mcp-hkex-filing

describe_schema

Read-onlyIdempotent

Retrieve the canonical schema for HKEX filings to understand available fields and filters. Specify a section to get only relevant details before querying.

Instructions

Use this before filtering or interpreting results to learn the canonical fields.

Use search_filings or search_documents to query filings once you know the field names. Pass section to return just one section (saves tokens); default returns everything: filing and document fields, the known filing types/categories/statuses/types, graph edge kinds, and the supported query filters. Reads no filings.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`section`	No	Narrow to one section: filing, document, types, query, or all (default).	all

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.1.3Sep 24, 2026

addedInput schema / properties / section

Added value: +{
+  "default": "all",
+  "description": "Narrow to one section: filing, document, types, query, or all (default).",
+  "enum": [
+    "all",
+    "filing",
+    "document",
+    "types",
+    "query"
+  ],
+  "title": "Section",
+  "type": "string"
+}

First observedSep 23, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: it reads no filings, defaults to returning all schema sections, and can save tokens when narrowed with section.

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

Conciseness5/5

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

The description is compact and front-loaded with the key guidance. Each sentence earns its place: when to use, what to use instead, how to narrow output, and what the default returns. No filler or redundancy.

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?

For a read-only schema-introspection tool with one optional parameter and an output schema, this description is complete. It covers use timing, sibling routing, parameter behavior, and side-effect profile without needing to document return values.

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

Parameters4/5

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

Schema coverage is 100%, so the parameter is already fully documented. The description adds value by explaining the token-saving rationale for passing section and enumerating what the default return includes, going beyond the schema's enum definition.

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 states a specific purpose: learn the canonical fields before filtering or interpreting results. It names the resource (the schema) and clearly distinguishes the tool from query siblings like search_filings and search_documents.

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?

It explicitly says when to use the tool (before filtering/interpreting results) and points to alternatives for querying once field names are known. This gives an agent a concrete decision rule rather than leaving usage to inference.

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

Deploy Server