SkyQuery

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: object resolution, ephemeris, small body, literature, conversions, etc. No two tools overlap in functionality, so an agent can easily select the right one for a task.
Naming Consistency5/5
All tool names use consistent snake_case and follow a descriptive verb_noun pattern (e.g., resolve_object, get_ephemeris). The naming is predictable and clear, making the tool surface easy to navigate.
Tool Count5/5
With 13 tools, the server is well-scoped for an astronomy research assistant. Each tool earns its place, covering essential functions without bloat or triviality.
Completeness5/5
The tool surface covers the core astronomy research workflow: object resolution, solar system data, catalog queries, literature search, unit and coordinate conversions, and citation management. No obvious gaps for the stated purpose.
Average 4.1/5 across 13 of 13 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 22 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It mentions combining SBDB, Horizons, and literature with full provenance, but does not disclose potential constraints like rate limits, latency, or error handling. The 'demo' label hints at possible limitations but is vague.
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 at two sentences, front-loading the key features. It is efficient, though the first sentence is slightly cryptic. Overall, it earns its length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description adequately covers the tool's functionality for a demo. It specifies the data sources and provenance, making it complete enough for its specialized scope.
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 single parameter 'with_paper' has 0% schema description coverage, and the description does not clarify its effect. While it might be inferred from the context of 'what is the latest paper', the description adds no explicit meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it is a composite tool for asteroid Apophis, providing its size, 2029 close approach, and latest paper in one call. It distinguishes itself from sibling tools by being a specialized one-stop query for Apophis, combining multiple sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a user wants quick, curated Apophis data, but does not explicitly state when to use this tool versus alternatives like get_small_body or object_dossier. No exclusions or caveats are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It indicates a fetch operation (read-only), and mentions rate limit implications, but does not detail side effects or response structure. The existence of an output schema mitigates the need for return format details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences: the first states the core purpose, and the second provides authentication tips. No extraneous information, and the key action is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one optional parameter, and an output schema exists. The description covers the main behavior and default behavior (today if no date). It could explicitly state that omitting 'date' returns today's picture, but this is implied. Overall, it is sufficiently complete for this low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning to the 'date' parameter. It implies the parameter accepts a date string or null for today, but does not specify the date format (e.g., YYYY-MM-DD). This provides partial clarity but lacks completeness.
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 fetches NASA's Astronomy Picture of the Day for a specific date or today. The verb 'Fetch' and resource 'APOD' are specific, and the tool is distinct from siblings like 'get_ephemeris' or 'resolve_object' 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions using a public DEMO_KEY and configuring a NASA key for higher rate limits, but provides no guidance on when to use this tool versus alternatives. No exclusions or context about other tools are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided. The description mentions it never uses hand-rolled trigonometry and returns the position with the frame explicitly labeled, but does not disclose behavioral traits like side effects (none expected), permissions, or rate limits. For a safe transformation tool, this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences. The first sentence states the purpose clearly, the second adds a credibility note. No wasted words, and the most important information is front-loaded.
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 simplicity and the existence of an output schema, the description is mostly complete. However, it lacks clarification on the input parameters (ra_deg, dec_deg) and their format, which is a gap since schema descriptions are absent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. However, it does not individually describe parameters like ra_deg, dec_deg, or explain their units. It only briefly mentions frames. The enum values are clear from schema, but the description adds minimal meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The tool clearly transforms coordinates between reference frames, naming specific frames (ICRS, FK5, FK4, Galactic). The verb 'transform' and resource 'coordinates between frames' are specific, and it distinguishes itself from sibling tools like convert_units or distance_from_parallax.
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 states it uses astropy's tested transforms, implying reliability, but gives no explicit guidance on when to use or avoid this tool, nor any alternatives. Usage context is implied by the tool's purpose but not clearly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses return fields and source, but does not mention any side effects, authentication needs, or rate limits. The behavior is straightforward, but transparency is minimal beyond listing outputs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load the purpose and source, then list returns and pairing advice. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and an output schema, the description is sufficient. It lists key return fields and suggests a companion tool, but could be slightly more explicit about input formats.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not explain the 'designation' parameter (e.g., format examples like number or name). The description compensates partially by stating it works for asteroids/comets, but leaves users to infer the input format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it looks up physical and orbital parameters of asteroids/comets from JPL SBDB, listing specific return fields. It distinguishes from siblings by mentioning pairing with get_ephemeris, but does not explicitly differentiate from other siblings like cone_search.
