SicariusGuard

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation5/5
Each tool targets a distinct aspect of Solana token safety analysis (e.g., batch scanning, specific checks for honeypot, LP lock, holder concentration, NFT, wallet reputation, deployer background). No two tools have overlapping purposes, and descriptions clearly delineate when to use each.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., check_honeypot, get_market_intel, recon_deployer). Verbs are appropriately descriptive and uniform, aiding predictability for an agent.
Tool Count5/5
With 11 tools, the server is well-scoped for its domain of Solana token security analysis. Each tool serves a necessary function without redundancy, covering individual checks, comprehensive scans, and specialized analyses.
Completeness4/5
The tool surface covers a wide range of safety checks (token, NFT, wallet, deployer) and includes batch processing and market intelligence. A minor gap might be the lack of a direct buy simulation, but the honeypot check covers sell simulation, and the overall set is highly comprehensive.
Average 4.6/5 across 11 of 11 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
- Last stable release on May 20, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Explicitly states it is a read-only operation with no on-chain side effects, and describes the internal process (discover pool, decode state, compute burn percentage, check holders). No annotations provided, so description carries full burden and does so well, though edge cases or limitations are not mentioned.
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 concise and front-loaded with the main purpose, though it includes some internal steps that could be trimmed. Still, it's not overly long and every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description lists the return fields (pool address, LP mint, burn percentage, lock type, risk score) and explains the internal logic, which is sufficient for a read-only tool with one parameter.
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?
The input schema already describes the sole parameter as 'Solana token mint address' with 100% coverage. The description does not add additional meaning to the parameter beyond the schema, so baseline 3 is appropriate.
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 it checks whether a Solana token's LP tokens are burned or locked, and distinguishes from siblings like full_token_scan for comprehensive analysis.
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?
Explicitly advises using this for LP-specific analysis and directs to full_token_scan for comprehensive analysis, providing clear when-to-use guidance.
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 states it is a read-only operation with no on-chain side effects and mentions a rate limit of 100 free calls/day per IP. It also outlines the 8 checks performed and the return format. However, it does not mention potential failure cases 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured, front-loading the purpose and usage, then detailing specific checks and alternatives. While slightly long, every sentence adds value and avoids redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 checks, multiple sibling tools, no output schema), the description covers all essential aspects: purpose, when to use, behavioral traits, parameter roles, return format (risk score and verdict), rate limits, and explicit alternatives.
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 coverage is 100%, so the schema already documents all parameters. The description adds slight context (e.g., txSignature for deeper analysis, isPumpSwap for Pump.fun tokens) but does not significantly enhance meaning beyond the schema.
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 analyzes Solana SPL tokens for rug pull, honeypot, and safety risks, and explicitly distinguishes it from sibling tools like check_honeypot and check_holder_concentration by positioning it as a comprehensive pre-trade check.
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?
Explicitly says 'Call this BEFORE executing any swap or buy transaction' and provides clear alternatives: 'Use this instead of check_honeypot or check_holder_concentration... Use full_token_scan instead when you also need Birdeye market data and wallet reputation.'
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 explains it's a read-only operation using DAS API and Enhanced Transactions with zero extra cost, and details the JSON dossier structure. No annotations are provided, so the description carries the full burden and does well, though it could mention any potential limitations.
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 somewhat lengthy but well-structured with front-loaded purpose and clear details. It could be slightly more concise without losing information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description thoroughly explains the return JSON dossier, covering all relevant fields. It also provides usage context and behavioral details, making it complete for an agent to invoke the tool correctly.
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?
The single parameter 'address' is described in both the input schema and the tool description, which adds context by specifying it's the Solana wallet address of the token deployer. The description adds value beyond the schema.
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 performs a deep background investigation on a Solana wallet to detect serial ruggers, using algorithmic analysis. It specifies the verb, resource, and method, distinguishing it from siblings like 'get_wallet_reputation' by focusing on rugger detection.
