Detect whether a piece of text was model-generated and whether this system has emitted something materially identical before. Call before writing to long-term memory: model output that gets re-ingested comes back later as a trusted fact, and every downstream run that reasons over it is wasted work you will not be able to trace. Costs $0.25 in USDC.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool has a monetary cost ($0.25 USDC) and hints at a side effect: it allows the system to avoid wasted traceability work. However, it does not state the output format or whether it returns a Boolean and/or confidence score, or if it performs writes. The cost disclosure is a strong transparency point, but the output behavior is not fully specified.
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 compact but informative. It leads with the core purpose, then the critical usage context, and ends with the cost. Every sentence adds value, and the structure is logical. There is no extraneous content.
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's complexity (simple input, deterministic output likely), the description covers purpose, usage, and cost well. It lacks output schema details and does not explain the return value, but the agent can likely interpret the result without that. The warning about memory is valuable. Missing: whether it also checks if the text is similar to previously emitted text or exactly identical, but the phrase 'materially identical' suggests similarity matching, which is a nuance. Overall, it's quite complete for a single-parameter tool.
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?
The schema has one 'content' parameter described only as 'string' with 0% schema description coverage. The description clarifies that 'content' is the text to check, which is straightforward. The description adds usage context (check before writing to memory) but doesn't specify encoding or length limits, but with a single string parameter, the semantics are clear enough from the name and description.
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 states a clear, specific purpose: detecting whether text is model-generated and whether it has been emitted before by this system. It identifies the resource (text) and the operation (check provenance), distinguishing it from sibling tools focused on answers, claims, sources, and waste reporting.
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?
Usage guidance is explicit: call before writing to long-term memory. It explains when not to use it implicitly (before memory writes) and warns about the consequence of skipping it (re-ingested model output becomes trusted fact, wasted work). The cost is also given, helping the agent decide. No alternative tool is named, but given the sibling list, provenance detection is unique.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a distinct purpose: verifying claims, sources, answers, checking provenance, tracking waste, querying pricing, and asking an AI assistant. No two tools overlap in their intended use, making it easy for an agent to select the right one.
Tool names follow mixed conventions: three verify_* tools share a prefix, but others use different verbs (check_, report_) or noun forms (pricing, waste_report) and one reverses the verb pattern (answer_share). While individual names are descriptive, the lack of a uniform verb_noun structure makes the set less predictable.
With 8 tools, the count is well within the ideal range for a focused verification service. Each tool addresses a distinct need—claim, source, and answer verification, provenance checking, waste tracking, and pricing—without redundancy.
The surface covers all advertised types of verification (claim, source, answer) plus provenance checking and waste tracking. Minor gaps exist like no dedicated tool for bulk or historical verification, but the core workflows are fully represented.