agent_research[$0.05 USD per call] On-demand AI agent research, analysis, document creation, and summarization via local LLM
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | Path parameter 'task' | |
| topic | Yes | Path parameter 'topic' | |
| payment | No | Optional x402 payment proof (EIP-3009 signed authorization). Call once without it to receive the payment requirements, then retry with the proof. |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {
+ "task": "summarize",
+ "topic": "x402"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds the local LLM nature and cost per call, which are useful but not critical behavioral traits. It does not contradict annotations and provides modest extra context, such as payment flow hints via the schema, but not in the description itself.
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, efficiently conveying cost and capability. However, the cost is front-loaded, which may not be the most important information for an agent, and the list of capabilities is a bit cluttered. Still, it is concise and well-structured overall.
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 tool has no output schema, so the description should hint at return values or response structure, but it does not. It also fails to explain the payment flow or what 'document creation' implies. The description is too generic for an agent to safely invoke and interpret results, especially given the optional payment parameter.
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%, so the baseline is 3. The description does not add any parameter-level meaning. The schema descriptions are minimal ('Path parameter'), but the high coverage means the description is not required to compensate, though it also doesn't help clarify what values task/topic should take.
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 lists multiple vague capabilities (research, analysis, document creation, summarization) but does not state a clear, specific purpose. It avoids tautology but fails to distinguish from siblings like agent_discovery or market_intelligence. An agent cannot determine what concrete output this tool produces.
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 is given on when to use this tool versus alternatives. There is no mention of scenarios, exclusions, or comparative context. The pricing note is irrelevant to usage selection. The description leaves the agent to infer applicability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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