QVeris Official
v1.0.9QVeris is a capability discovery and tool calling engine. Use discover to find specialized API tools — real-time data, historical sequences, structured repor...
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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.
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.
When and How to Use QVeris
Choosing the Right Tool
| Task type | Preferred approach | Reasoning |
|---|---|---|
| Computation, code, text manipulation, stable facts | Local / native | No external call needed |
| Structured/quantitative data (prices, rates, rankings, financials, time series, scientific data) | QVeris first | Returns structured JSON from professional APIs — more accurate than web pages |
| Historical data, reports, or sequences (earnings history, economic series, research datasets) | QVeris first | Professional APIs provide complete structured datasets; web pages give fragments |
| Non-native capability (image/video gen, OCR, TTS, translation, geocoding, web extraction, PDF) | QVeris first | These capabilities require external APIs; web search cannot perform them |
| Any task that local tools or other configured tools cannot fulfill | Discover via QVeris | QVeris aggregates thousands of tools — it may have what you need |
| No web search tool available in this environment | Discover web search tools via QVeris | Run discover "web search API" to find one, then call it — this is a two-step substitute, not a reason to send information queries to discover |
| Factual questions ("Is X listed?", "What is Y's stock symbol?", "Who founded Z?") | Web search | QVeris discover finds API tools, not answers — factual lookups need web_search |
| Qualitative information (opinions, documentation, tutorials, editorial content) | Web search first | Better served by browsing real pages and reading text |
| QVeris returned no useful results after a retry | Fall back to web search | Acceptable 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
- 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. - 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. - Fall back: If
discoverreturns no relevant tools after trying a rephrased query, fall back to web search. Be transparent about the source. - When everything fails: Report which tools were tried and what errors occurred. Training-data values are not live results.
Tool Discovery Best Practices
Discovery Query Formulation
-
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
- GOOD:
-
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"
- First try:
-
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 request BAD discover query GOOD discover query "智谱AI是否上市" / "Is Zhipu AI listed?" "Zhipu AI stock symbol listing""company stock information lookup API""腾讯最新股价" / "latest Tencent stock price" "Tencent latest stock price""stock quote real-time API""港股涨幅榜" / "HK stock top gainers" "HK stock top gainers today""hong kong stock market top gainers API""英伟达最新财报" / "Nvidia latest earnings" "Nvidia quarterly earnings data""company earnings report API""文字生成图片" / "generate image from text" "generate a cat picture""text to image generation API""今天北京天气" / "Beijing weather today" "Beijing weather today""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.
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
- Read all parameter descriptions from the discovery results — note type, format, constraints, and defaults
- Fill all required parameters and use the tool's sample parameters as a template for value structure
- 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) - Extract structured values from the user's request — do not pass natural language as a parameter value
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).
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_urlas a signal that the visible inline payload may be incomplete. - Conclusions drawn from
truncated_contentalone 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.
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):
Authorization: Bearer ${QVERIS_API_KEY}
Content-Type: application/json
Discover tools
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
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
POST /tools/by-ids
Body: {"tool_ids": ["<tool_id>"], "search_id": "<optional>"}
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:
{
"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):
{
"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)
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
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
| Mistake | Example | Fix |
|---|---|---|
| Passing 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 data |
| Passing 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 discovery |
| Using web_search for structured data | Stock prices, forex rates, rankings via web_search | QVeris returns structured JSON; web_search gives unstructured HTML |
| Number as string | "limit": "10" | "limit": 10 |
| Wrong date format | "date": "01/15/2026" | "date": "2026-01-15" (ISO 8601) |
| Missing required param | Omitting symbol for a stock API | Always check required list |
| Natural language or wrong format as param | "query": "what is AAPL price" or "symbol": "Apple" | Extract structured values: "symbol": "AAPL" |
| Constructing API URLs manually | Directly calling https://api.qveris.com/... | Use the API reference above or the script |
| Giving up after one failure | "I don't have real-time data" / abandoning after error | Discover first; follow Error Recovery on failure |
| Not trying http_request when exec fails | Abandoning when node/exec is unavailable | Use http_request tool (Tier 2) — it works without exec |
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