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 discover returns 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
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"
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"~~ (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.

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_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.

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 |