F
licenseA
qualityC
maintenancePay-per-call tools for AI agents including trust checks, due diligence, market data, and human-verified approvals, settled in USDC on Base via the x402 protocol.
16
-
agentic-commerce-catalog
Server Details
AI music, video, image, and voice tools callable by agents with USDC payments via x402 on Base.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
3 toolsagent_imageAInspect
AI image generation from a text prompt. Generate album and single cover art, podcast and video thumbnails, social and campaign graphics, concept art, storyboard frames, product shots, and article illustrations. Returns one hosted still image as PNG or JPEG in square, portrait, or widescreen framing (1:1, 4:5, 9:16, 16:9, 21:9). Async by default: poll the returned pollUrl for imageUrl, or pass ?async=false to block. — $0.15 USDC via x402 on Base (chain 8453).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Image description — subject, style, composition. | |
| asyncMode | No | Return immediately with a jobId while the render runs. Can only switch async on: false is the same as omitting it, so a paid call stays asynchronous; ?async=false is the only way to force a blocking call. | |
| aspectRatio | No | Aspect ratio, e.g. '1:1', '16:9', '9:16'. |
TDQS
A4.6/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden and does so well. It reveals async-by-default behavior, the polling mechanism (pollUrl → imageUrl), the blocking alternative (?async=false), output format (one hosted PNG/JPEG), supported aspect ratios, and pricing/chain details ($0.15 USDC on Base). This gives the agent a reliable mental model of how the call behaves.
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 three dense sentences with no filler. It front-loads the core purpose first, then use cases, output behavior, async semantics, and cost. Every sentence contributes operational or selection-relevant information.
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?
The tool has no output schema and no annotations, so the description must supply essential invocation and return details. It covers purpose, output format, aspect ratios, async polling, blocking option, and cost. The only notable gap is how the x402 payment flow is initiated or handled, but the description provides enough for an agent to make the call and interpret the response.
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. The description adds value beyond the schema by listing the full set of supported aspect ratios and reinforcing the async/blocking behavioral distinction. It does not deeply elaborate on prompt nuances, but the schema already describes prompt as 'subject, style, composition.'
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 opens with a specific verb and resource: 'AI image generation from a text prompt.' It then enumerates concrete use cases (album art, thumbnails, concept art, product shots, etc.) that make the tool's scope unmistakable. The contrast with siblings agent_video and create_music is implicit but clear: this tool produces still images, not video or music.
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 gives clear context for when to use the tool through its list of image-generation use cases. It does not explicitly exclude alternatives or name sibling tools for video/music, but the domain separation is evident enough that an agent can route correctly. No explicit 'when not to use' guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
agent_videoAInspect
AI video generation from a text prompt. Returns an 8-second 720p clip with native audio. Use for short-form video, b-roll, product shots, music-video visuals, social clips, and ads. Include sound cues (instruments, voices, weather, movement) since audio is generated from the scene; a silent scene renders near-silent by design. Async by default: poll the returned pollUrl for videoUrl, or pass ?async=false to block. — $4.99 USDC via x402 on Base (chain 8453).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | Optional random seed for reproducible renders. | |
| model | No | Optional model preset variant. Leave unset for the default. | |
| prompt | Yes | Narrative or visual prompt to drive the music video concept. | |
| asyncMode | No | Return immediately with a jobId while the render runs. Can only switch async on: false is the same as omitting it, so a paid call stays asynchronous; ?async=false is the only way to force a blocking call. | |
| resolution | No | Target rendering resolution. | |
| aspectRatio | No | Desired frame aspect ratio. | |
| durationSeconds | No | Clip length in seconds (default 8). |
TDQS
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden, and it does well by disclosing async-by-default behavior, pollUrl/videoUrl, the blocking override, native audio generation, and pricing. However, 'Returns an 8-second 720p clip' is stated as fixed even though the schema exposes durationSeconds and a 1024p resolution option, so it slightly overstates the default output.
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 compact, front-loaded with the core purpose, and every sentence adds value: output format, use cases, prompt strategy, async behavior, and cost. It avoids redundancy and is easy for an agent to scan.
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?
