Monad MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have completely distinct purposes: one queries token balances, while the other queries NFT counts. There is no overlap in functionality, and an agent can easily differentiate between them based on their clear, specific descriptions.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern with 'get' as the verb and hyphen-separated nouns ('mon-balance', 'nft-count'). The naming is uniform and predictable across the set.
Tool Count2/5With only two tools, the server feels thin for a blockchain domain like Monad, which typically involves more operations such as transaction queries, contract interactions, or token transfers. The scope is too limited for practical agent workflows.
Completeness2/5The toolset is severely incomplete for a blockchain server; it lacks essential operations like sending transactions, checking transaction status, querying contract data, or interacting with tokens beyond balances. This will cause significant agent failures in handling typical blockchain tasks.
Average 3/5 across 2 of 2 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
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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 full burden for behavioral disclosure. It only states what the tool does (query NFT count) without mentioning whether this is a read-only operation, if it requires authentication, rate limits, error conditions, or what format the response takes. For a tool with zero annotation coverage, this is insufficient behavioral context.
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 extremely concise - a single sentence that directly states the tool's function. There's no wasted language or unnecessary elaboration. It's appropriately sized for a simple query tool and gets straight to the point without any structural issues.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for proper tool usage. While it states the basic purpose, it doesn't cover important contextual information like response format, error handling, network specifics (Monad testnet limitations), or how this differs from the sibling balance tool. For a blockchain query tool, more context would be helpful.
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 input schema already fully documents both parameters (address and nftContract). The description doesn't add any additional parameter semantics beyond what's in the schema descriptions. It mentions both parameters implicitly through the function description but provides no extra details about format, validation, or usage patterns.
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 as '查询 Monad 测试网地址持有的 NFT 数量' (Query the NFT count held by a Monad testnet address). It specifies both the verb (query) and resource (NFT count), but doesn't differentiate from its sibling tool 'get-mon-balance' which presumably handles token balances rather than NFTs. The purpose is specific but lacks sibling distinction.
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. It doesn't mention the sibling tool 'get-mon-balance' or any other potential tools for querying NFT-related data. There's no indication of prerequisites, constraints, or typical use cases beyond the basic function stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- 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. While it states this is a query operation (implying read-only), it doesn't specify whether this requires authentication, has rate limits, returns real-time or cached data, or what happens with invalid addresses. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple query tool and front-loads the essential information. Every word earns its place.
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's simplicity (single parameter, no annotations, no output schema), the description is minimally adequate. It tells what the tool does but lacks important context about behavioral characteristics, usage scenarios, and output format. For a balance query tool, users would benefit from knowing what the return value looks like (numeric balance, units, etc.) and any constraints.
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 schema description coverage is 100%, with the single parameter 'address' fully documented in the schema as '需要查询的 Monad 测试网地址' (Monad testnet address to query). The description doesn't add any additional parameter information beyond what's already in the schema, such as address format examples or validation rules. With complete schema coverage, the baseline score of 3 is appropriate.
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: '查询 Monad 测试网地址的 MON 代币余额' (Query MON token balance for a Monad testnet address). It specifies the verb (query), resource (MON token balance), and target (Monad testnet address). However, it doesn't explicitly differentiate from its sibling tool 'get-nft-count', which appears to serve a different purpose (NFT count vs token balance).
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. It doesn't mention the sibling tool 'get-nft-count' or any other potential tools for querying different types of balances or addresses. There's no indication of prerequisites, limitations, or when this tool would be preferred over other methods.
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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