Fan Out Query MCP
Server Quality Checklist
Latest release: v0.2.10
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.
Naming Consistency5/5A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'analyze_content_gap' follows a clear verb_noun pattern.
Tool Count2/5A single tool is too few for a server named 'Fan Out Query MCP', which suggests a broader scope involving query decomposition and analysis. This minimal set feels thin and incomplete for the implied domain.
Completeness2/5The tool covers content gap analysis, but the server name implies capabilities like query decomposition, fan-out processing, or other related operations. There are significant gaps, such as tools for decomposing queries, retrieving results, or synthesizing outputs, which limit agent workflows.
Average 2.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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 Apache 2.0.
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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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'advanced content gap analysis' and techniques used, but doesn't describe what the analysis returns, potential side effects, performance characteristics, or error conditions. For a complex tool with 7 parameters and no output schema, this is a significant 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that directly address the tool's function. It's front-loaded with the primary purpose and avoids unnecessary elaboration. Every sentence contributes meaning, though it could be slightly more structured for a complex tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, nested objects, no output schema, no annotations), the description is insufficient. It doesn't explain what the analysis returns, how results are structured, or what 'gaps' means operationally. For a sophisticated analysis tool, users need more context about outputs and behavioral expectations.
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 description coverage is 100%, providing comprehensive parameter documentation. The description adds minimal value beyond the schema, mentioning only 'URL' and 'analysis depth' implicitly through 'Analyzes a URL' and 'advanced content gap analysis.' It doesn't explain parameter relationships or usage patterns, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Perform advanced content gap analysis using Query Decomposition and Self-RAG techniques' with specific verbs ('analyze', 'identify') and resources ('URL', 'user queries', 'content gaps'). It distinguishes what the tool does (analyze content coverage and gaps) effectively. However, without sibling tools, differentiation from alternatives isn't demonstrated, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or typical scenarios. It mentions what the tool does but offers no context about when it's appropriate or what problems it solves. Without sibling tools, this gap is still notable as it lacks any usage context.
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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