hybrid-rag-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap between tools. The single tool's purpose is clear and distinct by definition.
Naming Consistency5/5The single tool name 'search_docs' follows a consistent verb_noun convention. Since there is only one tool, no conflicting patterns exist.
Tool Count3/5One tool is on the thin edge of what is acceptable. For a narrow search-only server this could be sufficient, but for a 'hybrid-rag' service a single tool feels minimal.
Completeness2/5The server provides only search, with no document ingestion, update, deletion, or management capabilities. In a typical RAG workflow, agents would need upload or indexing tools to make the search useful, so the surface has significant gaps.
Average 4.3/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 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.
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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden. It does disclose the return format ({id, title, text, score}) and ordering ('most relevant first'), and mentions 'hybrid retrieval and reranking.' However, it does not explicitly state whether the operation is read-only, nor does it mention behavior for empty results, invalid queries, or rate limits. These omissions are non-trivial for a tool with no annotation safety hints.
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 compact and well-structured: a one-sentence purpose, then Args, then Returns. Every line earns its place, and the format matches the docstring convention, making it easy to parse. No redundant flourishes or unnecessary detail.
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?
For a tool with only two parameters and a clearly described output, the description is nearly complete. It covers purpose, parameters, return shape, and relevance ordering. The main gaps are edge-case behavior (no results, malformed queries) and lack of an explicit read-only statement, but these are minor relative to the simplicity of the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates. It explicitly defines 'query' as a natural-language search query and 'top_k' as how many passages to return with its default of 5. This gives the agent actionable semantics that the bare input schema lacks, making both parameters self-explanatory.
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 opens with a specific action: 'Search the document corpus with hybrid retrieval and reranking.' It names a clear verb, a clear resource, and even the retrieval technique, making the tool's function unambiguous. There are no sibling tools to distinguish from, but the purpose is stated with enough specificity that none is needed.
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 clearly identifies the tool's context: it is for searching the document corpus using a natural-language query. It implies when an agent would use it (whenever document search is needed) and does not require exclusions since no sibling tools exist. It stops short of explicit 'Use this when...' phrasing, but the context is clear and the parameters reinforce the intended usage.
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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