FluentLab Funding Assistant
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap between tools, as there are no other tools to compare or misselect against. The tool's purpose is clearly defined and distinct by default.
Naming Consistency5/5Since there is only one tool, it inherently follows a consistent naming pattern with itself. The tool name uses kebab-case (get-funding-options), which is a clear and readable convention, and there are no other tools to create inconsistency.
Tool Count2/5A single tool is too few for a server named 'FluentLab Funding Assistant', which implies a broader scope of funding-related operations. This minimal set suggests significant gaps in functionality, such as creating, updating, or managing funding applications, making it under-scoped for the apparent purpose.
Completeness1/5The tool set is severely incomplete for a funding assistant domain. It only provides retrieval of funding options, lacking essential operations like applying for funding, tracking applications, updating details, or managing user profiles, which are critical for a comprehensive funding workflow.
Average 3.8/5 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 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?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering key behavioral traits. The description adds context about the type of data returned ('program names, descriptions, eligibility criteria, and application details'), which is useful but doesn't provide rich behavioral details like rate limits or error handling.
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 concise and well-structured: three sentences that efficiently cover purpose, usage, and result details without redundancy. Each sentence adds clear value, making it front-loaded and easy to parse.
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?
Given the tool's moderate complexity (read-only search with two parameters), annotations cover safety and idempotency, and the description explains purpose, usage, and result content. However, without an output schema, the description could benefit from more detail on return format or pagination, though it's largely complete for its context.
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?
The input schema fully documents the two parameters (limit and page) with types and constraints, but schema description coverage is 0%. The description doesn't mention parameters at all, so it adds no semantic value beyond the schema. Baseline 3 is appropriate since the schema handles parameter documentation.
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: 'Retrieve available funding opportunities with detailed information including name and description.' It specifies the verb ('retrieve'), resource ('funding opportunities'), and scope ('detailed information'), though it doesn't differentiate from siblings as none exist.
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 provides explicit usage guidance: 'Use this tool when users need to explore available grants, funds, or financing opportunities.' This clearly indicates when to use the tool, but since there are no sibling tools, it doesn't specify alternatives or exclusions.
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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