Autonomous research agent that pays every source it cites in USDC on Arc via x402 micropayments.
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Average 3.8/5 across 2 of 2 tools scored.
The two tools are highly distinct: keryx_status provides metadata about the server and caller, while research performs a complex research task. There is no functional overlap.
Both use snake_case, but keryx_status is a noun phrase (status check) and research is a bare verb (action). Minor inconsistency in verb_noun pattern, but both are clear and predictable.
With only 2 tools, the server feels minimal. While each tool has a clear scope, the small count may leave users wanting more (e.g., budget configuration, history). Borderline thin for a general-purpose server.
The server covers status and research, but lacks complementary tools like budget management, source listing, or result history. For a dedicated research assistant, these are notable gaps.
keryx_statusKeryx remote MCP statusBDescribe this MCP surface, its payment behavior, and the active caller tier.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds that it returns payment behavior and caller tier, which provides some extra context beyond annotations, but does not elaborate on side effects or details of the return. No contradiction with 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?
The description is a single sentence, front-loaded with the action. It is concise but uses the imperative 'Describe' rather than a declarative statement, which slightly reduces clarity. No wasted words.
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?
The description mentions the high-level outputs (surface, payment, caller tier) but does not detail what 'MCP surface' means or the format of the response. Without an output schema, a more explicit description of the return would improve completeness. It is adequate but leaves ambiguity.
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?
There are 0 parameters, so schema coverage is trivial (100%). The description does not need to add parameter meaning. Baseline of 4 is appropriate given no parameters.
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 describes the MCP surface, payment behavior, and active caller tier. It is a specific verb-resource pairing, though it could be more precise (e.g., 'returns information about...'). It distinguishes from the sibling 'research' tool by implying different functionality.
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?
No guidance on when to use this tool versus alternatives. The description does not provide context for selection or exclusions. The only sibling is 'research', but no comparison is made.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
researchResearch with KeryxAInspectResearch a question under a USDC creator-payment budget. Keryx selects sources, pays access tolls and weighted citation rewards, then returns a grounded answer and receipt.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Optional Keryx model catalog id, for example deepseek-chat. | |
| budget | No | Maximum creator-payment budget in USDC; clamped to the caller's tier. | |
| question | Yes | Research question. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description discloses payment behavior and answer format, adding context beyond annotations (e.g., 'pays access tolls and weighted citation rewards'). No contradictions with 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 efficient sentences with front-loaded purpose. Every word adds value without redundancy.
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?
The description covers key aspects: action, budget mechanism, process, and output (answer and receipt). No output schema, but sufficient for agent selection.
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%, and the description adds meaning to the budget parameter by explaining its role in payments. No parameter documentation gaps.
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 action ('Research a question') and unique mechanism (USDC creator-payment budget, source selection, tolls and rewards), which distinguishes it from sibling 'keryx_status'.
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?
Implied usage: the tool is for research questions with a budget. No explicit when-to-use or when-not-to-use guidance, though sibling difference is clear.
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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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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