MCP RiR
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion. The tool's purpose is clearly defined and distinct.
Naming Consistency5/5The single tool name 'generate_room_ir' follows a clear verb_noun pattern and is descriptive, with no inconsistent conventions.
Tool Count4/5The server has exactly one tool, which is on the low end but reasonable for the very specific purpose of generating room impulse responses. It is not excessive, and the narrow scope justifies the minimal count.
Completeness5/5The tool fully covers the stated capability of simulating a room and writing a stereo impulse response WAV. There are no obvious missing operations within the declared domain.
Average 4.2/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits 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.
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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?
Beyond annotations, the description discloses that 'Omitted arguments are randomised' and 'Repeated calls vary even with identical arguments,' which aligns with idempotentHint=false and adds real behavioral context. It also mentions writing a WAV file and returning a summary/path, though it does not address potential file overwrites or permission requirements.
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 four sentences, each earning its place: purpose, return value, how to translate user input, and randomization behavior. It is front-loaded with the core action and contains no redundant or vague wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With a full input schema, an output schema, and annotations present, the description covers the essential workflow: simulating a room, generating a WAV, returning a summary/path, and randomizing omitted args. It leaves no significant gaps for invocation, though a brief mention of expected output schema contents would make it fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions achieve 100% coverage, providing per-parameter ranges and defaults, so the baseline is 3. The description mainly points to those guidelines ('following the guidance on each argument') and reiterates the randomization option, adding no new semantic detail beyond what the schema already offers.
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 opening sentence states the tool 'Simulate a room and write a stereo impulse response WAV for convolution reverb,' a specific verb and resource, and also notes it 'Returns a summary and the file path.' This is unambiguous and distinguishes the tool's purpose even without siblings.
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?
It instructs to 'Translate the space the user described into dimensions and an absorption coefficient, following the guidance on each argument,' which clarifies how to set key parameters. It mentions the convolution reverb use case and randomization behavior, but does not explicitly state when not to use the tool; given no siblings, this is adequate.
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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- Evaluate tool definition quality.
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