Selecting Data, Search, Model, and Challenge APIs: Define the Deliverable Outcome First

Product status update (checked against Beijing time 2026-09-13): EveryInfra General Text API was retired on 2026-09-12; the model, SDK, and chat/completions examples in this document are for understanding historical client behavior and migration boundaries only and do not constitute a new integration guide. Historical calls retain 410 ai_chat_retired compatibility; when processing personal EveryData collection results, switch to data cleaning tied to bound sources, independent permissions, and fixed recipes.

"I need to get information about a product" is not enough to select an API. You might already have a product URL and need a review list, or you might only have a category and want to find official documentation, or you might have already retrieved reviews and need only a problem summary. These three tasks have similar inputs but completely different deliverables. Choosing the wrong entry point commonly results in using search snippets to impersonate structured data, or having models invent facts they have never seen.

This document divides API selection into two decisions: first choose the capability required to complete the task, then choose how your application calls it. Data, search, models, and challenges solve different problems, while REST and MCP are integration methods. MCP is not a fifth data source, nor does it imply that all tasks should be delegated to autonomous agents.

State the Deliverable in a Single Sentence First

An executable requirement must specify the input, the target scope, the output fields to retain, and how success is determined. For example, "Read the reviews corresponding to this product URL within authorized limits, retain the review ID, source, and available rating, and hand them over to a person for compatibility review." This makes it easier to select an interface and identify evidence gaps than "research this product."

• Need objects and fields on a source platform: locate platform/action from the data capability catalog.
• Need to discover web pages, find original materials, or read pages: select discovery and reading capabilities from the search tool catalog.
• Already have sufficient materials and need categorization, translation, or summarization: select the model entry point and define output validation.
• Authorization testing in your own system involves challenges: check official testing mechanisms first before deciding whether challenge capabilities are needed.
• Need clients supporting tool protocols to call the above capabilities: separately evaluate MCP authentication, permissions, and client acceptance.

When the input is only a URL, you still cannot immediately determine the entry point. The same product URL can be used to read a product object or read page body text; the former requires structured fields, while the latter requires document content. Determine the output first to avoid treating "having a link" as the sole classification criterion.

Select Data APIs for Known Platform Objects

POST /api/v1/social

EveryData Requests are described using platform, action, and params. Objects such as platform accounts, posts, comments, products, or rankings are best discovered from this layer. Even if the input is a keyword, as long as the target is clearly a post or product on a specific platform, you should check that platform's search first rather than automatically jumping to open web search.

curl -fsS --max-time 30 \
  'https://api.everyinfra.com/api/v1/social/catalog?compact=1' \
  | jq '[.capabilities[] | {
      platform, action, required_params, mode, returns_list
    }]'

When reading the catalog, pay attention to the action's required inputs, whether it returns an object or a list, and whether it is synchronous or asynchronous. Field names appearing in a dictionary only indicate that the contract may include them; they do not guarantee presence in every row, nor do they imply historical completeness, pagination, or private content access. Source IDs, missing values, and actual sampling scopes must be preserved.

For example, after reading reviews from an authorized product, the data interface is responsible for delivering available records, not for judging product defects. User topics, sentiments, and suggestions belong to the subsequent analysis layer; the two layers are linked by review identity and version to prevent model label corrections from rewriting raw data.

Select Search Tools to Find or Read Materials

POST /api/v1/search

When you have a question but no source URL, perform discovery first; when you have a confirmed URL, prioritize reading the original text; when you need to cross-check relationships among multiple sources, schedule cross-checking. EverySearch Different tools handle these stages and should not be collapsed by applications into a single function that always passes just q.

curl -fsS --max-time 30 \
  'https://api.everyinfra.com/api/v1/search/tools' \
  | jq '[.tools[] | {
      tool, required_params, optional_params
    }]'

Tool identifiers in the catalog are tool. web and crosscheck accept questions, while read accepts URLs; actual available parameters are subject to current entries. News, forum, and academic entry points change retrieval scope but do not automatically increase evidence credibility. Forums are suitable for discovering specific problem clues, and interface definitions should still refer to primary sources of the corresponding version.

Success at this layer is not "having ten results," but finding sources that are readable, relevant, and capable of supporting current assertions. Multiple domains republishing the same announcement still originate from a single observation; search snippets cannot impersonate read full text, and read failures cannot be filled in by models generating page content.

