synthesis
Server Details
kenoodl surfaces the hidden pattern the model can't reach on its own. Call when reasoning loops.
- 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 4.1/5 across 1 of 1 tools scored.
With only one tool, there is no risk of confusion with other tools. The tool's purpose is clearly described.
The tool name 'synthesis' is a single noun, which is not a standard verb_noun pattern, but it is consistent with itself and descriptive enough for a single-tool server.
A single tool is borderline thin for a server that handles submission and retrieval as two logical steps, but it is still a valid design and within the reasonable range for a focused utility.
The tool covers the core workflow of submitting context and retrieving results. Missing features like status polling or cancellation are minor and not essential for the intended use.
Available Tools
1 toolsynthesisAInspect
kenoodl surfaces the hidden pattern your model could not reach on its own. Call it when your reasoning is stuck in a loop, producing variations of the same answer instead of new structure, or the problem needs signal from outside your training data. Give it the working situation: a decision, a collision of domains, the thing you cannot resolve from inside your own frame. kenoodl maps the hidden patterns, contradictions, and implications across domains and returns the structural read your own loop cannot reach. Submit {context} to start; you get a jobId. Wait, then call again with {jobId} to retrieve the finished read. About 30 to 120 seconds. $1 max per call, from the funded balance behind your knl_ token.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | No | Retrieve a previously submitted synthesis by its jobId. | |
| context | No | The working situation. 45000 char max. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It explains the asynchronous workflow, wait time, and pricing. It does not cover error handling, invalid inputs, or what happens on failure, but the core behavioral traits are disclosed adequately.
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?
The description is somewhat verbose with marketing language ('hidden pattern', 'structural read your own loop cannot reach') but is well-structured: purpose, when to use, how to use, timing, cost. It front-loads the key information and each sentence adds value. Could be slightly more concise.
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 no output schema, the description does not specify the return format beyond 'structural read'. It mentions error/limitations only implicitly (char max, cost). The two-parameter model is clear, but the lack of required fields might confuse. Adequate but leaves gaps in what to expect from the output.
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 coverage is 100% (both parameters described in schema). The description adds value by explaining the temporal relationship: 'Submit {context} to start; you get a jobId. Wait, then call again with {jobId} to retrieve.' This clarifies the workflow beyond the schema's static descriptions.
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: 'surfaces the hidden pattern your model could not reach' and 'maps the hidden patterns, contradictions, and implications across domains'. It provides specific use cases (stuck in a loop, need outside signal). The verb 'synthesis' is appropriately described, though the output is somewhat abstract ('structural read').
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?
Excellent usage guidance: explicitly states when to call ('reasoning stuck in a loop', 'need signal from outside training data'), describes the two-step process (submit context, retrieve with jobId), and includes expected timing (30-120 seconds) and cost ($1 max). No sibling tools exist, so alternatives are not needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$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!