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 a concrete use case by suggesting pairing with get_ephemeris to answer both 'how big' and 'where', but lacks explicit when-not-to-use or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It explains the core matching behavior, limit of 50 targets, and output (matches with separation and unmatched targets). Missing details like rate limits or idempotency, but adequate.
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?
Three sentences, front-loaded with main purpose, efficient with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description covers the essential gaps: operation, inputs, output types, and constraint. Minor missing details like allowed catalog values, but overall complete enough.
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 has 0% description coverage, so description must compensate. It explains 'tolerance_arcsec' well, but 'catalog' parameter is not explained (only default 'gaia'), and 'targets' is moderately explained. Incomplete parameter documentation.
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 action (match target names to nearest catalog source), explains the process (resolve to position, find nearest within tolerance), and distinguishes from siblings like resolve_object (single object) and cone_search (spatial search).
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?
Implied usage for matching multiple target names to a catalog, but no explicit when/why not or alternative comparison. The 50-target limit is mentioned, but lacks guidance on when to use crossmatch vs resolve_object or cone_search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that it resolves an object and optionally fetches papers, but does not elaborate on what 'resolve' entails, potential side effects, or authorization needs. Adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no extraneous text. Front-loaded with the core purpose, followed by usage guidance. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (not shown) and is straightforward, the description is mostly complete. However, it could clarify what kind of objects are resolved (e.g., celestial), but the sibling tools provide context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 2 parameters with 0% description coverage. Description adds meaning by explaining that 'with_papers' controls whether to attach papers, but does not detail the 'name' parameter beyond its schema type. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description states 'Resolve an object and, optionally, attach recent papers about it.' This clearly indicates the combined functionality of object resolution and literature lookup, distinguishing it from separate sibling tools like 'resolve_object' and 'search_literature'.
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?
Explicitly states when to use: 'Use when a user asks "tell me about X" and wants both the numbers and the references.' Provides clear context, though it does not explicitly mention when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns the converted value with its new unit and rejects mismatches. It does not discuss potential failures beyond mismatches, but for a pure conversion tool this is adequate.
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 remarkably concise: two sentences plus a line of examples, all front-loaded with the core purpose. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and the presence of an output schema (not shown but noted), the description covers the essential behavior: conversion, return format, and rejection of mismatches. It could mention supported unit systems or unit syntax, but overall is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, meaning the description must compensate for parameter meaning. The description gives examples of unit strings (mas, pc, ly) but does not specify the exact format or range of allowed units. This leaves ambiguity for the agent. Baseline 3 is appropriate as the schema itself is minimal.
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 converts a value between physical units using astropy, and provides concrete examples (mas, pc to ly, km/s), making the tool's purpose specific and distinct from sibling tools like convert_frame.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the conversion action and explicitly notes that unit mismatches are rejected. While it does not compare with alternatives like convert_frame, the context of sibling tools makes the usage context clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It discloses authentication requirement (ADS token via skyquery login), fallback to arXiv, and row cap of 50. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences, front-loaded with action, no redundancy. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists, so return structure coverage is sufficient. Covers input parameter behavior mostly, but missing explanation of 'prefer' parameter. Otherwise complete for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage. Description explains 'rows' cap (max 50) and indirectly 'prefer' by mentioning fallback, but does not explain the 'prefer' parameter's purpose (e.g., force ADS vs arXiv). 'query' is self-explanatory. Partial added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches astronomy literature with specific sources (NASA ADS or arXiv) and lists the returned fields (title, authors, year, bibcode, URL). It distinguishes itself from siblings which are different domain tools.
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 context: use to search literature, with fallback behavior when ADS token missing. Does not explicitly state when not to use, but sibling tools are distinct so implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It accurately discloses the rejection of non-positive parallaxes, a key behavioral trait. It does not detail output format or error handling beyond rejection, but the output schema exists.
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, consisting of just two short sentences. Every word adds value, with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single parameter and no annotations, the description covers input constraints and rejection behavior. The output schema exists, so return format is less critical. It could mention the output is distance in parsecs, but that is implied.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description adds meaning to the only parameter 'parallax_mas' by specifying its unit (milliarcseconds) and the constraint that it must be positive, which is not 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?
Description clearly states the tool converts parallax in milliarcseconds to distance in parsecs, using specific verbs and units. It distinguishes itself from sibling tools by this specific conversion function.
