Check multiple prompts for PROMPT IOC patterns in a single call.
More efficient than calling check_prompt() in a loop — tokenization
overhead is amortized and the cache reference is shared.
Args:
texts: List of prompt strings to check
Returns:
One result dict per input text, in the same order.
| Name | Required | Description | Default |
|---|---|---|---|
| texts | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It does disclose the return behavior ('One result dict per input text, in the same order'), which is useful. However, it omits any mention of side effects (if any), error handling, or constraints like batch size limits. For a non-destructive check operation, the lack of explicit safety statements is a gap, but not a contradiction.
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 compact and front-loaded: purpose in first sentence, usage rationale in the second, then Args and Returns. Every sentence adds value—no filler. The structure is clean and easily scannable for an agent.
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?
Given the tool's simplicity (one parameter, no nested objects, output schema present), the description covers purpose, usage, parameter meaning, and return structure adequately. It lacks explicit limitations (e.g., maximum batch size) or error behavior, but for a straightforward batch check, it is sufficiently 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?
The schema already defines 'texts' as a required array of strings. The description's Args section restates 'List of prompt strings to check', adding minimal new meaning beyond the schema. Since schema coverage is 0% by metric but the param is straightforward, a baseline score of 3 is appropriate given the schema's clarity and the description's confirmation.
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 'Check multiple prompts for PROMPT IOC patterns in a single call.' It uses a specific verb (Check), identifies the resource (multiple prompts), and specifies the pattern (PROMPT IOC). This distinguishes it from the sibling tool 'check_prompt' which handles a single prompt.
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 explicitly contrasts with check_prompt() in a loop and explains the efficiency benefit (amortized tokenization and shared cache reference). This directly tells the agent when to use this tool over the alternative, fulfilling the usage guidance dimension fully.
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, but there is notable overlap among threat-fetching methods: get_new_threats, poll_since, and drain_subscription all retrieve new threats via different mechanisms, and several stats tools (get_stats, freshness_limits, prompt_cache_stats) serve similar informational roles. Overall, descriptions help disambiguate, but a few tools could be confused.
The naming is predominantly snake_case, but mixes verb_noun (check_freshness, submit_ioc), noun phrases (family_threats, freshness_limits), and even a question (is_ioc_revoked). 'unsubscribe' breaks the pattern of other subscription tools (subscribe_threats, drain_subscription). This inconsistency is noticeable though still readable.
30 tools is on the heavy side for a single MCP server, exceeding the typical 15-tool comfort zone. However, the server covers a broad domain—IOC submission, retrieval, subscriptions, freshness tracking, prompt/skill scanning, and registry monitoring—so the large number is somewhat justified by the scope.
The tool surface covers the full threat intelligence lifecycle: submit (submit_ioc, submit_batch), query (lookup_ioc, search_by_type, recent_threats, family_threats), subscribe (subscribe_threats, drain_subscription), update (report_detection, vote_false_positive), and revoke (revoke_ioc). Minor gaps exist, such as the absence of a direct delete or update signature tool and no get-by-signature-id endpoint, but these are manageable for agents.