and non-destructive. The description adds valuable behavioral context, noting that the main planet table has 300+ columns and suggesting the category filter to narrow results, which implies a potentially large output and gives the agent guidance on managing it. This goes beyond the bare 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 well-structured, leading with the core action, then usage guidance, then supplementary context about the large table and when the tool is unnecessary. It is slightly long but every sentence adds relevant information; no waste.
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 presence of an output schema and clear sibling differentiators, the description covers all necessary aspects: what the tool lists, when to use it, when not to, and how to handle its potentially large result set. It also properly references the default table and lists the category options implicitly via the schema.
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%; both parameters (table and category) already have detailed descriptions including examples and defaults. The tool description adds a rationale for using the category filter ('to narrow results to a manageable set') but does not meaningfully enhance parameter semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific action and object: 'List available columns for a table in the NASA Exoplanet Archive, with data types, descriptions, and units.' This clearly distinguishes the tool from siblings like search_planets (which searches data) and list_tables (which lists tables). The purpose is further reinforced by 'discover what data fields exist' and its relation to search_planets/run_query.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use the tool: 'Use this to discover what data fields exist before selecting specific columns in search_planets or writing ADQL queries with run_query.' It also states when it is not needed: 'You do not need this for basic queries — search_planets returns a useful default column set.' This directly addresses usage versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context beyond that: it states the data source ('Planetary Systems Composite Parameters table') and the best-available nature of values, plus the distinction that only confirmed exoplanets are included. It does not contradict annotations, and the extra context about data provenance 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured in three focused paragraphs: purpose and return categories, usage guidance and naming constraints, and data source. Each sentence earns its place, no fluff. It is front-loaded with the core purpose, and the additional details are directly useful.
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?
With a single parameter, full schema coverage, rich annotations, and an output schema, the description adds complementary context: usage after search, exact name handling, and the composite data table. It covers all necessary aspects for an agent to select and invoke this tool correctly, without needing to explain return values since the output schema exists.
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 100%: the input schema fully describes planet_name with exact spelling, case-sensitivity, and guidance to use resolve_name. The description repeats these details (e.g., 'must be exact,' 'Kepler-22 b, not Kepler-22b') without adding new meaning beyond what the schema already provides. 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and resource ('comprehensive details for a single confirmed exoplanet by exact name'), clearly distinguishing it from siblings like search_planets (which searches) and resolve_name (which canonicalizes names). It explicitly frames this tool as the follow-up to search_planets for full details on a single result, making its purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit when-to-use guidance: 'Use this after search_planets to get full details on a specific result, or when the user asks about a specific known planet.' It also provides a clear alternative: 'If unsure of the exact name, use resolve_name first.' This is excellent contextual guidance that names the alternative tool and the condition for choosing 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?
Annotations already establish readOnly/openWorld/idempotent safety, so the description adds value by disclosing return behavior: aliases grouped by catalog (Gaia, TIC, KOI, etc.) and confirmed planets in the system. This goes beyond the annotations without contradicting them, though it could have mentioned any pagination or error behavior if relevant.
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 efficiently written in three short paragraphs, each earning its place: the first states the purpose and return value, the second gives usage context with concrete examples, and the third covers an additional use case. No redundant or filler content appears.
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 (one parameter), the presence of an output schema, and comprehensive annotations, the description covers all essential operational context: what it resolves, what it returns, when to prefer it over siblings, and an extra scenario ('all designations'). It fully equips an agent to invoke it 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?
The schema already provides 100% coverage for the single 'name' parameter with its own examples and description. The tool description reinforces the same concepts but adds no new semantic detail beyond what the schema already conveys, so the baseline 3 applies.
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 tool's action ('Resolve') and resource ('any planet or star name, alias, or catalog identifier') and states the outcome ('canonical name'). It explicitly distinguishes itself from sibling tools by instructing to use it BEFORE get_planet or search_planets, making its unique role clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit when-to-use guidance ('when the user provides a name that might not match the archive's naming convention') and cites specific problem cases like missing spaces and alternate designations. It also names the alternative tools (get_planet, search_planets) that this should precede, fulfilling the when/alternatives requirement.