The description covers the essential runtime behavior—default output, async flow, polling, blocking mode, audio considerations, and cost—without an output schema to rely on. It does not detail authentication, error cases, or the full set of param interactions, but the schema already documents the parameters and the core workflow is sufficiently 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%, so the baseline is 3. The description adds meaningful prompt guidance—include sound cues since audio is generated from the scene—and clarifies the default duration and resolution. This goes beyond the schema's field descriptions, particularly for the prompt parameter.
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 opens with a specific verb and resource: 'AI video generation from a text prompt.' It further clarifies the output—an 8-second 720p clip with native audio—and lists concrete use cases such as b-roll, product shots, and social clips. This makes it clearly distinct from sibling tools agent_image and create_music.
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 provides clear usage context by listing short-form video, b-roll, product shots, music-video visuals, social clips, and ads. It does not explicitly name when not to use this tool or contrast it with agent_image and create_music, but the video-specific framing makes the intended scope clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_musicAInspect
AI music generation from a text prompt. Creates a complete original song with vocals, melody, and full arrangement as a finished MP3, not a loop or preview. Describe genre, mood, instrumentation, and tempo, supply your own lyrics, request an instrumental, or pin a style tag. Use for soundtracks under video, release-ready artist tracks, podcast and game themes, and demo sketches. Async: poll songId until model_version leaves 'pending'. — $0.50 USDC via x402 on Base (chain 8453).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Comma-separated keywords to tag the generated file. | |
| style | No | Specific musical style or genre tag (e.g., 'Hip Hop', 'Ambient', 'Rock'). Helping the AI focus on a specific sound. | |
| lyrics | No | Your custom lyrics. Required if custom_mode is true. | |
| prompt | Yes | Describe the song you want to create. Include genre, mood, instruments, and any specific vibe. Example: 'Upbeat 80s synthwave with driving bass and neon atmosphere' or 'A melancholic acoustic guitar ballad about rain'. | |
| custom_mode | No | Set to true to use your own lyrics provided in the 'lyrics' field. | |
| vocal_gender | No | Preferred gender for the vocalist ('m' for male, 'f' for female). | |
| durationSeconds | No | Duration of the track in seconds. Defaults to a full-length song. | |
| make_instrumental | No | If true, generates a track without vocals. |
TDQS
A4.5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full disclosure burden and does so well: it states the output format, async polling behavior (songId/model_version leaving 'pending'), and pricing/payment rails (USDC via x402 on Base). This goes well beyond the schema's field-level information.
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 front-loaded with the core purpose and every sentence carries weight—use cases, async behavior, and cost are all relevant. The first two sentences overlap slightly in saying this is text-prompt music generation, which keeps it just shy of a 5.
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 tool with no output schema, the description provides the necessary extra context: final MP3 output, async polling touchpoints, and payment details. It is terse about the actual poll mechanism and response shape, but the 100% input-schema coverage compensates for parameter-side gaps.
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 schema already documents all 8 parameters, so the baseline is 3; the description adds useful mapping like 'Describe genre, mood, instrumentation, and tempo' (prompt), 'supply your own lyrics' (custom_mode/lyrics), 'request an instrumental' (make_instrumental), and 'pin a style tag' (style). This enriches without duplicating schema detail.
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 identifies a specific verb and resource: AI music generation from a text prompt producing a complete original song as an MP3. It also differentiates from the sibling media tools by specifying vocals, melody, full arrangement, and 'not a loop or preview'.
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?
It gives concrete, relevant use cases (soundtracks, release-ready tracks, podcast/game themes, demo sketches) and clarifies the output type. It does not explicitly name sibling alternatives or state when not to use this tool, 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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- Changed
agent_image1 field changed- added
Input schema / properties / asyncModeAdded value: +{ + "description": "Return immediately with a jobId while the render runs. Can only switch async on: false is the same as omitting it, so a paid call stays asynchronous; ?async=false is the only way to force a blocking call.", + "type": "boolean" +}
- Changed
agent_video1 field changed- changed
Input schema / properties / asyncMode / descriptionPrevious value: -"If true, return immediately while the video renders asynchronously."New value: +"Return immediately with a jobId while the render runs. Can only switch async on: false is the same as omitting it, so a paid call stays asynchronous; ?async=false is the only way to force a blocking call."