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 advises using the tool to evaluate a deployer before trusting their token, providing clear context. While it doesn't explicitly exclude cases or name alternatives, the use case is well-defined.
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?
No annotations are provided, so the description carries full burden. It correctly states the tool is read-only, mentions that newer tokens carry higher risk, and describes the output. However, it does not disclose error conditions, rate limits, or any potential side effects, which would be useful for full 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 extremely concise, consisting of two sentences. The first sentence clearly explains the purpose and output. The second provides usage guidance. No unnecessary words; each sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has a single parameter and no output schema, the description is highly complete. It describes the return values (timestamp, age, category, risk score) and provides interpretation context for risk. It also references the sibling tool for further analysis.
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?
The input schema has one parameter 'mint' with a description 'Solana token mint address'. The tool description does not add additional semantics beyond what the schema provides. Since schema description coverage is 100%, the baseline score is 3.
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 determines when a Solana token was created by finding its first transaction, and lists the returned fields (timestamp, age, category, risk score). It distinguishes from sibling tool full_token_scan, which is used for comprehensive analysis.
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?
The description explicitly says 'Use this for age-specific analysis; use full_token_scan for comprehensive analysis,' providing clear guidance on when to use this tool versus an alternative. It also notes it is a read-only RPC operation.
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?
With no annotations provided, the description carries the full burden. It declares the tool is read-only with no on-chain side effects and requires a Birdeye API key. This information is useful but could also mention rate limits or error handling. Still, it provides good 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 concise, with each sentence serving a distinct purpose: stating the main action, clarifying read-only nature, listing use cases, and providing exclusions. It is well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description enumerates the key fields in the returned JSON object. It covers use cases, prerequisites (API key), and alternative tools, making it contextually complete for a simple one-parameter tool.
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 coverage is 100%, so baseline is 3. The description repeats the schema's description of the 'mint' parameter ('Solana token mint address') but does not add further semantic detail beyond that.
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 retrieves real-time market intelligence for a Solana token from the Birdeye API, listing specific data returned (price, volume, market cap, etc.). It distinguishes itself from sibling tools like check_token_safety and full_token_scan by explicitly excluding safety checks.
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?
The description provides explicit guidance on when to use the tool (trade sizing, market health assessment, liquidity analysis) and when not to (safety/rug-pull checks), naming specific alternative tools (check_token_safety, full_token_scan).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description clearly states 'This is a read-only RPC call with no on-chain side effects', fully disclosing the behavioral trait.
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?
Five concise sentences front-loading purpose, return format, thresholds, read-only status, and usage guidance. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explains the return structure (boolean, reason, stats with thresholds) adequately. The single parameter and low complexity are well covered.
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?
Only one parameter 'mint' with 100% schema description coverage. The description mentions the return format but does not add extra meaning for the parameter beyond the schema.
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 specifies 'Analyze token holder distribution to detect supply concentration' with a clear verb and resource. It explicitly distinguishes from sibling tool check_token_safety, earning a top score.
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?
The description provides explicit when-to-use guidance: 'Use this when you specifically need holder distribution data' and names alternative check_token_safety for broader analysis.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully explains behavior: read-only operation, no on-chain side effects, zero cost, only requests a quote. It also notes returns JSON with isHoneypot and sellability details, covering all behavioral aspects.
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 compact but includes necessary details; the second sentence is a bit long but contains relevant information about sibling tools. No superfluous content, and the main purpose is front-loaded.
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 no output schema, the description explains the return type (JSON with isHoneypot and sellability details) but lacks specifics on sellability fields. For a simple check, it is adequate and covers cost, side effects, and usage 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?
Input schema has 100% coverage with clear descriptions for both parameters (mint and amount). The description adds context by explaining the simulation process, enhancing understanding of how parameters are used.
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 checks if a Solana token is a honeypot by simulating a sell order through Jupiter DEX, with specific verb and resource. It distinguishes from sibling tools like check_token_safety and full_token_scan, making the purpose unambiguous.