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?
Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds substantial behavioral context: it's a raw ADQL executor with SQL-like flexibility, lists supported functions, provides a cone-search example, and clarifies table semantics. It does not contradict annotations, though it stops short of detailing rate limits or error 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 appropriately detailed for a complex query tool. It is front-loaded with the core purpose, then systematically covers when to use, prerequisites, table inventory, supported syntax, and an example—every sentence adds value without 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?
For a tool with two parameters, an output schema, and rich annotations, the description is outstandingly complete. It covers the query language, common table names, example queries, and explicitly differentiates from sibling tools. The agent has everything needed to invoke it correctly and interpret results.
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 100%, so baseline is 3. The description goes beyond schema by explaining the ADQL dialect, giving a cone-search example, and describing common tables and their meanings—all of which help the agent construct a correct 'query' string. The 'limit' parameter is effectively explained via 'maxrec' and 'TOP N' mention.
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 opens with a specific verb and resource: 'Execute a raw ADQL query against the NASA Exoplanet Archive TAP service.' It clearly distinguishes the tool from search_planets by listing advanced use cases like GROUP BY, JOINs, and spatial/cone searches that the simpler tool cannot handle.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided on when to use this tool versus alternatives: 'Use this for complex queries that search_planets cannot handle' with concrete examples. It also advises a discovery workflow ('Use list_tables and list_columns first') and names common tables, giving clear 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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds useful context: the data source table, one row per confirmed planet with best-available measurements, and default behavior of returning most recently discovered planets when no filters are given. This goes beyond the structured annotations without contradicting them.
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 into four concise paragraphs: overview, primary usage, alternatives, and an edge-case hint. Every sentence carries meaningful information without padding, 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.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 21 optional parameters, an output schema, and multiple sibling tools, the description fully covers what the tool does, when to use it, how it behaves by default, and how to recover from empty name searches. The existence of an output schema means return-value details are not necessary here, so 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?
Schema description coverage is 100% and each parameter has a detailed description with examples, so the baseline is 3. The description adds extra semantic guidance by stating that all filter parameters are optional and combinable, and by noting that no filters returns recent discoveries. This enriches parameters as a group even though individual parameter details live in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Search for exoplanets in NASA's Exoplanet Archive with flexible filters') and identifies the exact data table returned. It clearly distinguishes itself from siblings by calling itself 'the primary tool for most exoplanet questions' and pointing to get_planet and run_query for alternative use cases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when/when-not guidance is provided: use for general exoplanet searches, use get_planet for single-planet exact lookups, use run_query for complex queries, and try resolve_name when name searches fail. This goes beyond vague usage hints and names specific alternatives.
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?
Beyond the annotations (readOnly, idempotent, not destructive), the description adds critical behavioral context: it returns a curated list of the most important tables, not all 40+ tables, and explains how to get the full list via run_query. It also clarifies the tool's relationship to other tools and the nature of the data returned, which is valuable beyond static hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening, usage guidance, and important caveats. Each sentence provides distinct value—no fluff or repetition. The length is justified given the need to explain the curated nature and the distinction from sibling tools.
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?
With an output schema present and no parameters, the description fully covers what an agent needs to know: what the tool returns, how to use it, its limitations, and how to access the full table list. It also provides specific examples of common tables, making it complete for practical use.
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 there is nothing for the description to explain. Per the baseline rule for 0-param tools, a score of 4 is appropriate. The description does not need to add parameter semantics, and it doesn't.
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 with a specific verb and resource: 'List all available tables in the NASA Exoplanet Archive with descriptions.' It also enumerates the return contents (names, descriptions, approximate row counts, guidance), making it immediately obvious what the tool does. It distinguishes itself from siblings by explicitly noting when search_planets or run_query are more appropriate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: 'Use this to discover what data is available before writing queries with run_query.' It also states when NOT to use it ('You do not need this tool for basic planet searches') and offers an alternative for discovering all tables via run_query with a specific query. This is textbook usage 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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