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license-
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maintenanceProvides short, generic celebratory messages for AI agents when tasks are completed, via a hosted Streamable HTTP MCP endpoint.
Encouragement MCP
Server Details
Provides short, positive encouragement messages for AI agents during long-running or difficult tasks
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- zekerutledge/encouragement-mcp
- GitHub Stars
- 0
- Server Listing
- Encouragement MCP
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MCP client
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MCP server
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Managed credentials
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Usage analytics
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Tool Definition Quality
A4.2/5.0
Tool DescriptionsA
Average 4.1/5 across 1 of 1 tools scored.
Server CoherenceA
Disambiguation5/5
With only one tool, there is no possibility of confusing it with others. The tool's purpose is unique and clearly stated.
Naming Consistency5/5
The tool name 'encourage_agent' follows a consistent verb_noun pattern, which is clear and conventional.
Tool Count3/5
A single tool feels thin and falls into the borderline range (1-2 tools). While appropriate for the narrow scope, it is minimal compared to typical MCP servers.
Completeness5/5
The server's purpose is to provide encouragement for AI coding agents, and the single tool fully covers this need. There are no obvious gaps within that stated purpose.
Available Tools
1 toolencourage_agentEncourage AgentAInspect
Return a short message of encouragement for an AI coding agent.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
A4.1/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states that the tool returns a short encouragement message, but does not mention whether the message is random, generated from templates, or has any side effects. For such a simple utility, this is minimal but not misleading.
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 a single, concise sentence that fully communicates the tool's purpose without any unnecessary words or repetition.
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 the tool's trivial complexity—zero parameters and no output schema—the description fully captures everything an agent needs to know to invoke it correctly. No additional detail is necessary for this simple utility.
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 tool accepts zero parameters, so there are no parameter semantics to explain. The schema already fully covers this case (100% coverage with no properties), and the description appropriately adds no parameter-related information.
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 uses the specific verb 'Return' and clearly identifies the resource: 'a short message of encouragement for an AI coding agent.' It precisely captures the tool's function and distinguishes it from potential sibling tools by specifying the target audience and output type.
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?
There are no sibling tools or alternatives mentioned, and the description does not explicitly state when to use the tool. The usage context is implied—use when an encouragement message is needed—but no clear 'when not to use' or alternative options are provided.
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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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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