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kush-coder

io.github.OpenOSINT/openosint

by kush-coder

search_censys

Query Censys internet-facing infrastructure data: resolve IP addresses to open ports, services, ASN and country, or inspect domain certificate history, SANs, issuer and visibility dates.

Instructions

Search Censys for internet-facing infrastructure data. IP address → open ports, services, ASN, country. Domain → certificate history, SANs, issuer, first/last seen. Uses CENSYS_API_ID and CENSYS_SECRET env vars. [passive] Censys API; returns data Censys already holds.

Input Schema

NameRequiredDescriptionDefault
targetYes
json_outputNoReturn result as structured JSON.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.36.1

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does supply real behavioral context: it names the required credentials (CENSYS_API_ID and CENSYS_SECRET env vars), declares the operation passive ('[passive] ... returns data Censys already holds'), and thus signals a non-invasive read against cached third-party data. It omits rate limits, pagination behavior, and error/failure modes for missing credentials.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four dense, front-loaded fragments using arrow notation carry purpose, per-input outputs, auth requirements, and the passive caveat with zero filler. Each sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description still tells the agent the input types, the returned fields, and the credential prerequisite, which is enough to invoke it correctly. It stops short of pagination/rate-limit/completeness caveats, which would be needed for a fully autonomous call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50% (the required 'target' parameter has no schema description), so the description must compensate — and it does: the 'IP address →' and 'Domain →' clauses establish that 'target' accepts an IP or domain and what each yields. The json_output flag is already documented in the schema, so no extra credit there.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Search) and resource (Censys internet-facing infrastructure data), then enumerates exactly what is returned per input type ('IP address → open ports, services, ASN, country'; 'Domain → certificate history, SANs, issuer, first/last seen'). This is well above a generic 'search' description. It does not, however, distinguish itself from the sibling search_shodan, which likely covers overlapping internet-facing infrastructure data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the returned-field breakdowns (use it when you have an IP or domain and want infrastructure/certificate data) and by the '[passive]' tag. There is no explicit when-to-use/when-not statement and no pointer to alternatives like search_shodan, so the agent must infer the routing decision.

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