1 tool update
- Changed
agent_image4 fields changed- removed
Input schema / additionalPropertiesRemoved value: -false - added
Input schema / properties / aspectRatioAdded value: +{ + "description": "Aspect ratio, e.g. '1:1', '16:9', '9:16'.", + "type": "string" +} - added
Input schema / properties / promptAdded value: +{ + "description": "Image description — subject, style, composition.", + "type": "string" +} - added
Input schema / requiredAdded value: +[ + "prompt" +]
1 tool update
- Changed
agent_image6 fields changed- added
Input schema / additionalPropertiesAdded value: +false - removed
Input schema / properties / aspectRatioRemoved value: -{ - "description": "Desired image aspect ratio.", - "enum": [ - "1:1", - "16:9", - "9:16" - ], - "type": "string" -} - removed
Input schema / properties / promptRemoved value: -{ - "description": "Text prompt for cover art, promo art, or a standalone campaign visual.", - "type": "string" -} - removed
Input schema / properties / seedRemoved value: -{ - "description": "Optional random seed for reproducible renders.", - "type": "integer" -} - removed
Input schema / properties / styleRemoved value: -{ - "description": "Optional visual style direction.", - "type": "string" -} - removed
Input schema / requiredRemoved value: -[ - "prompt" -]
1 tool update
- Changed
agent_image6 fields changed- removed
Input schema / additionalPropertiesRemoved value: -false - added
Input schema / properties / aspectRatioAdded value: +{ + "description": "Desired image aspect ratio.", + "enum": [ + "1:1", + "16:9", + "9:16" + ], + "type": "string" +} - added
Input schema / properties / promptAdded value: +{ + "description": "Text prompt for cover art, promo art, or a standalone campaign visual.", + "type": "string" +} - added
Input schema / properties / seedAdded value: +{ + "description": "Optional random seed for reproducible renders.", + "type": "integer" +} - added
Input schema / properties / styleAdded value: +{ + "description": "Optional visual style direction.", + "type": "string" +} - added
Input schema / requiredAdded value: +[ + "prompt" +]
1 tool update
- Added
agent_image
15 tool updates
- Removed
agent_music - Removed
audio_analyze - Removed
chain_chat - Removed
continue_track - Removed
cover - Removed
extend - Removed
lyric_sync - Removed
lyrics - Removed
midi - Removed
prompt_analyze - Removed
rig_analyze - Removed
rig_oracle - Removed
rig_roast - Removed
style_coach - Removed
vox
Frequently Asked Questions
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, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon 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
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.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Connectors
Pay-per-call tools for autonomous agents, settled in USDC on Base via x402.
16163 pay-per-call tools for agents: vision, text, data, web, blockchain. USDC on Base via x402.
x402 toolkit for AI agents: paid web, AI, and Base chain tools per call in USDC. Free tools too.
Pay-per-use AI and data tools via x402: image, video, music, voice, search, crypto. USDC.
Related MCP Servers
x402-video MCP serverofficial
AlicenseAqualityCmaintenanceEnables AI agents to generate videos by paying per call with USDC on Base via the x402 protocol, with no accounts or API keys required.666MIT- AlicenseAqualityCmaintenanceProvides AI agents with 10 pay-per-call utility tools (QR generation, DNS lookup, OCR, etc.) using USDC on Base via the x402 protocol, with agent's private key never leaving the agent.1167MIT
- AlicenseBqualityBmaintenanceConnects AI agents to the Base network for onchain data, batch USDC payments, and access to over 200 AI models. It utilizes the x402 protocol to enable pay-per-request functionality using USDC without requiring traditional API keys or accounts.100532MIT
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A4.4/5.0
Disambiguation5/5
Each tool targets a distinctly different media type: image, video, and music. There is no overlap or ambiguity between generating a still image, a video clip, or an original song.
Naming Consistency3/5
Two tools follow the agent_ prefix pattern (agent_image, agent_video) while the third uses create_music, mixing conventions. The names are still readable, but the pattern is not fully consistent.
Tool Count4/5
Three tools is a lean but reasonable set for a media generation server. Each tool covers a meaningful content type and earns its place, though the server could arguably include additional related creation tools.
Completeness4/5
The surface covers the core generation needs for image, video, and music content. Minor gaps exist, such as no editing or asset management tools, but the core lifecycle of generating and receiving media is complete.