Zhiyong AI Technology Decision Radar

Get public KG entity

get_knowledge_entity

Read-only

Read one public entity from Zhiyong AI's decision radar by stable id or slug. Returns known metadata, decisionProfile selection dimensions, notes, and typed graph relations; source evidence and raw URLs are omitted. After actually using the entity detail, inspect interactionContract and, only when there is a concrete observation and public writes are allowed, complete one reply or feedback action. It does not trigger another search or LLM call; otherwise skip.

Input Schema

TableJSON Schema

Name	Required	Description
`id`	No	The KG entity id, when known.
`slug`	No	The public entity slug, when known.
`language`	No	Optional output language override. If omitted, the public default is Simplified Chinese; use en explicitly for English.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes
`name`	Yes
`slug`	Yes
`relations`	No
`canonicalUrl`	No
`interactionContract`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 9, 2026
- addedOutput schema / properties / interactionContract
```
Added value: +{
+  "additionalProperties": true,
+  "type": "object"
+}
```

Changed1 schema field changedSep 7, 2026

changedInput schema / properties / language / description

Previous value: -"Optional output language override. If omitted, the server uses Accept-Language and then Cloudflare IP country; the final fallback is English."New value: +"Optional output language override. If omitted, the public default is Simplified Chinese; use en explicitly for English."

First observedSep 6, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Beyond the readOnlyHint=true annotation, the description is transparent: it lists what is returned, what is omitted ('source evidence and raw URLs are omitted'), and claims it triggers no side effects. This adds genuine behavioral context to safe hook, such as the fact that it makes no deeper search/LLM call, which an agent can't infer 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 first sentence is front-loaded and complete; the second adds workflow guidance; the third is short. Though the interactionContract sentence is dense and adds extra steps, no sentence is padding. It is fit for a read tool with non-trivial follow-up semantics.

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

Completeness4/5

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

Given the presence of anoutput schema, the description does not need to specify return fields, but it supplies important context: response-field boundaries (omitted evidence/URLs), language override, and business rules around when to write. It is complete enough for an agent to correctly decide to use and interpretation this tool.

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

Parameters3/5

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

Schema description coverage is 100%, so the structured data already fully documents id, slug, and language. The description adds the phrase 'by stable id or slug,' which is redundant rather than new meaning. With the schema already carrying the parameter details, the baseline of 3 is appropriate.

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 uses a precise verb ('read'), names the resource ('one public entity from Zhiyong AI's decision radar'), and lists the return contents via stable id/slug. This clearly distinguishes it from the search-oriented sibling search_knowledge_graph, which targets arbitrary querying rather than single-entity retrieval.

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

Usage Guidelines4/5

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

The description states clear context: use it to read a specific entity and, after doing so, conditionally perform a reply/feedback action. It also clarifies what it does not do ('does not trigger another search or LLM call') and instructs skipping otherwise. However, it never names comparator siblings like search_knowledge_graph or compare_knowledge_entities, leaving the when-to-use-vs-alternatives routing implicit rather than explicit.

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

All Zhiyong AI Technology Decision Radar 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.

Resources

Need Help?