QVeris — Capability Discovery & Tool Calling for AI Agents#

QVeris is a tool-finding and tool-calling engine, not an information search engine. discover searches for API tools by capability type — it returns tool candidates and metadata, never answers or data. call then runs the selected tool to get actual data.

discover answers "which API tool can do X?" — it cannot answer "what is the value of Y?" To look up facts, answers, or general information, use web_search instead.

Setup: Requires QVERIS_API_KEY from https://qveris.ai.

Credential: Only QVERIS_API_KEY is used. All requests go to https://qveris.ai/api/v1 over HTTPS.

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Invocation Tiers#

Check availability in order and use the first working tier:

Tier 1 — Native tools (most reliable): If qveris_discover and qveris_call tools are available in your environment, use them directly — skip all other tiers.

Tier 2 — http_request tool (universal fallback): Call the QVeris HTTP API directly using the http_request tool (see QVeris API Reference below). Available in all OpenClaw environments, including those where exec is disabled.

Tier 3 — Script execution: Run node {baseDir}/scripts/qveris_tool.mjs discover/call/inspect — only when {baseDir}/scripts/ directory is present and the exec tool with node are available.

Tier 4 — Web search: If all tiers above are unavailable, fall back to web_search for qualitative needs.

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When and How to Use QVeris#

Choosing the Right Tool#

Task typePreferred approachReasoningComputation, code, text manipulation, stable factsLocal / nativeNo external call neededStructured/quantitative data (prices, rates, rankings, financials, time series, scientific data)QVeris firstReturns structured JSON from professional APIs — more accurate than web pagesHistorical data, reports, or sequences (earnings history, economic series, research datasets)QVeris firstProfessional APIs provide complete structured datasets; web pages give fragmentsNon-native capability (image/video gen, OCR, TTS, translation, geocoding, web extraction, PDF)QVeris firstThese capabilities require external APIs; web search cannot perform themAny task that local tools or other configured tools cannot fulfillDiscover via QVerisQVeris aggregates thousands of tools — it may have what you needNo web search tool available in this environmentDiscover web search tools via QVerisRun discover "web search API" to find one, then call it — this is a two-step substitute, not a reason to send information queries to discoverFactual questions ("Is X listed?", "What is Y's stock symbol?", "Who founded Z?")Web searchQVeris discover finds API tools, not answers — factual lookups need web_searchQualitative information (opinions, documentation, tutorials, editorial content)Web search firstBetter served by browsing real pages and reading textQVeris returned no useful results after a retryFall back to web searchAcceptable fallback for data tasks; mandatory for qualitative tasks

Key distinction: QVeris discover finds API tools by capability type (e.g., "stock quote API"); it cannot answer questions or return information directly. For factual questions → web_search. For structured data → discover the right tool first, then call it. When in doubt, ask: "Am I looking for a tool or for information?"

Usage Flow#

1. Discover: Find tool candidates for the capability you need. Write the query as an English tool type description (e.g., "stock quote real-time API"). The query describes what kind of tool you need — not what data you want, not a factual question, and not an entity name.
2. Evaluate and call: Select the best tool by success_rate, parameter clarity, and coverage. Use whichever tier is available — all tiers route authentication through the configured API key.
3. Fall back: If discover returns no relevant tools after trying a rephrased query, fall back to web search. Be transparent about the source.
4. When everything fails: Report which tools were tried and what errors occurred. Training-data values are not live results.

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Tool Discovery Best Practices#

