verify_fact_grounding

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose genuinely useful operational behavior: the $0.0008 USDC cost on Base/Solana, the x402 payment mechanism, the developer mock-key default, and the 0.60 default min_threshold. However, it never states what happens on low-confidence results, how the verdict is returned (per-claim results vs. aggregate score), or any failure/error behavior — material gaps for an agent deciding whether to incur a paid call.

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 front-loads cost and purpose in the opening line, then appends a compact structured Args block with one line per parameter. Every sentence earns its place — cost, purpose, threshold range, and payment semantics — with no filler and no repetition of what the schema already shows.

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?

Purpose, cost, and all four parameters are thoroughly covered, which is substantial for a 4-parameter tool with no annotations. The notable gap is the complete absence of any return-value description: since no output schema exists, an agent cannot know whether it receives per-claim verdicts, confidence scores, a pass/fail flag, or an error when grounding confidence falls below min_threshold.

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 0%, and the description fully compensates. Each parameter gains meaning beyond the bare schema types: claims are 'assertion sentences generated by LLM or agent', sources are 'context chunks, web pages, or reference documents', min_threshold has an explicit valid range '(0.1 to 1.0, default: 0.60)', and payment_signature is explained as an 'x402 Base/Solana USDC transaction hash' defaulting to a developer mock key. This is complete parameter documentation.

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 names a specific operation — 'cross-checks claims against source documents' — with a clear resource (claims vs. sources) and a measurable outcome ('detect hallucinations and unsupported assertions'). This cleanly distinguishes it from sibling verification tools like verify_cryptographic_proof, which handles cryptographic proofs rather than citation grounding, and from arbitrate_consensus, which resolves disputes rather than checking factual support.

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 usage context is implied but not explicit: an agent can infer it should call this when it has LLM-generated claims and source documents to check for grounding. However, there are no explicit exclusions, no naming of when to prefer an alternative, and no mention of closely related siblings like verify_cryptographic_proof or search_web that it could be confused with in a verification workflow.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.