SR P3 MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: one fetches the current playlist in real-time, while the other searches historical playlists by date. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the need for current vs. historical data.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern with 'get' and 'search' as clear action verbs, followed by the resource 'p3_playlist' and a qualifier ('current' or 'by_date'). The naming is predictable and adheres to a uniform snake_case convention throughout.
Tool Count3/5With only two tools, the server feels thin for a radio playlist domain, as it lacks operations like searching by artist or title, fetching playlist details for specific songs, or handling user interactions. However, the tools cover basic current and historical data retrieval, which is minimally functional but could be expanded for better scope.
Completeness3/5The server provides core read operations for current and historical playlists, but there are notable gaps such as no ability to search within playlists (e.g., by artist or song), no update or delete functions (though these may not be needed for a read-only API), and no tools for managing user preferences or alerts. It covers basic retrieval but misses advanced querying features.
Average 4.3/5 across 2 of 2 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
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It states the tool returns an array of songs but does not describe what happens on no results, sorting, pagination, or rate limits. The date constraint is clearly stated, which adds some behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each adding distinct information: what it does, what it returns, and date constraints. No filler or redundancy. Front-loaded with the main action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks details about the returned array's structure (song fields). For a tool without output schema, the return format should be described. It also does not mention handling of invalid dates or empty results, leaving some ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds meaningful constraint information for the 'date' parameter (validity range) that is not in the schema. This adds value beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Search') and resource ('Sveriges Radio P3 playlist history'), specifying the key parameter (date/date range). It distinguishes from the sibling tool 'get_p3_current_playlist' by focusing on historical data filtered by date.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the valid date range ('within the last 90 days') and that future dates are not allowed. It implies usage context (historical search) but does not explicitly mention when not to use or directly compare to the sibling tool.
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 fully discloses return content (current, previous, next songs with details and UTC timestamps). It does not mention potential rate limits or authentication, but for a read-only fetch that is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that conveys all necessary information without redundancy. Every part adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description adequately explains what the tool returns (current, previous, next song with artist, title, album, timestamps). No missing context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so the description does not need to add parameter meaning. Schema coverage is 100%, and baseline is 4 per instructions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it fetches the currently playing song on Sveriges Radio P3, with specific channel number, and mentions details like artist, title, album, timestamps. This directly addresses the resource and action, and distinguishes from the sibling tool 'search_p3_playlist_by_date' which targets historical search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for current playlist retrieval, and the sibling tool name suggests alternative for date-based search. While it doesn't explicitly state when not to use, the context is clear enough.
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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