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?
Explicitly provides when to use this tool (verify sellability) and when to use alternatives (check_token_safety for broader analysis, full_token_scan for comprehensive scan). This gives clear guidance on tool selection.
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?
Declares read-only operation with no on-chain side effects, and describes output format (JSON with layered breakdown, finalScore 0-100, verdict). No annotations provided, so description carries the burden; lacks details on error states or data freshness, but otherwise transparent.
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?
Concise at about 3 sentences, front-loaded with the core purpose and key differentiators. Every sentence adds value: functionality, output, use case, rate limit. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, description fully covers return structure. With many sibling tools, it clearly explains when to use this vs alternatives, and includes rate limits. Adequate for a complex analysis tool.
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?
Schema coverage is 100% with all 3 parameters described. Description adds context: mint is the token address, txSignature for deeper analysis, isPumpSwap for graduated tokens. Baseline 3 due to high coverage, with added clarity earning a 4.
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?
Description clearly states it performs '10-layer safety analysis' and enumerates specific checks like mint auth, freeze, LP lock, token age, market intelligence, and wallet reputation. Explicitly distinguishes from sibling tool check_token_safety by noting the latter is faster but lacks market data.
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?
Explicitly advises use for 'high-value trades where you need maximum confidence' and directs to check_token_safety for faster checks. Also notes rate limit of 100 free calls/day per IP, helping the agent plan invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses it is a read-only operation with no on-chain side effects, queries Helius API, and requires an API key for full results. This is comprehensive given no annotations.
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 structured with key information upfront, but could be slightly more concise. Each sentence adds value, though the list of return fields might be streamlined.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no output schema), the description fully covers the tool's behavior, return fields, and limitations, making it complete for an AI agent.
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?
The single parameter 'address' has schema description (base58 Solana wallet address). The description adds context that it is the wallet to investigate, providing some extra meaning beyond the schema.
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 analyzes Solana wallet reputation using Helius DAS identity data and funding chain analysis, with specific outputs. It distinguishes itself from sibling tools by explicitly excluding token analysis.
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?
Provides explicit when-to-use (evaluate wallet trustworthiness before transacting) and when-not-to-use (do not use for token analysis, use check_token_safety or full_token_scan instead).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behaviors: read-only, no on-chain side effects, parallel execution, max 10 tokens, and lists return fields. With no annotations provided, the description fully compensates.
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?
Concise two-sentence structure with no wasted words. Front-loaded with core purpose, then details efficiently. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool, the description covers purpose, usage, behavior, return fields, and differentiates from 10 siblings. No output schema, but return fields are described. Complete.
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?
Schema coverage is 100% and describes the parameter. The description adds value by explaining the return structure and reinforcing the batch intent, going beyond the schema.
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?
Clearly states it scans multiple Solana tokens for portfolio-level risk assessment, runs full 10-layer analysis in parallel, and returns specific fields. Explicitly distinguishes from siblings by stating use case and naming alternatives for single token scans.
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?
Provides explicit when-to-use (portfolio, watchlist, multiple tokens) and when-not-to (single token) with named alternatives (full_token_scan, check_token_safety). This leaves no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
States 'Read-only — no on-chain side effects,' which is critical behavioral info. Also describes data sources and return fields, compensating for missing 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?
Two sentences: first function and output, second usage guidance. No unnecessary words, heavily front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one parameter, no output schema, but description enumerates return values (riskScore, verdict, etc.) and data sources, fully informing agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage with description for 'mint' parameter. Description adds 'NFT' and 'base58,' providing extra semantic clarity beyond schema.
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 it performs a safety analysis on a Solana NFT, specifying data sources (Helius DAS, Magic Eden) and outputs. It also distinguishes from sibling tool check_token_safety for fungible tokens.
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?
Explicitly says 'Use this for NFTs; use check_token_safety for fungible SPL tokens,' providing clear when-to-use and alternative.
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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