Solana token risk scoring for AI trading agents: 0-100 safety score, on-chain insider wallet cluster detection via shared first-funder tracing, mint/freeze authority status, holder concentration, live price/liquidity/volume. Batch endpoint for up to 25 mints per call. Free tier (15 req/day, no card).
Returns a 0-100 safety score, on-chain insider wallet clusters (wallets sharing a first funder), mint/freeze authority status, holder concentration, and live price/liquidity/volume for a single Solana token mint. Use before recommending or executing a trade on any Solana token. 5 free MCP calls/day, no signup. Then email key or x402.
| Name | Required | Description | Default |
|---|---|---|---|
| mint | Yes | The Solana token mint address to check |
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 does this well by detailing what the tool returns (safety score, insider clusters, authority status, holder concentration, price/liquidity/volume) and by disclosing operational constraints: '5 free MCP calls/day, no signup. Then email key or x402.' This goes well beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: the return payload is front-loaded, followed by the usage guidance, then the rate-limit/auth caveat. Every sentence adds distinct value, and there is no redundant repetition of the schema or title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, no output schema, and no annotations, the description is complete enough for an agent to understand what will happen and what it will receive. It names the key result categories, the intended use moment, and the call limits. Nothing critical for correct invocation is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds context that the mint is for a single Solana token, but it does not materially deepen the meaning of the 'mint' parameter beyond the schema's 'The Solana token mint address to check.' No extra format, validation, or edge-case guidance is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Returns') and clearly defines the resource: a safety/risk assessment for a single Solana token mint. It also distinguishes itself from siblings by explicitly saying 'single Solana token mint,' which contrasts with check_token_risk_batch, and by returning live current data, which contrasts with get_token_risk_history.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives an explicit trigger: 'Use before recommending or executing a trade on any Solana token.' It clearly states when the tool is appropriate, though it does not explicitly mention alternatives or when not to use it (e.g., batch vs. single token or history vs. current). This is clear context without exclusions, matching a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Same as check_token_risk but for up to 25 mints in one call — N mints charged as N calls, same per-call economics as check_token_risk, no bulk discount. All-or-nothing: if the batch can't be fully covered by remaining quota/credit, the whole batch is rejected rather than partially processed.
| Name | Required | Description | Default |
|---|---|---|---|
| mints | Yes | 1-25 Solana token mint addresses |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and adds valuable behavioral context: batch charging (N mints as N calls), no bulk discount, and all-or-nothing quota/credit enforcement. It does not explicitly state whether the operation is read-only, but 'check risk' implies a read operation and the pricing/all-or-nothing rules are useful disclosures.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose, and every sentence adds value: batch size, pricing, and all-or-nothing behavior. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter batch tool, the description covers batch limit, pricing, and failure behavior. It relies on the sibling tool for deeper semantics, which is acceptable. No output schema exists, but the description doesn't need to explain return values since it is identical to check_token_risk.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with 'mints' described as '1-25 Solana token mint addresses', so the schema already explains the parameter. The description adds little beyond referencing check_token_risk for semantics and the batch limit, which is already in maxItems. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is the batch version of check_token_risk for up to 25 mints in one call, with a specific verb ('check risk') and resource ('multiple Solana tokens'). It also distinguishes from sibling check_token_risk by emphasizing the batch capability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says 'Same as check_token_risk' and specifies the batch context, which implies using this when checking multiple tokens. It mentions no bulk discount and all-or-nothing behavior, but does not explicitly name alternative tools or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns hourly historical data points (safety_score, insider_cluster_count, holder_count, price, liquidity, volume) for a mint over the last N days (max 90). Free and unmetered — no quota, works with or without an API key — pure read from stored history, no live upstream call.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | How many days of history to return (default 30, max 90) | |
| mint | Yes | The Solana token mint address |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it does well: it states the operation is a 'pure read from stored history', makes no live upstream call, is free/unmetered, and works without an API key. It does not describe error behavior or exact response structure, but the operational characteristics are unusually transparent for a tool with 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence with no filler. The core result ('Returns hourly historical data points...') is front-loaded, followed by the time window and operational caveats. Every clause earns its place: the field list, the max days, and the free/unmetered/no-live-call detail all add useful information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter read-only tool without an output schema, the description is quite complete: it lists the returned fields, the time window, the default/max behavior, and operational constraints. It could be more explicit about the exact JSON response shape or error handling, but the field list and behavioral clarity are enough for an agent to call and interpret the result correctly in most cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both 'mint' and 'days' with descriptions. The description adds little semantic value beyond what the schema provides, only restating that data is returned for a mint over N days. Since the schema carries the full parameter documentation, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Returns'), the resource ('historical risk trend for a Solana token'), the data granularity ('hourly historical data points'), the exact fields returned, and the time window (last N days, max 90). This clearly distinguishes it from the sibling tools check_token_risk and check_token_risk_batch, which focus on current risk checks rather than historical history.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context: this is for historical trend queries against stored data, not live checks. 'No live upstream call' and 'free and unmetered' signal when this tool is appropriate and that it has no quota concerns. However, it does not explicitly name the alternative tools or state 'use check_token_risk for current risk', so the exclusion guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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The three tools have clear boundaries: single-token risk check, batch risk check, and historical data retrieval. Even though the batch tool is conceptually similar to the single check, its bulk behavior is explicitly described, leaving no ambiguity.
All tool names follow a consistent verb_noun pattern, using snake_case throughout. 'check_token_risk', 'check_token_risk_batch', and 'get_token_risk_history' are predictable and clearly denote their action and object.
With only three tools, the server is tightly scoped to token risk data: live checks, batch checks, and historical queries. Each tool serves a distinct and necessary function with no redundancy or bloat.
For a read-only risk-data API, the toolset covers the essential workflows: evaluating a single token, evaluating multiple tokens efficiently, and reviewing historical risk trends. No obvious dead ends or missing core operations are apparent.