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 a clear condition for use: parallax must be positive. It warns that non-positive parallaxes are rejected. However, it does not mention when not to use it or suggest 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?
With no annotations, the description carries full burden. It discloses return data: coordinates, object type, cross-identifiers, properties with units and provenance. However, it doesn't mention error behavior or rate limits, but for a query tool this is reasonable.
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 efficient: one sentence for purpose, then a sentence summarizing outputs, and a final sentence for usage guidance. No wasted words, and key points are front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter and an output schema, the description covers the essential aspects: purpose, input hints, output summary. It could mention handling of ambiguous names, but overall it's complete enough for a simple query tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description adds meaning by giving examples of valid inputs ('SIMBAD-style names like 'Vega', 'M31', or catalog ids'), which helps the agent understand what to provide beyond the schema's plain string type.
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 'Resolve an astronomical object name or identifier to normalized data' with specific verb and resource. It distinguishes from siblings by noting 'This is usually the first call: downstream tools take the returned RA/Dec', making it clear as a starting point.
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 when to use (first call) and examples of input names ('Vega', 'M31'). While it doesn't explicitly list when not to use, the context with sibling tools and the phrase 'usually the first call' gives good 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 exist, so the description carries full burden. It discloses caps on radius and row_limit with a rationale ('protect free public services') and mentions unit-tagged columns. It does not detail authentication or rate limits, but the read-only nature is implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with no wasted words. The first sentence delivers the purpose, followed by essential parameter details and constraints. Each sentence adds value.
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 has 4 parameters, no annotations, and an output schema, the description covers purpose, parameter usage, constraints, and output property (unit-tagged columns). It is sufficiently complete for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It explains the 'center' parameter format (name or RA DEC), 'catalog' options (Gaia or VizieR ID), and implies constraints for 'radius_deg' and 'row_limit'. This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Return catalog sources within a radius of a position,' specifying the verb, resource, and scope. It distinguishes this tool from siblings like crossmatch by defining a cone search operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains how to specify the center (object name or RA DEC), catalog (Gaia or VizieR ID), and includes radius/row limit caps. It provides clear context for when to use this tool, though explicit 'when not to use' compared to siblings is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description conveys key behavioral traits: the tool returns deduplicated acknowledgments and honors each service's acknowledgment policy. It implies a read-only operation with no side effects, which is adequate for a simple retrieval 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 two sentences long, with the first sentence immediately stating the core function and the second adding usage guidance. There is no extraneous information; every sentence earns its place.
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 has no parameters and an output schema is present, the description fully covers what the tool does and when to use it. It tells the agent exactly what to expect (deduplicated acknowledgments) and when to call (end of conversation), making it complete for its context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so schema description coverage is effectively 100%. The description does not need to explain parameters, and it adds value by explaining the return content and usage context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Return the deduplicated acknowledgments for every source used this session.' This is a specific verb+resource combination that clearly distinguishes it from sibling tools focused on astronomy data retrieval or conversion.
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 advises 'Call this at the end of a research conversation to get a ready-to-paste citations block.' This provides clear context for when to use the tool, though it does not explicitly mention when not to use it or suggest 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?
With no annotations provided, the description fully discloses behavior: it returns a time series, specifies the data source (JPL Horizons), explains the observer_location parameter with a concrete example ('500@399' for geocentric), and notes that windows exceeding ~2000 samples are rejected. No contradictions.
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?
Four sentences, front-loaded with core purpose, then details, then usage guidance. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (astronomy ephemeris), the presence of an output schema (so return values are covered), and the fact that the description covers purpose, usage, parameters, and limitations, it is fully complete for agent selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning beyond the schema: it explains observer_location as a Horizons code with an example, clarifies step with examples like '1h' or '30m', and gives example targets for the target parameter (e.g., '99942 Apophis'). This compensates for the incomplete schema descriptions (60% coverage).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it computes a solar-system body's apparent ephemeris from JPL Horizons, listing returned quantities (ICRS position, distance, range rate, V magnitude) and examples of targets. It also distinguishes from siblings by noting this capability is unique among astronomy MCP servers.
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 explicit guidance: 'prefer it for "where is <body> on <date>" questions.' It also mentions the sample limit (~2000 samples) which helps agents avoid rejected queries. However, it does not give explicit when-not-to-use scenarios.
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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