Return the CausalLayer issuer registry, or one issuer record. The registry lists all trusted public-key fingerprints, key algorithms, validity windows, and the anchor-log repo for each active issuer. FREE — no API key required.
| Name | Required | Description | Default |
|---|---|---|---|
| issuer_id | No | Optional issuer id, e.g. 'causallayer-prod-2026-q2'. If omitted, returns the full registry. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral disclosure burden. It indicates the tool is a read operation (returns registry records) and is free. It does not mention restrictions, rate limits, or data freshness, but the disclosed behavior is clear and non-contradictory.
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?
Two sentences: first states primary action, second elaborates on contents and cost. Efficient and front-loaded. No superfluous text.
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 is simple (one optional param, no output schema). The description adequately covers what it returns and that it requires no API key. It does not detail return format, but given the simplicity and lack of output schema, this is not a major gap.
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 coverage is 100% with a single optional parameter. The description adds an example value ('causallayer-prod-2026-q2') and states the behavior when omitted, which adds marginal value beyond the schema description. Baseline 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 clearly states it returns the CausalLayer issuer registry or one issuer record, and lists the contents (fingerprints, algorithms, validity windows, anchor-log repo). This is specific and aligns with the tool name. It does not explicitly differentiate from sibling tools, but the unique name and context make the purpose unambiguous.
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 'FREE — no API key required', which is a useful usage note. However, it provides no guidance on when to use this tool versus alternatives like verify_certificate or query_jurisdiction_overlay. Usage context is implied but not explicit.
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 targets a distinct operation: prospective gate, incident extraction, anchor status, issuer registry, jurisdiction overlay, remediation simulation, incident submission (two variants), and certificate verification (two variants). Despite two submission and two verification tools, their descriptions clearly differentiate the inputs and purposes, preventing ambiguity.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., evaluate_prospective_response, submit_incident, verify_certificate). The verbs are descriptive and the nouns correspond to the domain objects, making the naming predictable and clear.
With 10 tools, the server covers a complex domain (causal liability attribution for AI incidents) without being overwhelming. Each tool serves a distinct role in the workflow, and the count feels well-scoped for the functionality offered.
The tool set covers the core lifecycle: extraction, submission (structured and trace-based), verification (standard and recompute), a prospective gate, jurisdiction query, remediation simulation, and infrastructure queries (anchor, registry). Minor gaps exist, such as no tool to list or search past incidents/certificates, but the essential operations are present.