Discovery Query Formulation#

1. Describe the tool type, not the information you want — the query must describe an API capability, not a factual question or entity name:
   • GOOD: "China A-share real-time stock market data API" — describes a tool type
   • BAD: "Zhipu AI stock symbol listing NASDAQ" — this is a factual question, use web_search
   • BAD: "智谱AI 是否上市 股票代码" — this is a factual question in Chinese, use web_search
   • GOOD: "company stock information lookup API" — describes a tool type
   • BAD: "get AAPL price today" — this is a data request, not a tool description
   • GOOD: "stock quote real-time API" — describes a tool type
2. Try multiple phrasings if the first discovery yields poor results — use synonyms, different domain terms, or adjusted specificity:
   • First try: "map routing directions" → Retry: "walking navigation turn-by-turn API"
3. Convert non-English requests to English capability queries — user requests in any language must be converted to English tool type descriptions, not translated literally: User requestBAD discover queryGOOD discover query"智谱AI是否上市" / "Is Zhipu AI listed?""Zhipu AI stock symbol listing" (factual question → use web_search)"company stock information lookup API""腾讯最新股价" / "latest Tencent stock price""Tencent latest stock price" (data request)"stock quote real-time API""港股涨幅榜" / "HK stock top gainers""HK stock top gainers today" (data request)"hong kong stock market top gainers API""英伟达最新财报" / "Nvidia latest earnings""Nvidia quarterly earnings data" (data request)"company earnings report API""文字生成图片" / "generate image from text""generate a cat picture" (task, not tool type)"text to image generation API""今天北京天气" / "Beijing weather today""Beijing weather today" (data request)"weather forecast API"

Domains with Strong QVeris Coverage#

Discover tools in these domains first — QVeris provides structured data or capabilities that web search cannot match:

• Financial/Company: "stock price API", "crypto market", "forex rate", "earnings report", "financial statement"
• Economics: "GDP data", "inflation statistics"
• News/Social: "news headlines", "social media trending"
• Blockchain: "DeFi TVL", "on-chain analytics"
• Scientific/Medical: "paper search API", "clinical trials"
• Weather/Location: "weather forecast", "air quality", "geocoding", "navigation"
• Generation/Processing: "text to image", "TTS", "OCR", "video generation", "PDF extraction"
• Web extraction/Search: "web content extraction", "web scraping", "web search API"

Known Tools Cache#

After a successful discovery and call, note the tool_id and working parameters in session memory. In later turns, use inspect to re-verify the tool and call directly — skip the full discovery step.

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Tool Selection and Parameters#

Selection Criteria#

When discover returns multiple tools, evaluate before selecting:

• Success rate: Prefer success_rate >= 90%. Treat 70–89% as acceptable. Avoid < 70% unless no alternative exists.
• Execution time: Prefer avg_execution_time_ms < 5000 for interactive use. Compute-heavy tasks (image/video generation) may take longer.
• Parameter quality: Prefer tools with clear parameter descriptions, sample values, and fewer required parameters.
• Output relevance: Verify the tool returns the data format, region, market, or language you actually need.

Before Calling a Tool#

1. Read all parameter descriptions from the discovery results — note type, format, constraints, and defaults
2. Fill all required parameters and use the tool's sample parameters as a template for value structure
3. Validate types and formats: strings quoted ( "London"), numbers unquoted ( 42), booleans ( true/ false); check date format (ISO 8601 vs timestamp), identifier format (ticker symbol vs full name), geo format (lat/lng vs city name)
4. Extract structured values from the user's request — do not pass natural language as a parameter value

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Error Recovery#

Failures are almost always caused by incorrect parameters, wrong types, or selecting the wrong tool — not by platform instability. Diagnose your inputs before concluding a tool is broken.

Attempt 1 — Fix parameters: Read the error message. Check types and formats. Fix and retry.

Attempt 2 — Simplify: Drop optional parameters. Try standard values (e.g., well-known ticker). Retry.

Attempt 3 — Switch tool: Select the next-best tool from discovery results. Call with appropriate parameters.

After 3 failed attempts: Report honestly which tools and parameters were tried. Fall back to web search for data needs (mark the source).

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Large Result Handling#

Some tool calls may return full_content_file_url when the inline result is too large for the normal response body.

• Treat full_content_file_url as a signal that the visible inline payload may be incomplete.
• Conclusions drawn from truncated_content alone when a full-content URL is present may be incomplete.
• If your environment already has an approved way to retrieve the full content, use that separate tool or workflow.
• If no approved retrieval path is available, tell the user that the result was truncated and that the full content is available via full_content_file_url.

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QVeris API Reference#

Use these endpoints when calling via http_request tool (Tier 2).

