Sol MCP — Solana Token Risk & Signals

Server Quality Checklist

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Latest release: v1.3.0
Disambiguation5/5
Each tool has a clearly distinct purpose: batch_token_risk handles multiple tokens, get_full_analysis combines risk and momentum, get_graduation_signals provides vetted decisions, get_momentum_signal focuses on momentum alone, get_token_risk assesses individual risk, and get_trading_performance evaluates trading stats. There is no overlap or ambiguity in their functions.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with 'get_' or 'batch_' prefixes, using snake_case throughout (e.g., get_token_risk, batch_token_risk). This uniformity makes the set predictable and easy to understand.
Tool Count5/5
With 6 tools, the count is well-scoped for the server's purpose of Solana token risk and signals analysis. Each tool serves a specific role in risk assessment, momentum analysis, signal vetting, and performance tracking, with no redundancy or missing essential functions.
Completeness5/5
The tool set comprehensively covers the domain: it includes individual and batch risk analysis, momentum signals, combined verdicts, vetted graduation signals, and trading performance metrics. This provides full lifecycle coverage from token evaluation to trade outcomes, with no obvious gaps.
Average 4.1/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
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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, openWorldHint=true, and idempotentHint=true. The description adds useful context about the evaluation criteria (risk score + momentum) and signal thresholds, but does not disclose rate limits, authentication needs, or pagination behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. All sentences add value, though the second sentence could be slightly more concise by integrating the evaluation criteria more smoothly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, and 100% schema coverage, the description is mostly complete. It explains the purpose and usage context well, but lacks details on output format or error handling, which would be helpful since there is no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description implies filtering by BUY/SKIP decisions but does not add syntax or format details beyond what the schema provides, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get recent token graduation signal decisions'), resource ('from Sol's on-chain analysis engine'), and scope ('pump.fun tokens'). It distinguishes from siblings by focusing on graduation decisions with BUY/SKIP signals, unlike risk or momentum tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('to discover tokens Sol's AI has vetted as worth trading'), but does not explicitly state when not to use it or name specific alternatives among the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover key behavioral traits (read-only, open-world, idempotent, non-destructive). The description adds useful context about the 10-token limit and sorted return order, but does not disclose rate limits, authentication needs, or error handling beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences: the first states the purpose and constraints, the second explains the return format. Every word earns its place, and information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, and no output schema, the description is mostly complete but could better explain the risk score format or error cases. It adequately covers the core functionality and constraints for a batch read operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents the 'mints' parameter. The description adds no additional parameter semantics beyond implying batch processing, so it meets the baseline for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get risk scores') and resources ('multiple Solana tokens'), distinguishing it from siblings like 'get_token_risk' by emphasizing batch processing and the 10-item limit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (for multiple tokens up to 10), but does not explicitly state when not to use it or name alternatives like 'get_token_risk' for single tokens, which would be helpful for sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide safety and idempotency hints (readOnlyHint, destructiveHint, idempotentHint), so the bar is lower. The description adds valuable context by specifying the analysis method ('multi-window buy/sell ratio analysis') and the return format with confidence levels, which are not covered by annotations. No contradictions with annotations are present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose, method, and return values without unnecessary words. It is front-loaded with the core action and resource, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (financial analysis with a single parameter), annotations cover safety aspects, and the schema fully documents the input. However, there is no output schema, so the description must explain return values, which it does by listing the signal categories and confidence levels. It could be more complete by detailing the analysis windows or confidence thresholds, but it provides sufficient context for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'mint' fully documented in the schema. The description does not add any parameter-specific details beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate as the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get') and resources ('buy/sell momentum signal for a Solana token'), and distinguishes it from siblings by specifying the analysis method ('multi-window buy/sell ratio analysis'). It explicitly mentions the return values, which helps differentiate it from tools like 'get_token_risk' or 'get_full_analysis'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining momentum signals but does not explicitly state when to use this tool versus alternatives like 'get_graduation_signals' or 'get_full_analysis'. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior, so the description adds value by detailing the analysis criteria (liquidity, whale concentration, holder count, volume patterns). However, it lacks additional context such as rate limits, data freshness, or error handling, which would enhance transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core function, followed by risk categories and analysis factors, all in two efficient sentences with zero wasted words. It is appropriately sized for a single-parameter tool, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, one parameter, rich annotations, and lack of output schema, the description is mostly complete. It explains what the tool does, the output format (score and label), and analysis factors. However, it could improve by mentioning the return structure or any limitations, but it's adequate for the context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema fully documents the 'mint' parameter. The description adds no extra parameter details, but since there is only one parameter and the schema is comprehensive, a baseline of 3 is appropriate. The slight boost to 4 reflects the tool's simplicity and the description's implicit reinforcement of the parameter's purpose in the context of risk scoring.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get a risk score and risk label') and resource ('for a Solana token mint address'), distinguishing it from siblings like 'get_full_analysis' or 'get_trading_performance' by focusing solely on risk assessment. It specifies the output range (0-100) and risk categories, making the function explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for risk evaluation of Solana tokens but does not explicitly state when to use this tool versus alternatives like 'batch_token_risk' (for multiple tokens) or 'get_full_analysis' (which might include more metrics). No exclusions or prerequisites are provided, leaving the context somewhat vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable context beyond annotations by explaining the combined verdict interpretation ('low risk + strong buy = best setup for entry'), which helps the agent understand the output's meaning. Annotations cover safety (readOnlyHint, non-destructive) and idempotency, so the bar is lower, but this extra insight into result interpretation is beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core functionality in the first sentence, followed by a concise interpretation of results. Both sentences earn their place by providing essential information without redundancy, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (combining two metrics), rich annotations, and no output schema, the description is mostly complete. It explains what the tool returns and how to interpret it, but lacks details on output format or error handling, which could be helpful for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the 'mint' parameter. The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or constraints, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get both risk score AND momentum signal for a token in one call.' It specifies the verb ('Get'), resources ('risk score' and 'momentum signal'), and distinguishes it from siblings like 'get_token_risk' and 'get_momentum_signal' by combining both in a single operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: when needing both risk and momentum data together. It implies an alternative (using separate tools for each metric) but doesn't explicitly name them or state when not to use this tool, such as when only one metric is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true. The description adds valuable context about what the tool returns (performance stats, recent trades) and Sol's trading strategy (pump.fun graduating tokens, risk + momentum), which helps the agent understand the data's nature beyond the safety profile indicated by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by specific metrics, context about Sol's trading, and usage guidance. Every sentence adds value without redundancy, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only tool with one optional parameter and no output schema, the description provides sufficient context: it explains what data is returned, the trading strategy, and when to use it. However, it doesn't detail output format or potential limitations (e.g., data freshness), leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'recent_count' fully documented in the schema. The description mentions 'recent closed trades' but doesn't add semantic details beyond what the schema provides, such as how trades are selected or formatted. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get Sol's live trading performance stats and recent closed trades' with specific metrics (win rate, total PnL, ROI, recent trade outcomes). It distinguishes from sibling tools by mentioning 'get_graduation_signals for trade ideas' as a different use case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided: 'Useful for evaluating signal quality before using get_graduation_signals for trade ideas.' This clearly states when to use this tool (evaluation) versus when to use an alternative (trade ideas), with a named sibling tool mentioned.
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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