Solana transaction diagnosis, simulation, token-risk evidence, and priority fees via x402 USDC.
Normalize audio to a bounded 128 kbps MP3 and return base64 output plus loudness measurements. Costs $0.01.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| maxSeconds | No | ||
| targetLufs | No | ||
| audioBase64 | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds value by disclosing the monetary cost ($0.01) and the output format (base64 plus loudness), which are not covered by annotations. No contradictions detected.
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 extremely concise (two sentences, 20 words) and front-loaded with the action. Every word serves a purpose: action, output, and cost. No 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?
Given that the tool has 4 parameters and no output schema, the description misses critical information about input parameters, processing details, and potential constraints. While the cost and output format are mentioned, the lack of parameter explanations makes the description incomplete for effective use.
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 has 4 parameters with 0% description coverage, meaning the schema provides no explanations. The tool description fails to mention or explain any of these parameters (url, maxSeconds, targetLufs, audioBase64), leaving their semantics entirely unclear.
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 verb 'normalize', the resource 'audio', and specifies the output format (base64 MP3) along with additional return value (loudness measurements) and cost ($0.01). This specificity distinguishes it from sibling tools which are unrelated (PDF, Solana).
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 normalizing audio to MP3 with loudness measurement, and the cost provides a decision factor. However, it lacks explicit guidance on when to use this tool versus alternatives, 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.
Extract a PDF into page-delimited Markdown with metadata and a source digest. Costs $0.0025.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| title | No | ||
| maxPages | No | ||
| pdfBase64 | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, openWorld, and idempotent traits. The description adds context about cost ($0.0025) and output format (page-delimited Markdown with metadata and source digest), which are beyond the annotations. No contradictions.
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?
Single sentence with no wasted words. All key elements are front-loaded: action, output format, and cost.
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?
While the description captures the overall purpose, it lacks parameter documentation and does not explain how to provide the PDF (via URL or base64). With 4 parameters and no output schema, more detail is needed for correct invocation.
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 0%, so the description must compensate. However, the description does not explain any of the 4 parameters (url, title, maxPages, pdfBase64). The agent must infer from parameter names alone, which is insufficient.
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 extracts a PDF into page-delimited Markdown with metadata and a source digest, using a specific verb and resource. It distinguishes from sibling tools which are in different domains (audio, Solana).
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?
No explicit when-to-use or when-not-to-use instructions are provided. However, the tool's purpose is clear and sibling tools are unrelated, so usage is implied but not explicitly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get timestamped network-wide or account-localized priority-fee quantiles before choosing a compute-unit price. Suitable for recurring bot polling. Costs $0.002.
| Name | Required | Description | Default |
|---|---|---|---|
| accounts | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark as read-only, idempotent, and open-world. Description adds valuable context: it is timestamped, costs $0.002 per call, and suitable for polling. No contradictions.
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?
Two sentences, each earning its place. First sentence delivers core purpose and scope. Second adds cost and suitability. 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?
No output schema, but description mentions 'priority-fee quantiles' and 'timestamped', giving a reasonable sense of return type. Cost and usage guidance included. Could be slightly more explicit about output format.
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 has 0% description coverage. Description implies 'accounts' parameter is for account-localized fees but does not explain syntax, default behavior (omission yields network-wide), or constraints beyond maxItems. Partial compensation.
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?
Clearly states it retrieves 'priority-fee quantiles' with network-wide or account-localized scope. Verb 'Get' and resource are specific. Distinguishes from sibling Solana tools which focus on token/transaction analysis.
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?
Explicitly says 'Suitable for recurring bot polling' and 'before choosing a compute-unit price', providing clear context for when to use. No explicit when-not or alternatives, but sufficient for the use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze a Solana mint and holder risks. Costs $0.006.
| Name | Required | Description | Default |
|---|---|---|---|
| mint | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds the cost of $0.006, which is a key behavioral trait not covered by annotations. No contradictions.
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 extremely concise (one sentence) but lacks structure. While it fronts the key action and cost, it could be expanded with minimal additional context without losing conciseness.
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 a single parameter, no output schema, and annotations provide safety/hint info, the description is too sparse. It does not explain what the analysis returns or what 'holder risks' entails, leaving 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?
The only parameter 'mint' has no description in schema (0% coverage). The description does not explain what a mint address looks like, how to format it, or what data is required, leaving the agent guessing.
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 verb 'Analyze' and resource 'Solana mint and holder risks', distinguishing it from sibling tools focused on transactions, fees, or audio.
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?
No guidance on when to use this tool versus alternatives like solana_transaction_analysis or solana_priority_fees. Context signals mention sibling tools but description lacks usage criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Explain a confirmed Solana transaction and classify failures. Costs $0.004.
| Name | Required | Description | Default |
|---|---|---|---|
| signature | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds cost information ($0.004) beyond annotations (readOnlyHint, openWorldHint, idempotentHint). No contradiction, but lacks disclosure on rate limits or output details.
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?
Two sentences with clear purpose and cost, but missing parameter context; overall efficient.
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?
No output schema and description does not specify return format; for a tool with one parameter, behavior is under-specified.
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?
Single parameter 'signature' has no description in schema and the tool description does not explain what it is or how to obtain it; 0% schema coverage not compensated.
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?
Clearly states the tool explains a confirmed Solana transaction and classifies failures, differentiating it from sibling tools like solana_token_analysis.
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?
No guidance on when to use vs alternatives (e.g., solana_transaction_simulate), nor prerequisites like transaction confirmation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Simulate a Solana transaction and classify failures. Costs $0.008.
| Name | Required | Description | Default |
|---|---|---|---|
| encoding | No | base64 | |
| transaction | Yes | ||
| accountAddresses | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds the cost ($0.008) and the classification of failures, providing moderate additional behavioral context.
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 concise sentence with an additional cost note. Every word adds value with no 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?
Despite having no output schema, the description does not explain return values or provide enough context for the three parameters. The tool's complexity is not adequately covered for an agent to use it effectively.
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 documentation coverage is 0%, and the description provides no information about the parameters (encoding, transaction, accountAddresses). The description fails to compensate for the missing param details.
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 simulates a Solana transaction and classifies failures, using a specific verb and resource. However, it does not explicitly differentiate from sibling tools like solana_transaction_analysis.
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?
No guidance on when to use this tool vs alternatives. While the cost is mentioned, there is no explanation of when simulation is appropriate or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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
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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.
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Pay-per-call Solana token risk intelligence: 8 tools via x402. From $0.005 USDC, no API key.
Solana token due-diligence: 3-source fused risk verdict incl. LP-lock depth. $0.01 via x402.
Solana wallet forensics SDK, x402 pay-per-call, AI agents.
Solana address risk grades and token scans for AI agents. Pay-per-call via x402 (USDC on Base).
Add one secure layer between your agents and this server.
The Solana tools are distinct among themselves, but the inclusion of normalize_audio and pdf_to_markdown creates domain confusion. An agent cannot easily determine when to use these media tools in a server named after Solana transaction evidence.
Naming conventions are mixed: normalize_audio uses verb_noun, pdf_to_markdown uses a different pattern, and Solana tools use a prefix but mix noun and verb forms. No consistent verb_noun pattern across the set.
With 6 tools, the count is reasonable for a server, but the scope is mismatched—two tools are unrelated to Solana transactions, making the set feel poorly scoped for the server's stated purpose.
The Solana tools cover priority fees, token analysis, transaction analysis, and simulation, which are useful for transaction evidence, but missing operations like transaction broadcast or account details. The unrelated audio and PDF tools add noise rather than completeness.