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getOgQuery

Read-only

Answer natural-language questions about any webpage content using AI. Optionally extract structured data by providing a JSON schema.

Instructions

Ask a natural-language question about the content of any URL and receive an AI-generated answer via the OpenGraph.io API. Optionally pass a responseStructure schema to extract structured data. Note: uses 100 API credits per request (or 200 with a large model). Query API remains on v1.1 until the billing path check is updated for v3.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the webpage to query.
queryYesNatural-language question to answer about the page content.
retryNoAutomatically retry failed requests.
cache_okNoUse cached page results. Set to false to bypass cache. Defaults to true.
modelSizeNoAI model size. 'small' uses 100 credits; 'large' uses 200 credits. Defaults to 'small'.
use_proxyNoRoute the request through a standard proxy.
accept_langNoAccept-Language header for the outbound request. Defaults to 'auto'.
ai_sanitizeNoScan the fetched content for prompt-injection attempts.
auto_renderNoAutomatically detect and switch to headless rendering for SPA pages.
full_renderNoFully render the page with JavaScript before querying.
max_retriesNoMaximum number of retry attempts (1–4). Defaults to 4.
use_premiumNoRoute the request through a premium proxy.
use_superiorNoRoute the request through a superior-tier proxy.
max_cache_ageNoMaximum cache age in milliseconds. Defaults to 432000000 (5 days).
proxy_countryNoTwo-letter ISO country code for geo-targeted proxy exit node.
ai_sanitize_modeNo'sanitize' cleans the content, 'warn' returns a safety report, 'block' returns HTTP 422 when risk_score >= 0.7.
responseStructureNoOptional JSON schema describing the shape of the desired response. When provided, the model returns a structured JSON answer.
wait_for_selectorNoCSS selector to wait for before querying.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
resultNoAI-generated answer. May be a string or structured object.
questionYes
Behavior3/5

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

Annotations already declare `readOnlyHint: true` and `destructiveHint: false`. The description adds credit consumption details (100 or 200 credits) and a version note. No contradictions; additional behavioral context beyond annotations is minimal but adequate.

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?

The description is concise: two sentences plus a brief note. It front-loads the core purpose and critical details (API credit usage, version status). Every sentence adds value with no redundancy.

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?

Given high schema coverage, existence of an output schema (not shown), and annotations that cover safety, the description is mostly complete. It could mention return format or pagination, but output schema likely handles that. Overall adequate for the tool's complexity.

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

Parameters3/5

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

Schema description coverage is 100% (all 18 parameters have descriptions). The description adds no extra parameter meaning beyond what the schema provides, except for highlighting `responseStructure` for structured data. Baseline score of 3 is appropriate.

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

Purpose5/5

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

The description clearly states the tool's function: 'Ask a natural-language question about the content of any URL and receive an AI-generated answer.' It distinguishes from sibling tools like `getOgData` or `getOgExtract` by focusing on natural-language queries and optional structured extraction via `responseStructure`.

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?

The description implies usage for querying URL content with NL questions but provides no explicit guidance on when to use this tool versus alternatives (e.g., `getOgExtract` for structured data without AI). No when-not or exclusion cases are mentioned.

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

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