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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

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.

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:

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",

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

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

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

Weekly Installs

280

Repository

qverisai/open-q…s-skills

GitHub Stars

10

First Seen

Feb 27, 2026

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