RealWorldAPIs

com.realworldapis由 realworldapis.com 發布官方發布者v3.0.1更新於 Oct 25, 2026

Evidence-first registry of real-world APIs for AI agents, with verified metadata and comparison.

已驗證Streamable HTTP可網頁執行網頁搜尋與抓取開發者工具

概覽

AI 產生的概覽

一個遠端唯讀的真實世界 API 登錄表,讓助理為實體世界任務尋找、篩選、驗證並比較 API。

可以做什麼
針對開放式自然語言任務或目標推薦 API,候選清單來自託管推薦服務,比較僅限這些候選。依類別、能力、協定狀態或就緒度搜尋與篩選 API 目錄。直接搜尋已驗證的能力圖譜,查看任務層級能力及其執行條件。驗證某個已知 API 是否滿足某一具體需求,回傳 YES、NO、PARTIAL 或 UNKNOWN 及證據。依名稱或 slug 取得單一 API 的完整決策檔案,並對 2 至 8 個已知 API 逐欄位比較。
適用情境
適合需要在真實世界 API(天氣、地圖、移動、物流、物聯網、能源、機器人)之間做選擇,或在整合前確認某個 API 是否滿足特定需求的場景。也適合要求證據支撐、未知項目不得當成已確認支援的選型工作。
執行需求
遠端 Streamable HTTP 端點;目前不需 API 金鑰,也未宣告環境變數或請求標頭。用戶端需支援遠端 Streamable HTTP 連線,並需能存取該託管服務的網路。
安裝前請注意
六個工具皆為唯讀,僅回傳目錄與證據資料,不寫入、不付款。結果來自託管的自有服務,可能獨立於公開文件而變動,README 建議在正式整合前核對供應商文件。Unknown 值不代表已確認支援。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 RealWorldAPIs,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/com.realworldapis%2Frealworldapis/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "realworldapis": {
      "type": "http",
      "url": "https://mcp.realworldapis.com/mcp"
    }
  }
}

README

RealWorldAPIs MCP

RealWorldAPIs is an evidence-first API registry for AI agents connecting to the physical world.

A real-world API connects software or AI agents to data, devices, infrastructure or services representing the physical world — including weather, maps, mobility, logistics, IoT, energy and robotics.

This repository documents and provides a publicly inspectable MCP interface implementation for RealWorldAPIs.

The live hosted RealWorldAPIs service is the source of truth for current tools, schemas, recommendation behavior and evidence semantics. The hosted service may evolve independently from the code visible in this repository.

One core evidence rule applies throughout the service:

Unknown remains Unknown and is never treated as confirmed support.

Public interface, proprietary intelligence

RealWorldAPIs intentionally separates its public interoperability surface from its proprietary decision systems.

Public / interoperability surface

The following are intended to be publicly discoverable and usable for interoperability:

  • the remote MCP endpoint and tool schemas
  • public HTTP endpoints and OpenAPI descriptions
  • public API catalog and capability/evidence outputs
  • public documentation and usage examples
  • factual provider metadata and evidence links
  • structured recommendation and verification results returned by the hosted service

Proprietary hosted systems

The following are not published or licensed through this repository:

  • the canonical RealWorldAPIs recommendation and ranking engine
  • natural-language task parsing and task-frame logic
  • capability retrieval, fusion and sibling-capability reranking logic
  • internal scoring rules, weights and optimization logic
  • verification and evidence-processing implementation
  • assertion-generation and research pipelines
  • benchmark tooling, active private evaluation sets and holdout sets
  • internal administration, proposal and approval systems

The MCP layer calls hosted canonical RealWorldAPIs services for recommendation and evidence-backed decision support. Publishing an interface, schema or result does not publish the underlying hosted implementation.

See PROPRIETARY_NOTICE.md for the repository rights notice.

Live service

No RealWorldAPIs API key is currently required to discover or call the public MCP tools.

MCP tools

The live RealWorldAPIs MCP service currently exposes six read-only tools:

ToolPurpose
find_api_for_taskNatural-language API selection using the canonical hosted recommender, followed by constrained comparison over returned candidates.
search_apisDeterministic search and filtering over canonical API fields.
search_capabilitiesSearch the verified capability graph directly.
verify_requirementEvidence-backed YES / NO / PARTIAL / UNKNOWN verification of one API against a specific requirement.
get_apiRetrieve one canonical API decision profile by slug or name.
compare_apisDeterministic side-by-side comparison of canonical fields.

The live tool set and schemas are authoritative. The source-visible implementation in this repository may lag the hosted service.

Which tool should I use?

If you need...Use
Recommendations for an open-ended natural-language task or desired outcomefind_api_for_task
Exact filtering by category, capability, protocol status or readinesssearch_apis
Direct discovery over verified task-level capabilitiessearch_capabilities
Evidence-backed adjudication of an exact API requirementverify_requirement
The canonical record for one already-known APIget_api
A field-by-field comparison of already-known candidatescompare_apis

find_api_for_task

Use when the user describes a goal rather than a specific API.

