pancakeswap-poolspy-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 tool's purpose is clearly defined and distinct.
Naming Consistency5/5A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name follows a clear verb_noun pattern.
Tool Count2/5One tool is too few for a server focused on monitoring PancakeSwap pools, as it lacks essential operations like retrieving pool details, tracking updates, or analyzing liquidity. This severely limits functionality.
Completeness2/5The tool surface is severely incomplete for monitoring pools; it only fetches new pools without supporting queries for existing pools, pool metadata, or historical data, leaving significant gaps in coverage.
Average 4/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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 provided, the description carries the full burden of behavioral disclosure. It describes what the tool does (returns a list of pools) and the time-based filtering, but doesn't mention important behavioral aspects like rate limits, authentication requirements, error conditions, or response format. The description is accurate but incomplete for behavioral transparency.
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 perfectly structured and concise. The first sentence states the purpose clearly, followed by a well-organized parameter section with clear explanations and default values. Every sentence earns its place with no wasted words or redundant information.
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 the tool has 2 parameters, no annotations, and no output schema, the description provides good parameter documentation but lacks important contextual information. It doesn't describe the return format, what fields are included in pool objects, error handling, or any constraints beyond the parameters. For a data retrieval tool with no structured output definition, more completeness would be helpful.
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?
The description provides excellent parameter semantics beyond the input schema. While schema description coverage is 0%, the description explains both parameters clearly: 'time_range_seconds' specifies 'the time range in seconds to look back for new pools' with a default value explanation, and 'limit' specifies 'the maximum number of pools to return' with its default. This fully compensates for the lack of schema descriptions.
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 clearly states the specific action ('Returns a list'), resource ('trading pools'), and scope ('created in the specified time range on Pancake Swap V3 BNB Smart Chain'). It distinguishes this tool by specifying the exact blockchain and protocol version, though no sibling tools are mentioned for comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving recently created pools within a time range, but provides no explicit guidance on when to use this tool versus alternatives, prerequisites, or exclusions. Since no sibling tools are listed, the lack of comparative guidance is less critical, but the description doesn't address potential use cases 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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- Evaluate tool definition quality.
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