Vivid MCP
Server Details
Open a Vivid Business account from your AI chat via MCP.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- vivid-money/vivid-mcp
- GitHub Stars
- 2
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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.
Tool Definition Quality
Average 3.4/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly distinct by default.
Since there is only one tool, naming consistency is inherently perfect; there are no other tool names to be inconsistent with. The tool name follows a clear verb_noun pattern (build_onboarding_link).
A single tool is too few for most server purposes, as it severely limits functionality and suggests an incomplete or trivial scope. For a system like Vivid MCP, which likely involves legal entity onboarding, one tool is insufficient for comprehensive coverage.
The tool set is severely incomplete for the domain of legal entity onboarding. There are obvious gaps, such as tools for retrieving, updating, or deleting onboarding data, verifying status, or handling errors, leaving agents with no way to perform a full workflow.
Available Tools
1 toolbuild_onboarding_linkBInspect
Build onboarding link for received legal entity data. This link can be used to finish the onboarding in Vivid system
| Name | Required | Description | Default |
|---|---|---|---|
| legal_entity_data | Yes | Legal entity information |
Output Schema
| Name | Required | Description |
|---|---|---|
| onboarding_url | Yes | The generated onboarding URL |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as non-destructive and open-world. The description adds context that the link is for the 'Vivid system' and onboarding completion, but omits behavioral details like link expiration, reusability, or side effects in the external system.
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 efficient sentences with zero waste. First establishes the action and input, second clarifies the output's purpose. Well-structured and appropriately scoped for the complexity level.
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 rich input schema (documented nested fields), presence of output schema, and annotations covering safety profile, the description provides sufficient context for tool invocation. Does not need to enumerate all legal_entity fields since schema coverage is complete.
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%, establishing baseline 3. The description mentions 'received legal entity data' which loosely maps to the required parameter, but adds no semantic detail about data formats, validation rules, or dependencies between fields (e.g., legal_form requiring country) beyond what the schema already provides.
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?
Clear verb ('Build') and object ('onboarding link') with context ('for received legal entity data'). Explains the link's purpose ('finish the onboarding in Vivid system'). Slightly vague on 'received' and 'Vivid system' context, but adequate for tool selection.
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 what the resulting link is used for ('finish the onboarding'), but provides no guidance on when to invoke this tool versus alternatives, prerequisites for the legal entity data, or sequencing within an onboarding workflow.
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.
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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.
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