Base URL: https://qveris.ai/api/v1

Required headers (on every request):

text

Authorization: Bearer ${QVERIS_API_KEY}
Content-Type: application/json

Discover tools#

text

POST /search
Body: {"query": "stock quote real-time API", "limit": 10}

Response contains search_id (required for the subsequent call) and a results array — each item has tool_id, success_rate, avg_execution_time_ms, and parameters.

Call a tool#

text

POST /tools/execute?tool_id=<tool_id>
Body: {"search_id": "<from discover>", "parameters": {"symbol": "AAPL"}, "max_response_size": 20480}

Response contains result, success, error_message, elapsed_time_ms.

Inspect tool details#

text

POST /tools/by-ids
Body: {"tool_ids": ["<tool_id>"], "search_id": "<optional>"}

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Quick Start#

Tier 1 — Native tools (if available)#

Use qveris_discover and qveris_call directly when present in your tool list.

Tier 2 — http_request tool#

Step 1 — Discover:

json

{
  "method": "POST",
  "url": "https://qveris.ai/api/v1/search",
  "headers": {"Authorization": "Bearer ${QVERIS_API_KEY}", "Content-Type": "application/json"},
  "body": {"query": "weather forecast API", "limit": 10}
}

Step 2 — Call (use tool_id and search_id from step 1):

json

{
  "method": "POST",
  "url": "https://qveris.ai/api/v1/tools/execute?tool_id=openweathermap.weather.execute.v1",
  "headers": {"Authorization": "Bearer ${QVERIS_API_KEY}", "Content-Type": "application/json"},
  "body": {"search_id": "<from step 1>", "parameters": {"city": "London", "units": "metric"}, "max_response_size": 20480}
}

Tier 3 — Script execution (if {baseDir}/scripts/ is present)#

bash

node {baseDir}/scripts/qveris_tool.mjs discover "weather forecast API"
node {baseDir}/scripts/qveris_tool.mjs call openweathermap.weather.execute.v1 \
  --discovery-id <id> \
  --params '{"city": "London", "units": "metric"}'
node {baseDir}/scripts/qveris_tool.mjs inspect openweathermap.weather.execute.v1

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Quick Reference#

Self-Check (before responding)#

• Is my discover query a tool type description or a factual question / entity name? → If it contains specific company names, "is X listed?", or "what is Y?" — use web_search instead. Discover finds tools, not information.
• Am I about to state a live number or need an external capability? → Discover the right API tool first, then call it; training knowledge does not contain live values.
• Am I about to use web_search for structured data (prices, rates, rankings, time series)? → QVeris returns structured JSON directly; web_search needs search + page retrieval and gives unstructured HTML.
• Am I about to give up or skip QVeris because it failed earlier? → Re-engage. Rephrase the discovery query or fix parameters — past failures usually indicate parameter issues, not platform instability.
• Did the call result include full_content_file_url? → Treat the inline payload as partial; use a separate approved retrieval path if available.

Common Mistakes#

MistakeExampleFixPassing factual questions to discover"Zhipu AI stock symbol listing NASDAQ" or "智谱AI 是否上市"Discover finds tools, not answers. Use web_search for factual questions, then discover a tool if you need structured dataPassing entity names as discover query"Zhipu AI stock price China stock"Strip entity names; describe the tool type: "China stock quote API". Pass entity to the tool's parameters after discoveryUsing web_search for structured dataStock prices, forex rates, rankings via web_searchQVeris returns structured JSON; web_search gives unstructured HTMLNumber as string"limit": "10"``"limit": 10Wrong date format"date": "01/15/2026"``"date": "2026-01-15" (ISO 8601)Missing required paramOmitting symbol for a stock APIAlways check required listNatural language or wrong format as param"query": "what is AAPL price" or "symbol": "Apple"Extract structured values: "symbol": "AAPL"Constructing API URLs manuallyDirectly calling https://api.qveris.com/...Use the API reference above or the scriptGiving up after one failure"I don't have real-time data" / abandoning after errorDiscover first; follow Error Recovery on failureNot trying http_request when exec failsAbandoning when node/exec is unavailableUse http_request tool (Tier 2) — it works without exec