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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
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.
100% free. Your data is private.
Tool Definition Quality
A3.9/5.0
Tool DescriptionsA
Average 4.2/5 across 3 of 3 tools scored.
Server CoherenceA
Disambiguation5/5
Each tool targets a distinct media type: image, video, and music. The purposes are clearly separated, so an agent can easily select the appropriate tool without confusion.
Naming Consistency2/5
The naming convention is inconsistent: two tools use the 'agent_' prefix (agent_image, agent_video) while one uses a verb_noun pattern (create_music). This mixed style makes the set feel less cohesive.
Tool Count3/5
With only 3 tools, the set is on the small side, but it covers the core media generation capabilities (image, video, music). The server name suggests a broader 'commerce catalog' scope, which is not reflected, making the count feel slightly mismatched.
Completeness4/5
Within the domain of media generation, the tools cover the major output types: still images, video clips, and music tracks. Minor gaps exist (e.g., no audio-only generation without music, no editing or variation tools), but the core workflows are handled.
Available Tools
3 toolsagent_imageAInspect
Generate a still image from a text prompt. PAYMENT-SIGNATURE-authenticated requests default to asynchronous execution. Asynchronous responses return a jobId and pollUrl. Poll GET /agent/image/{jobId} without another payment until status is 'completed' and imageUrl is available. — $0.15 USDC via x402 on Base (chain 8453).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Image description — subject, style, composition. | |
| aspectRatio | No | Aspect ratio, e.g. '1:1', '16:9', '9:16'. |
Tool Definition Quality
A3.9/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and discloses key behaviors: default asynchronous execution for payment-signature-authenticated requests, the jobId/pollUrl flow, polling without extra payment, and the $0.15 USDC cost on Base chain. It is transparent about execution and payment, though it omits error handling or failure scenarios.
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 compact, with the purpose front-loaded and essential details about async behavior and pricing packed into three sentences. Each sentence carries meaning, though the payment/async details could be slightly more structured, it remains efficient and readable.
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 2-parameter tool with no output schema, the description covers the essential behavioral aspects: async execution, polling, and result retrieval (jobId, pollUrl, status, imageUrl). It also includes cost and chain information, making it reasonably complete, though it lacks failure handling details.
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 coverage is 100% and both parameters already have clear descriptions (prompt details, aspect ratio examples). The description adds no additional parameter-level detail beyond mentioning 'text prompt,' so the baseline of 3 applies.
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?
Description begins with 'Generate a still image from a text prompt,' which clearly states the verb, resource, and scope. The term 'still image' distinguishes it from sibling tools like agent_video (video) and create_music (music).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for still image generation via a text prompt, but it does not explicitly mention when to use this tool versus alternatives or any exclusions. There is no direct reference to sibling tools or criteria for choosing this one.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
agent_videoAInspect
Text-to-video generation for agents. Returns an 8-second 720p clip with native audio. Use for short-form video, b-roll, product shots, music-video visuals, and social clips. Audio is generated from the scene, so include sound cues in the prompt (instruments, voices, weather, movement) for an audible result — a still, silent scene renders near-silent by design. PAYMENT-SIGNATURE-authenticated requests default to asynchronous execution. Asynchronous responses return a jobId and pollUrl. Poll GET /agent/video/{jobId} without another payment until status is 'completed' and videoUrl is available. — $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 | If true, return immediately while the video renders asynchronously. | |
| resolution | No | Target rendering resolution. | |
| aspectRatio | No | Desired frame aspect ratio. | |
| durationSeconds | No | Clip length in seconds (default 8). |
Tool Definition Quality
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 fully carries the transparency burden and exceeds expectations. It discloses async execution defaults, jobId/pollUrl response shape, polling endpoint without extra payment, resolution cap, native audio behavior ('near-silent' for still scenes), and even pricing/payment details. No contradiction exists.
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 dense but every sentence carries useful information. It front-loads the core purpose, then flows through use cases, audio guidance, async flow, and pricing. A bit verbose (e.g., multiple clauses on audio), but nothing is truly redundant.
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?
Given there is no output schema, the description thoroughly explains the async return contract (jobId, pollUrl, videoUrl) and polling semantics. It also covers edge cases (silent scene behavior), payment requirements, and rendering defaults, making it complete for an agent to invoke and manage the task.
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 descriptions cover 100% of parameters, so baseline is 3. The description adds valuable context beyond the schema: it links prompt content to audio generation, clarifies that asyncMode is the default for payment-signature requests, and implies default duration (8 seconds). This elevates it slightly.
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+resource: 'Text-to-video generation for agents,' clearly distinguishing it from sibling tools (agent_image, create_music). It also specifies the output format (8-second 720p clip with audio), leaving no ambiguity about purpose.
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 explicitly lists appropriate use cases: 'short-form video, b-roll, product shots, music-video visuals, and social clips.' It also provides guidance on crafting prompts for audio ('include sound cues') without explicitly naming exclusions or alternatives, but sibling tool names make the distinction clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_musicAInspect
Generate a song from a prompt with optional custom lyrics and style inputs. Returns JSON describing the generated track when the request succeeds. — $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. |
Tool Definition Quality
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It adds useful context about the cost ($0.50 USDC via x402 on Base) and the return format (JSON describing the generated track). However, it does not mention failure modes, processing time, or any side effects beyond the payment.
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, front-loaded with the core purpose in the first sentence and cost/return details in the second. Every word earns its place with no redundancy.
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?
Given the tool has 8 parameters, no output schema, and no annotations, the description covers the essential aspects: what it does, the cost, and the return format. It does not elaborate on error conditions or parameter interdependence, but the schema covers parameter details well.
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 schema provides 100% parameter description coverage, so the baseline is 3. The description mentions 'custom lyrics' and 'style inputs' but only mirrors what the schema already documents, adding no new semantic value.
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 generates a song from a prompt, with optional lyrics and style inputs. The verb 'generate' and resource 'song' are specific, and it naturally distinguishes itself from sibling tools (agent_image, agent_video) by media type.
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 makes it obvious that this tool is for music generation, which implicitly separates it from the image and video sibling tools. However, it does not explicitly state when to use this tool over alternatives or provide exclusion criteria.
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.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
x402-video MCP serverofficial
Alicense-qualityBmaintenanceEnables AI agents to generate videos by paying per call with USDC on Base via the x402 protocol, with no accounts or API keys required.70MIT- 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.11107MIT
- 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