Read the agent-integrity ledger: total spend, spend that produced nothing usable, the waste rate, and a breakdown by failure kind and by agent. Self-reported and independently detected waste are reported separately — an agent grading its own work is not evidence. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | ISO timestamp |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It explicitly notes that self-reported and independently detected waste are reported separately, indicating a non-trivial distinction in the tool's output. This is a key behavioral disclosure, though it does not describe side effects (which are likely none for a read operation).
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 concise and well-structured, providing necessary detail without redundancy. It uses a clear list of metrics and a separate sentence for the self-reported distinction, making it easy to parse.
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 enumerates the fields the tool returns (total spend, waste, waste rate, breakdowns) and the distinction in reporting. While there is no output schema, this description gives sufficient context for most use cases. It does not specify the output format (e.g., JSON shape), but that is not critical for a read operation.
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 only parameter, 'since', is described as an ISO timestamp, which is clear and sufficient. The description does not elaborate on default behavior or filtering semantics, but that is not necessary given the parameter's straightforward meaning.
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 explicitly states the tool reads the agent-integrity ledger and enumerates the specific metrics it returns (total spend, wasted spend, waste rate, breakdowns). This clearly distinguishes it from the sibling tools, which suggest different actions (e.g., report_waste, verify_answer).
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 clearly explains what the tool does and the key nuance about self-reported vs. independently detected waste. However, it does not explicitly state when to use this tool over the alternatives (e.g., when to call waste_report vs. report_waste or verify_answer), though the sibling names infer a read-only reporting role.
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.