RPCS1 Agent Tuner
Server Details
Configure AI agents and diagnose oscillation, overload, freeze, and environment mismatch.
- 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.
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 4.4/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools. The single tool has a clear and distinct purpose.
The single tool name follows a consistent verb_noun pattern and is descriptive of its function. No other tools exist to create inconsistency.
One tool feels minimal but is appropriate for the narrow scope of recommending agent configurations. The server would benefit from additional tools for managing configurations, but it is not severely under-scoped.
The tool covers the core functionality of recommending agent configurations. Minor gaps exist, such as listing or updating past recommendations, but the primary use case is well-served.
Available Tools
1 toolrecommend_agent_configurationRecommend AI agent configurationARead-onlyIdempotentInspect
Use this when a user needs concrete LLM and agent-runtime settings matched to environmental entropy, predictability, stakes, context horizon, and commitment style. It diagnoses likely oscillation, overload, or freeze regimes and returns explainable RPCS1 receiver dynamics.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| environment | Yes | ||
| target_platform | Yes | The platform whose runtime parameters should be recommended. |
Output Schema
| Name | Required | Description |
|---|---|---|
| warnings | Yes | |
| reasoning | Yes | |
| confidence | Yes | |
| predicted_regime | Yes | |
| receiver_profile | Yes | |
| platform_parameters | Yes | |
| imm_principles_applied | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: the tool diagnoses oscillation, overload, or freeze regimes and returns explainable dynamics, which goes beyond annotations.
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, front-loaded with usage guidance, no redundant information. Every word earns its place.
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 complexity (nested inputs, output schema exists), the description covers purpose, when-to-use, behavioral insights, and return value (RPCS1 dynamics). The output schema handles return details, so no further description needed.
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 33%, so the description should compensate. It mentions environmental parameters but does not explain task or target_platform fields. No additional parameter-level detail beyond the schema is provided.
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's purpose: to recommend LLM and agent-runtime settings based on environmental factors. The verb 'recommend' and resource 'configuration' are specific, and there are no sibling tools, so no differentiation needed.
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 begins with 'Use this when...', providing explicit guidance on when to invoke the tool. However, it lacks when-not-to-use or alternatives, which would improve the score.
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.
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