Example task:

json
{  "task": "I need my agent to connect to a car and predict weather on the route"}

The structured shortlist returned by the canonical hosted recommender is the source of truth. Any AI comparison is constrained to that shortlist and is not permitted to introduce outside APIs.

search_apis

Use when the task is explicit enough to filter the registry directly.

Example:

json
{  "categories": ["Geospatial"],  "agent_capabilities": ["locate"],  "mcp": "Yes",  "min_readiness": 60,  "limit": 10}

Only explicit stored values are used. For example, mcp: "Yes" must not be satisfied by an API whose MCP status is Unknown.

search_capabilities

Use when the task is better expressed as an exact capability or execution requirement than as a broad API category.

Capability results can include execution, access, approval, hardware, sandbox, physical-consequence and reversibility fields when available from the canonical evidence graph.

verify_requirement

Use when you need to adjudicate one API against one exact requirement.

Example:

json
{  "api": "here-location-services",  "requirement": "truck routing in Sweden"}

The verifier returns an evidence-backed YES, NO, PARTIAL or UNKNOWN result. Unknown is never silently promoted into support.

get_api

Example:

json
{  "identifier": "geoapify"}

The response is the canonical RealWorldAPIs decision profile, including provider metadata, capabilities, authentication, protocol status and evidence links.

compare_apis

Example:

json
{  "apis": ["Geoapify", "Google Maps Platform", "Mapbox"],  "fields": [    "category",    "capabilities",    "agent_capabilities",    "auth_methods",    "openapi",    "mcp",    "agent_readiness_score"  ]}

The comparison is deterministic. RealWorldAPIs does not infer missing support or silently replace Unknown with a guess.

Connect

Any MCP client that supports remote Streamable HTTP can connect to:

text
https://mcp.realworldapis.com/mcp

Example MCP configuration:

json
{  "mcpServers": {    "realworldapis": {      "url": "https://mcp.realworldapis.com/mcp"    }  }}

Codex-style TOML

toml
[mcp_servers.realworldapis]url = "https://mcp.realworldapis.com/mcp"

Evidence model

RealWorldAPIs is designed around a normalized evidence layer rather than free-form API recommendations.

The canonical dataset tracks fields such as:

  • provider and category
  • verified task-level capabilities
  • agent action primitives
  • authentication methods
  • OpenAPI
  • MCP
  • A2A
  • x402
  • llms.txt
  • public documentation status
  • authentication verification
  • access and setup requirements
  • approval requirements
  • hardware dependencies
  • sandbox availability
  • physical consequence
  • irreversibility
  • direct evidence links

Grounding rules

  1. The canonical structured dataset is the source of truth.
  2. Unknown is distinct from No.
  3. AI comparison may not introduce APIs outside the structured shortlist.
  4. Search and comparison tools use canonical stored evidence.
  5. Hard provider facts require the appropriate evidence quality under the hosted RealWorldAPIs evidence policy.
  6. Provider documentation should be checked before production integration.

Why RealWorldAPIs exists

General API directories are useful for broad discovery, but AI agents increasingly need a normalized way to answer questions such as:

  • Which exact API capability can perform this task?
  • Which APIs can locate a vehicle and route a technician?
  • Which weather APIs are both agent-actionable and machine-readable?
  • Which robotics APIs expose machine-usable interfaces?
  • Which APIs can be combined for a physical-world workflow?
  • What access, approval, hardware or safety constraints apply?
  • What evidence gaps remain before an agent should rely on a provider?

RealWorldAPIs focuses specifically on APIs that connect software and agents to the physical world.

Research and dataset

The public research page, State of Agent-Ready Real-World APIs 2026, summarizes the catalog snapshot across agent-readiness, protocol support and category-level evidence:

https://realworldapis.com/research/state-of-agent-ready-apis-2026

Machine-readable research data:

https://realworldapis.com/research/state-of-agent-ready-apis-2026.json

The public catalog is available at:

https://realworldapis.com/api/catalog.json

The verified capability graph is available at:

https://realworldapis.com/api/capabilities.json

Architecture

The MCP server is intentionally an interface layer over canonical hosted RealWorldAPIs services.

text
MCP client   |   +-- find_api_for_task --------> hosted canonical recommender   +-- verify_requirement --------> hosted evidence/verifier layer   +-- search_capabilities -------> canonical capability graph   +-- search_apis ---------------> canonical API catalog   +-- get_api -------------------> canonical API decision profile   +-- compare_apis --------------> canonical evidence fields

This avoids maintaining a second independent recommendation, ranking or evidence system inside the public MCP interface.

The implementation of the canonical hosted recommendation, ranking, verification and evidence-processing systems is proprietary and is not contained in this repository.

