Keepygaga RAG
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool exposed, there is no possible overlap or misselection between tools. The single search operation is unambiguous.
Naming Consistency5/5The lone tool is named with a clear, lowercase verb ('search') that accurately describes its action. There are no other names to create mixed conventions or inconsistent patterns.
Tool Count3/5A single tool is borderline: acceptable for a retrieval-only endpoint, but thin for a server branded as a RAG system, which would typically benefit from source/table discovery or management tools.
Completeness3/5The search tool itself is feature-rich with hybrid retrieval, reranking, and filters, but the surface is incomplete for a RAG lifecycle: agents cannot ingest, update, delete, or even enumerate available sources/tables without external knowledge.
Average 4.6/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only and idempotent, so the description correctly builds on that. It adds meaningful behavioral details: results are grouped by text table, network calls are made to Embedding and Reranker providers, and reranking happens only with user consent. This is valuable context beyond the structured annotations.
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 well-structured and front-loaded with purpose, followed by scope/exclusions, privacy details, argument summaries, and a verification caveat. It is somewhat long and the Args block partially duplicates the schema, but every section earns its place for a search tool with privacy and authority caveats.
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?
The description is complete for a read-only search tool: it defines scope, results contents, privacy behavior, parameter constraints, and the need to verify matches against the source file. The presence of an output schema also covers return-value details, so no essential context is missing.
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%, so the schema already documents all parameters well. The description's Args section restates the schema (query, top_k range, table_ids, source_ids limits) rather than adding deeper semantic details, so a baseline of 3 is appropriate.
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 states a specific verb ('Search'), a clear resource ('ordinary local knowledge'), and the retrieval mechanism (hybrid FTS and vector recall, RRF, reranking). It also explicitly distinguishes what this tool is not for by saying it never searches core Agent memory or context-backup trees.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear scope: search ordinary local knowledge, not memory or backup trees. It also tells the agent that results are only candidate sources and that the returned source file should be read before treating a match as authoritative, which is a practical, actionable usage guideline.
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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