pii_guardDetects PII (emails, phones, SSNs, credit cards) in any text with character spans. Compliance agents use this before processing user data. [price: $0.001/call USDC via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Input text |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden and mostly meets it: it discloses that the operation is detection (not redaction), reports character spans, and surfaces the $0.001/call cost. It could add limitations like false-positive behavior or input size bounds, but it does not hide any obvious side effects.
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 definition is two tight clauses and a parenthetical price; every element adds decision-relevant information. The main capability is front-loaded before the compliance use case and pricing.
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?
For a one-parameter detector with no output schema, the description is nearly complete: it states the input domain, the detected PII categories, the character-span output property, a target user, and cost. The main missing piece is a fuller specification of the response format when no PII is found.
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%: the sole parameter 'text' is already described as 'Input text'. The description reinforces that the text is arbitrary ('any text') but does not add parameter-specific constraints such as length limits or encoding, so the baseline of 3 is appropriate.
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 opens with the specific verb 'Detects' and the resource 'PII', names concrete entity types (emails, phones, SSNs, credit cards), and adds 'character spans' to define the output style. This distinguishes it from sibling guards such as prompt_guard and json_guard without needing to open the schema.
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?
'Compliance agents use this before processing user data' is a clear when-to-use statement tied to a concrete workflow, so an agent can select it appropriately. It does not explicitly name alternatives or exclusions, but the stated context is sufficient for a single-purpose detection tool.
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.
Most tools have distinct purposes, but several clusters overlap: domain_facts, page_meta, and scrape all return page title information, and search_verify, hallucination_check, and sweep all target claim validation. The descriptions usually clarify the use case, but the boundaries are not always obvious.
All names use lowercase snake_case, so there is a baseline consistency, but the pattern is mixed: bare verbs like scrape, summarize, and sweep sit alongside noun+noun forms like domain_facts and noun+verb forms like entity_find. The names are readable but do not form a predictable verb_noun API convention.
At 26 tools, this is heavy and above the typical well-scoped 3-15 range, though the server is explicitly positioned as a broad shelf of paid utilities. Many tools are small one-purpose endpoints, so the count feels more like a catalog than a focused suite, but it is not an extreme mismatch.
The shelf covers the major advertised areas: web page analysis, research verification, text guards and NLP, blockchain reads, and image generation. There are some gaps such as no web search and no transaction sending, but agents can typically work around them or pair this with another server.