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

Even with readOnlyHint/openWorldHint/idempotentHint annotations, the description adds substantial behavioral detail: the two-tier routing (SEC EDGAR/XBRL vs. grounded pipeline), the exact verdict set, the meaning of 'could_not_verify' (including verification_error and not being evidence), the meaning of 'unsupported', and the guarantee of verbatim evidence with citations. This goes well beyond the annotations and provides crucial caller guidance.

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 longer than average but every section earns its place: trigger phrases, usage context, routing logic, return semantics, and caller warnings. It is front-loaded with the most identifying examples and structured clearly, though the density of clauses (e.g., the long routed-pipeline sentence) makes it slightly heavier than necessary.

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?

There is no output schema, yet the description fully specifies the return contract: verdicts, actual value with pipeworx:// citation, reasoning, and detailed failure modes. It also explains what unsupported and could_not_verify mean, how to interpret errors, and what sources are used. For a tool of this complexity, the description is remarkably complete.

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 adds only modest extra nuance beyond the schema, such as recommending tolerance_pct=1–2 for hallucination detection and noting that tolerance overrides claim wording. This is helpful but not a major semantic addition since the schema already documents both parameters thoroughly.

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 identifies the tool as 'natural-language claim verification against authoritative sources' with explicit trigger phrases and examples ('fact check', 'verify the claim that…'). It uniquely distinguishes itself from sibling research/entity tools by focusing on true/false verdicts for claims, and it even mentions replacing a 4–6 call pipeline, which reinforces its specific role.

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 gives an explicit trigger: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides routing guidance for company-financial claims versus all other factual claims. However, it does not explicitly name alternatives or state exclusions (e.g., when to use ask_pipeworx_grounded or deep_research instead), so it falls slightly short of full alternative differentiation.

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