uniswap-poolspy-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'get_new_pools' has a single, clearly defined purpose: retrieving newly created Uniswap V3 pools within a specified time range.
Naming Consistency5/5Since there is only one tool, naming consistency is inherently perfect. The tool name 'get_new_pools' follows a clear verb_noun pattern (get + new_pools), which would be consistent if more tools were added.
Tool Count2/5A single tool is too few for a server named 'uniswap-poolspy-mcp', which suggests broader monitoring or analysis of Uniswap pools. This minimal toolset limits functionality to only retrieving new pools, lacking operations like querying existing pools, analyzing pool metrics, or tracking pool changes over time.
Completeness2/5The tool surface is severely incomplete for the implied domain of Uniswap pool monitoring. While 'get_new_pools' covers discovery of new pools, there are significant gaps: no tools for getting pool details, tracking liquidity or volume trends, analyzing historical data, or performing CRUD-like operations on pool information, which agents would need for comprehensive analysis.
Average 3.3/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read-only operation ('returns a list') but lacks details on permissions, rate limits, error handling, or response format. For a tool with 4 parameters and no annotation coverage, this leaves significant behavioral gaps, though it minimally indicates a safe read operation.
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 appropriately sized, with a clear purpose statement followed by a parameter breakdown. Each sentence adds value, and there is no redundant information. However, it could be slightly more front-loaded with key usage notes, preventing a perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no annotations, and no output schema, the description is moderately complete. It covers parameter semantics thoroughly but lacks behavioral context (e.g., response format, error cases) and usage guidelines. For a read-only tool with moderate complexity, this is adequate but has clear gaps, aligning with a minimum viable description.
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?
With 0% schema description coverage, the description fully compensates by detailing all 4 parameters: 'chain', 'order_by', 'time_range_seconds', and 'limit'. It provides clear semantics, default values, supported options for 'chain' and 'order_by', and explanations of each parameter's role, adding substantial value beyond the bare schema.
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: 'Returns a list of trading pools created in the specified time range on Uniswap V3.' It specifies the verb ('returns'), resource ('trading pools'), and scope ('created in the specified time range on Uniswap V3'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a score of 5.
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 exclusions. It only lists parameters without contextual usage advice. While no sibling tools are specified, the description still lacks general usage context, such as typical scenarios or limitations.
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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