Select Model APIs After Acquiring Materials

POST /api/v1/chat/completions

Models are suited for converting authorized materials into themes, summaries, translations, or structures pending review. They should not be required to guarantee up-to-date facts when no search or data input is provided. When extracting a field, define how unknowns are expressed; values without evidence return a missing status rather than having the model fabricate data to satisfy a complete JSON schema.

curl -fsS --max-time 30 \
  'https://api.everyinfra.com/api/v1/models' \
  | jq '{default_model, models: [.data[].id]}'

EveryInfra Current implementation is a non-streaming text path. SDK request shape compatibility does not mean all OpenAI parameters, tool calls, or multimodal entry points are available, nor is the model catalog an itemized service acceptance result. Fix the model ID during integration, check with minimal text input first, and then restore business options.

Output validation has two steps: first check whether the structure can be read programmatically, and then check whether conclusions are supported by the input. Topic classification should retain original text evidence; summaries should trace back to sources. For judgments affecting customers, employees, or public statements, models prepare candidates only, and final actions retain corresponding human confirmation.

Lewis et al.'s RAG paper examined the combination of generative models and retrievable external storage. This provides technical context for distinguishing "acquiring materials" from "generating based on materials"; this document therefore adopts phased acceptance rather than treating search hits or generation fluency as guarantees of factual correctness.

Challenge Capabilities Are Part of Authorization Testing

In your own login or form testing, check whether challenge products provide test keys, test modes, or official demos first. Many integration issues can be verified directly using these mechanisms without obtaining real challenge results. Only when your current authorization workflow genuinely requires it should you check EverySolve types and inputs.

curl -fsS --max-time 30 \
  'https://api.everyinfra.com/api/v1/captcha/types' \
  | jq '[.types[] | {
      type, available, required_params, solution
    }]'

Public sitekeys, page URLs, challenge parameters, and server-side secrets are not the same type of input; do not accidentally pass secrets to solver interfaces or write them into logs. Different solution types may also differ and cannot all be handled as a single token field. The appearance of a type in the catalog does not mean completion is guaranteed at that moment, nor does obtaining a result mean the target website server has passed verification.

Challenge capabilities do not replace account permissions, platform licenses, or user confirmation. For processes involving payments, identity verification, or account recovery, challenge success is not a free pass to continue executing sensitive operations. This document does not provide unauthorized third-party access paths.

Choose REST or MCP After Capabilities Are Decided

When invocation steps are fixed, backend tasks exist, and acceptance conditions are clear, REST generally facilitates explicit management of each request, retry, and result storage. When tool clients need to select actions based on context, MCP can be evaluated; however, wrapping interfaces into tools does not eliminate input validation, permissions, billing, or user approval. This is an application design trade-off, not a performance comparison.

MCP requires separate verification of protocol discovery, authentication methods, client visibility, and actual business results. Seeing the name tools/list proves discovery paths only; it does not mean Claude, ChatGPT, or Cursor current accounts can pass required credentials, nor does it mean every tool has completed an actual invocation.

For tasks with external side effects, clients should present targets and parameters while retaining confirmation. For read-only tasks, data scope and available tools must also be restricted to prevent malicious text in web pages or comments from becoming new operational instructions. Protocol selection cannot replace these business boundaries.

If a selected capability involves long-running tasks, you can design delivery checks against Microsoft's asynchronous request-response pattern: record acceptance, processing, and final results separately. These states persist after choosing an integration method; protocol names do not replace task completion conditions.

How to Combine Three Common Tasks

• Product issue research: data APIs acquire authorized reviews, deduplicate by identity, models generate themes with original text, and humans review before handing over to product teams. Accept review ownership first, and do not use summary generation to represent overall workflow success.
• Technical documentation verification: search discovers version-matching official pages, reads body text, preserves paragraph positions, and organizes parameter differences. If reliable URLs already exist, start directly from reading without repeating search.
• Custom form integration: use official testing mechanisms to verify frontend and backend first; if authorization testing requires challenge services, check types, result structures, and target server verification. Submitting forms and processing challenges are two independent actions.

Not every step in a workflow requires model participation. Deterministic parameter checks, time conversions, identity deduplication, and arithmetic calculations can be performed using ordinary code. Introduce models only when language comprehension or candidate explanation formation is needed, and retain traceable inputs.

Turn Selection Records into a Reviewable Acceptance Check

It is recommended to leave a brief, consistent record for each selected capability: input contract, expected output, source scope, authorization basis, synchronous/asynchronous mode, success and empty result criteria, timeout handling, billing checks, and responsible owner. OpenAPI can describe HTTP interfaces and structures, but it still cannot independently prove that a business task completed or that all data uses were authorized.

The first round should execute only a minimal objective, checking whether actual results can enter business systems. Retain queries and human reviews for unknown results, unobserved status for missing fields, and failure information for unreadable sources. Do not automatically switch models, keys, or expand collection scope just to make the workflow look complete.

The sign of completed selection is not using every product name, but ensuring that every necessary step has clear inputs, credible deliverables, and failure routing. Verify this single path clearly before deciding to expand objects, enable agents, or increase automation.