Heuristic likelihood (0-100) that a passage of text is AI-generated, from lexical-diversity and burstiness signals with a transparent rationale. Pure, no network; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text passage to score |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adds some behavioral context: it's heuristic, free, and has no network dependency. But it doesn't disclose potential limitations, such as text length constraints or the format of the 'transparent rationale' in the output.
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 (two sentences) and front-loaded with the core purpose and output range. No unnecessary words.
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 tool has no output schema and no annotations. The description mentions the output is a 0-100 likelihood and 'transparent rationale', but doesn't fully describe the response structure. For a simple tool, it's somewhat complete but could specify the exact output fields.
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 input schema has 100% coverage, so baseline is 3. The description adds meaning by explaining the tool uses lexical-diversity and burstiness signals, beyond the schema's simple 'Text passage to score' 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 clearly states the tool gives a heuristic likelihood (0-100) of AI-generated text using lexical-diversity and burstiness signals. This distinguishes it from sibling tools like watermark_detect or provenance_check, which focus on other authenticity aspects.
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 mentions the tool is 'Pure, no network; price 0.0 (free)', indicating it's a local, free heuristic. However, it doesn't explicitly state when to use this tool versus alternatives like C2PA inspection or domain reputation checks, but the context is clear enough.
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 clearly distinct purposes, especially the ledger-watch groups with domain prefixes. However, there are near-duplicate utilities such as content_authenticity_domain_reputation and domain_intel_reputation, and fx_tax_convert overlaps with price_oracle_fx_rate/convert. The sheer number of tools also increases the chance of selecting the wrong one, though descriptions are generally clear.
The naming is largely consistent with a <domain>_<action> or <domain>_<watch>_<action> pattern, and all names use snake_case. Minor deviations include standalone names like entity_search, kyb_report, verify_receipt, and the confusing singular/plural pair of sanction_watch_* and sanctions_screen_*. Overall, the pattern is predictable.
With 172 tools, this server is far beyond the typical well-scoped range. It bundles dozens of unrelated utility domains (weather, carbon, CVE, geo, etc.) alongside the Japan public-ledger watches, making it unwieldy for an agent to navigate. This extreme count is a major coherence problem.
For the core Japan public-ledger domain, coverage is excellent: each ledger has search, get, timeline, recent_changes, and verify_ledger, plus cross-ledger entity_search and temporal_query. The unrelated utility areas are also fairly complete for their own purposes, but the server's scope is so broad that some utilities are duplicated (e.g., multiple currency converters). Overall, no critical dead ends in the main domain.