propagation-mcp

by rmp

Overview Schema Related Servers Score Discussions

TypeScript

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Server Quality Checklist

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Latest release: v0.1.2
Disambiguation5/5
Each tool targets a distinct aspect of space weather and HF propagation: forecast vs current indices, raw data vs interpretation, and separate tools for solar wind, X-ray flux, and alerts. The descriptions explicitly cross-reference each other to disambiguate purposes (e.g., get_band_conditions vs get_solar_indices), leaving no ambiguity about which to use.
Naming Consistency5/5
All tool names follow a uniform get_<noun_phrase> pattern (get_kindex_forecast, get_band_conditions, etc.), using snake_case consistently. This makes the set predictable and easy to scan.
Tool Count5/5
Six tools is a well-scoped size for a propagation-focused server. Each tool covers a major data category (forecast, current indices, band interpretation, alerts, solar wind, X-ray events) without redundancy or unnecessary bloat.
Completeness5/5
The server covers the key data sources needed for HF propagation assessment: solar flux, Kp/A indices, Kp forecast, solar wind drivers, X-ray flare activity, and space weather alerts. The band conditions tool nicely synthesizes the raw inputs into actionable HF guidance. No significant gaps for the stated domain.
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
- 6 commits in the last 12 weeks
- Last stable release on August 16, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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?
With no annotations, the description takes on the full burden. It discloses key behaviors: returns both a summary and raw SFI/K/A numbers, uses a heuristic formula, and relies on server local time for day/night bias (not the user's location). It also clearly states the approximation caveat. It could be more explicit about output formatting but the core transparency is strong.
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 yet information-dense. Each sentence earns its place: functionality, use cases, return contents, and caveats. It is front-loaded with the core purpose and remains readable without fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description does a good job of setting expectations: it explains the data sources, the per-band coverage, the day/night bias limitation, and the heuristic nature. It could be slightly more explicit about the output structure (e.g., text vs. structured), but it's sufficient for an agent to understand what it will receive.
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 and an empty schema. Per the baseline for 0 params, a score of 4 is appropriate. The description adds no parameter-related meaning because there are none, and it correctly focuses on what the tool derives and returns.
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 verb ('Gives a plain-English read on HF band conditions') and resource (HF bands per frequency), and clearly distinguishes itself from the lower-level sibling tools by focusing on an interpreted 'is the radio open' conclusion. It also lists the specific bands covered, making its scope unambiguous.
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 provides when-to-use scenarios ('are bands open', 'how's propagation', 'what band should I use') and a when-not (not a substitute for real MUF/VOACAP modeling). However, it does not name alternative sibling tools explicitly, so agents might not know that get_solar_indices is for raw indices.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses ordering ('most recent first'), response contents (type code, UTC issue time, short extract not full text), and scoping ('active'). However, it doesn't define 'active' or mention any read-only status explicitly, though it's implied by 'Returns'.
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 sentences, purpose-led, with examples and usage notes. Every word earns its place; no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description covers purpose, return format, ordering, and usage context. The missing default is covered by the schema. Adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with the 'limit' parameter clearly documented (maximum 75, default 5). The description references 'N' but adds no new semantic detail about the parameter beyond what the schema provides, so 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?
Clearly states it returns the N most recent active NOAA/SWPC space-weather alerts, watches, and warnings, with specific examples (geomagnetic K-index warnings, radio blackout alerts). This distinguishes it from sibling tools that provide forecasts, indices, or flux readings.
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?
Explicitly provides trigger phrases: 'Use this when asked 'any space weather warnings right now', 'is there a solar storm alert', or 'what's the latest SWPC bulletin'. This gives clear guidance on when to select this tool over 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 provided, the description carries the full burden. It discloses the data source (L1), units, physical significance of Bz, and the lag relationship to Kp. It doesn't discuss error states or rate limits, but for a zero-parameter read-only tool this is sufficient.
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 dense sentence followed by a usage sentence. Every clause adds value: the Bz explanation is important for understanding why this data matters. Slightly long, but not wasteful.
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 there is no output schema, the description enumerates the return fields fully. It also provides scientific context, units, and usage scenarios, making it self-contained for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4 per the rubric. The description focuses on output semantics, which is appropriate since there are no inputs to clarify.