See docs/ARCHITECTURE.md for additional public interface details.

Repository layout

text
src/  worker.js               Publicly inspectable MCP interface implementation
examples/  requests.md             Example tool calls and expected semantics
docs/  ARCHITECTURE.md         Public data flow, grounding and failure behavior  DATA_MODEL.md           Important catalog fields and evidence semantics
CHANGELOG.md               Release historyCONTRIBUTING.md            Contribution and correction workflowPROPRIETARY_NOTICE.md      Repository rights and proprietary-service boundary

Status

The hosted RealWorldAPIs service evolves independently from this public repository.

For current production behavior, use the live MCP endpoint and canonical website endpoints. Third-party directories and this repository may temporarily show an older tool count, description, implementation detail or schema after a production deployment.

Contributing

Corrections to public API metadata are welcome when supported by current provider documentation. Please include the evidence URL and identify the exact field that should change.

Public contributions do not grant access to, or imply publication of, proprietary RealWorldAPIs ranking, verification, optimization, benchmark or research systems.

See CONTRIBUTING.md.

Rights and licensing

This repository is publicly source-visible but is not released under an open-source license.

No MIT, Apache, BSD, GPL or other open-source license is granted by this repository.

Use of the hosted RealWorldAPIs service is separate from rights in repository source code. Public protocol descriptions, schemas and factual outputs may be used for interoperability as permitted by applicable terms and law, but the proprietary hosted RealWorldAPIs systems are not licensed through this repository.

See PROPRIETARY_NOTICE.md.

Copyright © 2026 RealWorldAPIs. All rights reserved.

來源:README.md,提交 39545da

工具

6

find_api_for_task

Find API for Task

Find the best APIs for a natural-language physical-world task. Use this for open-ended task or outcome-based recommendations; use search_apis when you already know exact filters, get_api for one known API, or compare_apis for known candidates. Returns an authoritative canonical structured_results shortlist plus a best-effort AI comparison restricted to that shortlist. The canonical engine now applies Verified/High capability evidence before ranking: explicit hard requirements expressed with terms such as must, required, no or without can exclude a candidate only when verified evidence contradicts them. Unknown evidence remains eligible and is surfaced as uncertainty. requirements.mcp, openapi and auth are translated into explicit requirements; categories and capabilities remain task-shaping hints. free_tier and min_readiness are accepted for compatibility but are not hard filters.

輸入參數task必填limitrequirements

search_apis

Search APIs

Search the canonical registry when you already know keywords or exact requirements. All supplied filter groups combine with AND logic; array capability filters require every supplied value, and protocol fields match the exact Yes/No/Unknown status, so Unknown never satisfies Yes. Results include each API's capability_summary and direct decision-profile URL. Results are sorted by agent_readiness_score descending and then API name, with 1–20 results returned (default 10). Use search_capabilities when you need capability-level filters such as approval, physical consequence, sandbox or access friction.

輸入參數a2amcpx402limitqueryopenapillms_txtcategoriescapabilitiesmin_readinessagent_actionableagent_capabilities

search_capabilities

Search Verified Capabilities

Search the canonical 770-capability Verified/High decision graph directly. Use this when the question is about what an agent can actually do and under what execution conditions: task/capability text, API, category, action type, acted-on object, human approval, physical consequence, irreversibility, sandbox, hardware dependency, access friction or setup. All supplied filter groups combine with AND logic; multiple values inside one array filter are alternatives (OR). Unknown remains Unknown and may be filtered explicitly by passing the literal value Unknown.

輸入參數limitqueryobjectsandboxapi_slugscategoriesprimitivesagent_setupaction_typesactionabilityhuman_approvalaccess_friction另有 3 個

verify_requirement

Verify API Requirement

Verify whether one specific API can satisfy one exact requirement using the canonical Verified/High capability graph plus linked assertion evidence. Returns YES, NO, PARTIAL or UNKNOWN with matched capability, access/setup prerequisites, approval, safety, reversibility, reliability, coverage and other requested dimensions; direct provider facts remain distinct from inferred policy assertions. Unknown remains Unknown.

輸入參數api必填locationrequirement必填

get_api

Get API

Retrieve one canonical API decision profile when you already know its name or slug. The response includes the full Verified/High capability profile with execution, access, approval, reversibility, physical-consequence and evidence fields. Exact slug or exact API name is preferred; a partial identifier is accepted only when it uniquely resolves to one record.

輸入參數identifier必填

compare_apis

Compare APIs

Compare 2–8 already-known APIs side by side using canonical catalog fields plus capability-graph summary fields such as verified capability count, physical consequences, approval relevance and irreversibility. Use get_api for the full decision profile of one API and search_capabilities for capability-level filtering. No missing value is inferred and Unknown remains Unknown.

輸入參數apis必填fields

版本歷史

2
  1. v3.0.1最新Sep 27, 2026
  2. v1.2.1Sep 16, 2026

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