space-finder-mcp

by loosephoto

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Latest release: v0.1.0
Disambiguation5/5
Each tool serves a clearly distinct purpose: historical reverse lookup, upcoming launches, daily APOD, NEO tracking, and media-specific searches for images, audio, and video. There is no functional overlap between tools.
Naming Consistency3/5
All names use snake_case, but the pattern is mixed: 'reverse_lookup' and 'search_space_*' follow a verb_noun structure, while 'upcoming_launches', 'apod', and 'neo_today' are noun phrases or acronyms. The inconsistency reduces predictability.
Tool Count5/5
Seven tools cover a broad but focused range of space-related queries without unnecessary duplication or excessive granularity. The number is well-suited for the server's purpose.
Completeness5/5
The toolset covers historical events, future launches, daily imagery, near-Earth objects, and media retrieval across three formats. This provides comprehensive coverage of the inferred domain without significant gaps.
Average 4.2/5 across 7 of 7 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present. The description only states the key requirement and the general return value; it does not disclose whether the operation is read-only, what errors may occur, or any rate-limit/network 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 a single concise sentence with no redundant or unnecessary content.
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?
For a simple APOD-fetching tool, the description is minimally adequate, but it lacks important context such as date format, expected response contents, and failure behavior, especially given the absence of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The date parameter is implied to be optional and to specify a particular day, but the accepted format (e.g., YYYY-MM-DD), valid date range, and behavior when omitted are not described.
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 that the tool returns NASA's Astronomy Picture of the Day for today or a specified date, using an unambiguous verb ('返す') and a specific resource. This distinguishes it from the sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It mentions the NASA_API_KEY requirement, which is useful, but it does not explain when to prefer this tool over siblings or provide guidance on date formatting or invocation context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the API key requirement but does not explicitly state whether the operation is read-only or if it has any side effects. This lack of explicit behavioral transparency is a gap.
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, succinct sentence that conveys the essential function and the key requirement. It avoids any unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there are no parameters and an output schema is present, the description is complete for basic usage. It includes the important authentication note. No additional context seems necessary for an agent to decide to call it.
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?
There are no parameters in the schema. The description does not need to explain any parameters, and the baseline score of 4 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 states that the tool returns Near-Earth Objects approaching Earth today. It explicitly mentions the NASA API key requirement, which distinguishes its purpose from the sibling tools that handle launches, media, and space imagery.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool over alternatives, but the function is straightforward: to get today's NEO data. Given the distinct nature of sibling tools like 'upcoming_launches' and 'apod', the intended use case is clear without further guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool queries Wikidata and auto-resolves country names to Q-ids, but it does not mention side effects, network calls, or potential errors. It also leaves ambiguity around the `launch_date` parameter behavior (whether it filters or sorts), which could affect the agent's expectations.
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 compact and well-organized: a one-line summary, two illustrative examples, then a clearly formatted parameter list. Each sentence serves a purpose, and the structure front-loads the core functionality before diving into details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Since an output schema exists, the description need not explain return values. However, it lacks explicit handling of empty results or error conditions, which could be important for an agent's decision-making. Still, the core context—what the tool does and how to configure it—is sufficiently covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Every parameter in the schema is explained in the Args section. The description adds critical details beyond the schema, such as `country_qid` taking precedence over `country` and `launch_date` controlling ordering vs enumeration. This fully clarifies the meaning and usage of each argument.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: '逆引き歴史Q&A — カテゴリ+国で絞り込んだ最初/記録の宇宙オブジェクトを Wikidata から探す。' This identifies a specific action (search), resource (Wikidata), and subject (space objects filtered by category and country). It is distinct from sibling tools like upcoming_launches or apod which focus on current or media content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides concrete examples ('米国で最初に打ち上げた宇宙望遠鏡は?') that imply when to use it, but it does not explicitly state when to use this tool over alternatives or when not to use it. There is no mention of exclusion criteria or comparisons to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits beyond the basic return of a list. It does not mention that it is read-only, any potential side effects, or error conditions, leaving a 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 succinct and directly to the point, with no unnecessary details. It is well-structured and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list-returning tool, the description covers all necessary context. An output schema is present, so return value details are not required, and the description adequately conveys the tool's purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the 'limit' parameter as the number of items to return with a default of 5, adding semantic meaning to the schema. This goes beyond the bare type and default value, although it is the only parameter and fully covered.
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 that the tool returns a list of upcoming rocket launches, which is a specific verb and resource. It is easily distinguished from sibling tools like reverse_lookup or apod, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about the tool's function, but it does not explicitly mention when to use it over alternatives. However, given the distinct nature of sibling tools, the appropriate usage is strongly implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It implies a read-only search operation but does not explicitly state side effects, permissions, or rate limits. The return format (content and structuredContent) is mentioned, but behavioral details beyond that are absent.
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, using a header line, parameter explanation, and a bullet-like example list. It is concise, with no redundant fluff, and every sentence contributes to understanding the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with three parameters and no output schema, the description provides enough to call it correctly: parameters, expected return format, and examples. Minor gaps include lack of error handling or empty-result behavior, but these do not critically hinder usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no parameter descriptions, but the tool description fully covers all three parameters: query (with examples), limit (default and max), and kind (with all values and meanings). This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it searches space-related audio and returns playable URLs, distinguishing it from sibling image/video search tools. The verb '検索し' and resource '音声' are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear guidance on when to use each 'kind' value (sound_effect for short sounds, podcast for long talks) with concrete query examples. However, it does not explicitly compare to sibling tools like search_space_videos, so it falls short of full alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns structuredContent and content, implying a read-only search operation, and does not hint at side effects or mutations. It is transparent enough for a search tool.
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 very concise: two clear sentences about purpose and output, a list of examples, and a parameter list. No redundant or irrelevant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, output format, parameter semantics, and usage examples. This is sufficient for an agent to understand when and how to invoke the tool without ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description explains all three parameters: query as search term, limit as count with default (3) and maximum (10), and show_inline as a toggle for inline display. This fully compensates for the missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches and displays images from the NASA Image & Video Library, with a specific scope (planets, satellites, missions) and explicitly differentiates output roles (structuredContent for LLM, content for chat). Examples further clarify intended use.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides guidance on output format and parameter semantics, but does not explicitly contrast with sibling tools like search_space_videos or search_space_audio. The intended usage is implied via the name and description rather than directly stated.
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?
Explains the return behavior in detail: content includes a summary and inline poster, structuredContent includes JSON with title/play URL/resolution/subtitles, and large video data is never base64-embedded but played from URL. This is meaningful behavioral disclosure beyond basic search semantics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with a clear summary, return-format explanation, examples, and an Args list. Every sentence adds value and no redundant filler is present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description explains what appears in content and structuredContent, including key fields and the URL-based playback approach. Examples and parameter constraints together give an agent enough context to call and interpret the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters are described in the text even though the schema has no descriptions. Query semantics, English reliability, limit constraints (default 3, max 10), and show_poster behavior are all explicitly stated.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool searches space-related videos and returns playable MP4 URLs. It gives concrete usage examples (Mars, rocket launch, Hubble) and distinguishes itself from sibling tools like search_space_images and search_space_audio by focusing on video content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides practical guidance such as using English query terms for better results and notes the default/maximum limit. It does not explicitly contrast with sibling tools or state when not to use it, but the resource type and examples make the appropriate usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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