Trust checks for Solana trading: realized-PnL wallet trust, token safety, trade cost, forensics.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.3/5 across 4 of 4 tools scored.
Each tool targets a clearly distinct aspect of risk: token structure, trade costs, wallet funding origin, and wallet profit/loss trust. There is no overlap between the checks; they address different resources (token vs. wallet) and different risk dimensions.
All tool names follow the same verb_noun pattern: check_token_safety, check_trade_cost, check_wallet_forensics, check_wallet_trust. The pattern is consistent and predictable, making it easy to infer the function of each tool.
With exactly four tools, the server is well-scoped for its stated purpose of trust and risk checks. Each tool earns its place, covering the core risk categories without bloat or obvious redundancy.
The set provides comprehensive coverage for the domain: token structural risk, trade cost/liquidity, wallet origin/age, and wallet trust via profit/loss. The checks are complementary and leave no obvious dead ends, given the focus on structural and behavioral risk assessment.
check_token_safetyCheck Token Structural RiskAAnalyzes structural risk of a Solana token from on-chain data: real-holder concentration with bonding-curve and liquidity-pool accounts excluded, presence of known sniper-bot addresses among early buyers, and a simulated buy/sell round trip to detect honeypot behavior. Returns: verdict (LOW_RISK, ELEVATED, HIGH_RISK, CRITICAL, UNKNOWN), stable reason codes, and top-holder percentages. Scope is structural only; it does not detect risk that exists purely in trading behavior.
| Name | Required | Description | Default |
|---|---|---|---|
| mint | Yes | Solana token mint address (base58, 32-44 chars) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond readOnly/openWorld/idempotent annotations, description discloses methodology (excludes bonding-curve/LP accounts, simulates buy/sell) and output components (verdict, reason codes, percentages). It also notes limitation about trading-behavior risk.
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?
Three sentences, front-loaded with the main purpose, then outputs, then scope. No wasted words; each sentence adds essential information.
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-param tool with no output schema, description covers behavior, outputs, and limitations well. No significant gaps.
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 already provides 100% description coverage for mint (base58, 32-44 chars), and the description does not add parameter-specific detail. 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?
Description uses specific verb 'Analyzes structural risk of a Solana token from on-chain data', lists concrete components (holder concentration, sniper bots, honeypot simulation). Clearly distinguishes from sibling wallet/trade cost tools by focusing on token structural risk.
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?
States clear scope ('structural only') and what it does NOT detect ('trading behavior'), which helps choose when to use it. However, it does not explicitly mention alternatives like check_wallet_forensics or check_trade_cost, so it earns a 4 rather than 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_trade_costEstimate Trade Execution CostAEstimates what a trade of a given size would cost in a Solana token's main liquidity pool. Returns: expected slippage in percent, an estimated round-trip cost calibrated against measured live execution overhead, a recommended maximum size in SOL, pool liquidity, and a verdict (ACCEPTABLE_COST, ELEVATED_COST, HIGH_COST, UNTRADEABLE, NO_POOL, UNKNOWN).
| Name | Required | Description | Default |
|---|---|---|---|
| mint | Yes | Solana token mint address (base58) | |
| size_sol | No | Trade size in SOL (default 0.5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds value by detailing the exact return fields and the verdict enum, and mentions that the round-trip cost is 'calibrated against measured live execution overhead', giving insight into methodology without contradicting 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, information-dense sentence followed by a list of return values. It is front-loaded with the core purpose and avoids unnecessary words. The return list is slightly long but serves a clear function given the absence of an output schema.
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 absence of an output schema, the description compensates well by enumerating all return components and possible verdicts. It also provides some context on calibration. It does not mention potential errors outside the verdict list, but the verdicts cover no-pool and unknown cases, making it reasonably complete for a read-only estimator.
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 does not add much beyond the schema: it references 'a given size' for size_sol but does not describe format or default behavior, which the schema already covers. No additional parameter meaning 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 clearly states the tool estimates trade execution cost in a Solana token's main liquidity pool, listing specific outputs (slippage, round-trip cost, recommended max size, liquidity, verdict). This distinguishes it from sibling tools focused on token safety, wallet forensics, and wallet trust.
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 implies usage (when you need to estimate trade cost) but does not explicitly state when to use this tool over alternatives or any exclusions. Sibling names provide context, but there is no direct guidance on selection or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_wallet_forensicsTrace Wallet Funding OriginATraces where a Solana wallet's funding came from and how old it is, using on-chain transfer history. Returns: origin classification (CEX_FUNDED, WALLET_FUNDED, UNKNOWN_ORIGIN, UNVERIFIABLE_ORIGIN), the funding transaction, wallet age, and a fresh_wallet flag.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Solana wallet address (base58, 32-44 chars) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description provides useful behavioral details beyond the annotations: it explicitly lists the return fields and classification enum values (CEX_FUNDED, WALLET_FUNDED, etc.) and the use of on-chain transfer history. Annotations already declare this as a safe, read-only, idempotent operation, so the description adds valuable context about what the output looks like without being redundant.
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 exceptionally concise: two sentences, the first stating the tool's function and the second enumerating the return values. Every word contributes value, and the key information is front-loaded.
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?
With no output schema, the description fully compensates by listing all return fields and enum values. The tool is simple (one parameter, read-only) and the description covers purpose, method, and output sufficiently. No significant gaps remain.
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?
The single parameter (address) is fully described in the schema with format details (base58, 32-44 chars), so schema coverage is 100%. The description doesn't add additional parameter-level semantics, which is acceptable since the schema already carries the burden. 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 uses a specific verb ('Traces') and clearly identifies the resource (Solana wallet's funding origin and age). It distinguishes itself from sibling tools by focusing on funding forensics through on-chain history, which is unique among the listed wallet-related tools.
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 clearly implies when to use this tool (when you need to know where a wallet's funding came from and its age) but does not explicitly state exclusions or alternatives. The context is clear enough for an agent to infer the use case, though it could mention when not to use it (e.g., for token safety or trade cost analysis).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_wallet_trustCheck Wallet TrustARecomputes the realized SOL profit and loss of a Solana wallet from its on-chain transaction history and returns a trust verdict. Returns: verdict (TRUSTED, NEUTRAL, UNTRUSTED, INSUFFICIENT, UNVERIFIABLE), a confidence level, realized profit/loss in SOL, the number of coins analyzed, stable reason codes, and flags such as known_sniper_bot. Returns UNVERIFIABLE when the history cannot be reconstructed reliably rather than estimating.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Solana wallet address (base58, 32-44 chars) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it enumerates return fields, explains the UNVERIFIABLE verdict, and notes that it recomputes from on-chain history 'rather than estimating.' This complements the read-only/idempotent annotations without contradiction.
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 that front-load the primary action and then detail outputs and edge-case behavior. Every sentence earns its place with no redundancy or filler.
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?
With no output schema, the description compensates by explicitly listing return values (verdict, confidence, P/L, coin count, reason codes, flags) and the special UNVERIFIABLE behavior. It is complete for a tool with one parameter and clear annotations.
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?
The input schema already has a clear description for the single 'address' parameter with 100% coverage. The tool description does not add additional parameter-level meaning beyond what the schema provides, so the baseline of 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 clearly states the tool's function: 'Recomputes the realized SOL profit and loss... and returns a trust verdict.' It specifies the exact resource (Solana wallet) and distinguishes from sibling tools like check_token_safety or check_trade_cost by focusing on wallet trust assessment.
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 implies usage for evaluating wallet trust based on realized P/L, providing context about what the tool does. However, it does not explicitly mention when to use it over siblings or state exclusions, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!