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menantonio83-hue

RiskDataApi — Insider Cluster Detection for Solana Tokens

Server Quality Checklist

100%
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  • Latest release: v0.1.13

  • Disambiguation5/5

    The three tools are clearly distinct: check_token_risk covers a single token snapshot, check_token_risk_batch explicitly handles multiple mints, and get_token_risk_history addresses historical data. There is no meaningful overlap or ambiguity between current risk checking, batch risk checking, and historical trends.

    Naming Consistency5/5

    All tool names follow a consistent snake_case verb_noun pattern: check_token_risk, check_token_risk_batch, and get_token_risk_history. The shared token_risk core with clear qualifiers makes the toolset predictable and easy to navigate.

    Tool Count5/5

    Three tools is appropriately scoped for a focused insider-cluster risk API: single-token checks, batch checks, and historical data. Each tool serves a distinct and necessary purpose without redundancy or bloat.

    Completeness5/5

    The toolset covers the core domain completely: immediate single-token risk assessment, scaled batch assessment, and historical trend analysis. Since the API is read-only risk intelligence, no create/update/delete operations are expected, and no significant capability gap is apparent.

  • Average 4.4/5 across 3 of 3 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
    • 653 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • Tools from this server were used 10 times in the last 30 days.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

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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?

    With no annotations, the description carries the full burden and discloses the free-tier limit and auth path ('5 free MCP calls/day, no signup. Then email key or x402'). It also implies a read-only data check via 'Returns', though it doesn't explicitly state the absence of side effects.

    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 core behavior is front-loaded in one dense sentence, followed by a one-line usage instruction and a rate-limit note. The final fragment 'Then email key or x402' is cryptic but concise overall.

    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?

    In the absence of an output schema, the description enumerates all major output categories and the usage timing, plus rate-limit/auth context. The ambiguity of 'email key or x402' and the lack of explicit read-only confirmation are 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% and already documents the mint parameter as 'The Solana token mint address to check'. The description adds only the 'single Solana token mint' cardinality, which is useful but not a substantive semantic addition.

    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?

    States a concrete verb ('Returns') and enumerates specific data categories (0-100 safety score, insider clusters, authority status, holder concentration, price/liquidity/volume) for a single Solana token mint. The word 'single' differentiates it from the sibling check_token_risk_batch.

    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?

    Explicitly instructs 'Use before recommending or executing a trade on any Solana token', providing a clear trigger context. It does not mention sibling alternatives or exclusion conditions, but the single-token scope gives adequate guidance.

    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?

    With no annotations, the description carries full responsibility for behavioral disclosure. It reveals critical behaviors: up to 25 mints per call, per-mint billing equivalent to check_token_risk, no bulk discount, and an all-or-nothing rejection policy when quota/credit cannot cover the full batch. This is rich, decision-relevant context.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is two sentences with zero filler. The first sentence establishes the tool's purpose and relationship to check_token_risk; the second explains the all-or-nothing batch policy. Every phrase earns its place and the structure 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?

    For a simple one-parameter tool with no output schema, the description covers capacity limits, billing, and atomicity. It relies on the reference to check_token_risk for baseline return behavior, which is a reasonable shortcut given sibling context, but it does not explicitly describe the batch return format or ordering. This is a minor gap rather than a major omission.

    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 fully describes the single 'mints' parameter with minItems, maxItems, and item description ('1-25 Solana token mint addresses'). The description adds batching and pricing context but does not enhance the parameter's intrinsic meaning beyond what the schema provides, so 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.

    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 risk for multiple Solana tokens at once, with a specific verb ('check') and resource ('risk for multiple Solana tokens'). It distinguishes itself from the sibling check_token_risk by explicitly noting the batch capability of up to 25 mints.

    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 says 'Same as check_token_risk but for up to 25 mints in one call', which clearly indicates when to use this tool over the single-mint alternative. It also adds usage-relevant details about billing and quota, though it does not explicitly mention get_token_risk_history or list strict 'when not to use' conditions.

    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?

    With no annotations provided, the description carries the full burden and does so well: it discloses that the call is free, unmetered, has no quota, works with or without an API key, performs no live upstream call, and is a pure read from stored history. This materially tells an agent what side effects and constraints to expect without needing 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, with the core behavior and return contents front-loaded, followed by essential operational context (free, unmetered, no auth needed, no live upstream call). Every clause adds distinct information and none is redundant with the schema.

    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?

    This is a simple two-parameter read-only tool, and the description fully covers what an agent needs to call it correctly: the parameters are schema-documented, the return values are enumerated, and the operational traits (cost, auth, quota, live-call behavior) are disclosed. The lack of an output schema is mitigated by the explicit field list.

    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%, so the baseline is 3, but the description adds value beyond the schema by specifying the hourly granularity, the exact returned data fields, and the 90-day cap context. It does not repeat schema descriptions, making it a genuine semantic supplement rather than a restatement.

    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 names a specific action ('Returns hourly historical data points'), a concrete resource (a mint), and a precise time window (last N days, max 90). The enumerated fields (safety_score, insider_cluster_count, holder_count, price, liquidity, volume) make the tool's output unmistakable, and the phrase 'historical risk trend' clearly distinguishes it from current-risk checkers.

    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 clearly positions this as the tool for historical trends rather than current checks, and the sibling names check_token_risk and check_token_risk_batch reinforce that contrast. It does not explicitly say 'use this instead of X', but the historical-vs-current distinction is sufficiently clear from the description and title.

    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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