OneQAZ Trading Intelligence

fetch

Read-onlyIdempotent

Purpose: ChatGPT-connector-standard document fetch by id from search results. Namespaces: tool:{name} returns the tool's full documentation and how to call it; resource:{uri} returns the resource's live data (core resources resolved server-side — also the bridge for clients without MCP resource support, e.g. Gemini); signal:{market}:{symbol} returns the symbol's latest combined research signal. Triggers: ChatGPT connectors / Deep Research call this after search. Clients without MCP resource support can call it directly with a known resource id, e.g. fetch("resource:market://global/summary"). When to call: whenever the full content behind a search result id is needed. Prerequisites: a valid id — from search results or a known namespace id. Next steps: for tool docs, call the named tool via tools/call; for signals, get_signal_detail / explain_decision for deeper evidence. Caveats: uncovered resource uris return description-only text (no fabricated data). text is a JSON document for resource/signal ids. Output: {id, title, text, url, metadata, disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes	document id — "tool:{name}", "resource:{uri}", or "signal:{market}:{symbol}" (market: crypto / kr_stock / us_stock)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	No
`url`	No
`text`	No
`title`	No
`metadata`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 23, 2026

addedOutput schema / description

Added value: +"`full_data` for fetch — 실응답에서 추출(2026-09-23)."

addedOutput schema / properties

Added value: +{
+  "id": {
+    "anyOf": [
+      {
+        "type": "string"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "default": null
+  },
+  "metadata": {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  "text": {
+    "anyOf": [
+      {
+        "type": "string"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "default": null
+  },
+  "title": {
+    "anyOf": [
+      {
+        "type": "string"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "default": null
+  },
+  "url": {
+    "anyOf": [
+      {
+        "type": "string"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "default": null
+  }
+}

Changed1 schema field changedSep 22, 2026

addedInput schema / properties / id / description

Added value: +"document id — \"tool:{name}\", \"resource:{uri}\", or \"signal:{market}:{symbol}\" (market: crypto / kr_stock / us_stock)"

AddedJul 7, 2026

TDQS

A4.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, but the description goes beyond them. It discloses that uncovered resource URIs return 'description-only text (no fabricated data)' – a critical honesty guarantee. It also states that `text` is a JSON document for resource/signal ids, and describes the output envelope as 'flat envelope, OpenAI fixed shape.' These details add behavioral context that an agent would otherwise not know and that is not derivable from annotations alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than most but is exceptionally well-structured with clear section labels (Purpose, Namespaces, Triggers, When to call, Prerequisites, Next steps, Caveats, Output). Every section earns its place – each addresses a different agent concern. The purpose is front-loaded, and the information density is high. It is not bloated; it is comprehensive for a tool with three distinct resource types. The only reason it doesn't earn a 5 is that a few sentences could be tightened, but the structure is exemplary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (three namespaces, multiple triggers, integration with other tools), the description is remarkably complete. It covers the input format, prerequisites, caveats, and even next steps for follow-up actions. It references the output envelope but does not duplicate the output schema since that exists separately. For an agent to call this tool correctly, everything needed – from valid id construction to error handling (description-only text) – is present. No gaps are apparent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers the id parameter 100% and gives a succinct description with namespace patterns. The tool description enriches this substantially by explaining each namespace in depth: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns live data and serves as a bridge for clients without MCP support; `signal:{market}:{symbol}` returns the latest combined research signal. It also gives concrete examples like 'fetch("resource:market://global/summary")'. This goes well beyond the schema and fully compensates for any ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a precise purpose: 'ChatGPT-connector-standard document fetch by id from `search` results.' It specifies the verb (fetch), the resource (document by id), and the source (search results). It also clearly enumerates the three supported namespaces (tool, resource, signal), which distinguishes it from the many sibling get_* tools that are single-purpose. An agent can immediately understand what this tool does and how it differs from a search or a detail tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage guidance is explicit and prescriptive. It states that ChatGPT connectors/Deep Research call this after search, that clients without MCP resource support can call it directly with a known resource id, and 'When to call: whenever the full content behind a search result id is needed.' It also provides 'Next steps' that route the agent to alternative tools (get_signal_detail / explain_decision) when deeper evidence is required, effectively telling the agent when NOT to use this tool. This is a model of usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All OneQAZ Trading Intelligence tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.