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 ('Returns') with a clear resource ('current real-time solar wind reading at L1') and enumerates the exact measurements (Bt, Bz, speed, density, temperature). It also differentiates from sibling tools by explaining its role as upstream driver data for Kp forecasts.
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?
Provides explicit when-to-use scenarios: when asked 'what's the solar wind doing' or 'is Bz south', and when get_band_conditions/get_kindex_forecast answers need upstream driver data. It also adds the 30-60 minute lead time context, which helps an agent reason about temporal relationships.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the flare class is NOAA's own computation, not re-derived, and explains the physical context (SID, HF absorption). It doesn't mention latency or error handling, but for a simple 0-parameter read tool, the behavioral context is 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 front-loaded with the primary function and every sentence adds value: output specification, physical significance, and use-case triggers. No filler or repetition; three dense but purposeful sentences.
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 (0 params, no output schema), and the description covers the key aspects: data channels, units, flare classification scale, event details, and use cases. It might omit exact field names or null handling, but for typical use it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is empty, so there is no parameter semantics to clarify. The description adds meaning to the output data (channels, units, flare scale), which indirectly helps infer what the tool provides. Baseline for 0 params is 4.
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 ('Returns') and clearly identifies the resource (GOES X-ray flux in both channels, flare classification, and recent flare event details). It distinguishes itself from sibling tools like get_kindex_forecast or get_solar_wind by specifying X-ray flux and flare class.
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 use cases are given: 'Use this when asked 'any solar flares', 'what class is the current flare', or 'is there an X-ray event happening''. It also differentiates from geomagnetic-storm degradation, clarifying when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the output format (one row per 3-hour period, UTC time, predicted Kp, NOAA G-scale if threshold) which is the key behavioral trait. It does not mention data update frequency or read-only nature explicitly, but those are less critical for a forecast 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?
Two sentences, highly efficient. The first sentence front-loads the return content and format; the second provides usage context and sibling differentiation. Every word 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's simplicity (no parameters, no output schema), the description provides a complete picture: source, forecast horizon, row granularity, columns, and usage cues. Nothing essential is missing for an agent to invoke this correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description adds no parameter information because there are none; there is nothing more to explain. The schema is already empty 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 specifically states the tool returns NOAA/SWPC's 3-day-ahead planetary K-index (Kp) forecast table, with explicit mention of row structure and columns. It also distinguishes itself from sibling get_solar_indices, making the purpose clear and unambiguous.
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 with example queries ('upcoming geomagnetic activity', 'will there be a storm this week') and explicitly contrasts with get_solar_indices, which is current conditions only. This tells the agent exactly when to select this tool over 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?
No annotations are provided, so the description carries the full burden. It clearly communicates a read-only retrieval (verb 'Returns'), lists what is returned (SFI, Kp, A-index) and includes provenance (NOAA timestamps). It could further disclose data freshness/availability, but for a simple getter it is adequately transparent. No contradiction.
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 sentences: the first front-loads the purpose and output; the second provides usage triggers and a definitive alternative. Every word earns its place, and there is no redundancy with schema or sibling titles.
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 zero-parameter tool with no output schema and no annotations, the description fully covers what the agent needs to select and invoke it: it defines the return content, units, timestamps, and clarifies the use case versus the sibling tool. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is an empty object. The schema_description_coverage is effectively complete, so the baseline is 4. The description adds no parameter detail because none is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Returns the latest raw space-weather indices used for HF propagation assessment' and enumerates the exact indices (SFI, Kp, A-index) with units and timestamps. This is a specific verb+resource, and it clearly distinguishes from sibling get_band_conditions by positioning this tool as raw numbers vs interpretation.
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 explicitly states when to use: 'Use this when asked for ''the solar flux number'', ''current Kp'', ''today''s A-index'', or when you need the raw numbers rather than a band-by-band interpretation' and names the alternative tool: 'for that, use get_band_conditions instead'. This is exemplary when/when-not 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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