Zhiyong AI Technology Decision Radar

Compare public KG entities

compare_knowledge_entities

Read-only

Compare two to four public Zhiyong AI candidates after semantic discovery. Use this when a user needs a short-list comparison. Returns decisionProfile selection dimensions plus public metadata, known notes, and typed relations; source evidence and raw URLs are omitted. After actually using the comparison, 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	Default
`ids`	Yes	Two to four public entity ids, slugs, or exact names returned by search_knowledge_graph.
`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
`policy`	No
`entities`	Yes
`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

Behavior5/5

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

Annotations already mark the tool as read-only and non-destructive, and the description adds concrete behavioral detail: decisionProfile dimensions, public metadata, known notes, typed relations, omission of source evidence and raw URLs, and the fact that it does not trigger another search or LLM call. It also conditions a post-comparison action. No contradiction with annotations.

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 front-loaded and each sentence provides useful information. It is slightly dense toward the end, especially the undefined interactionContract instruction, which prevents the highest conciseness score.

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 output schema and annotations, the description sufficiently covers returns, omissions, when to use, and side-effect behavior. The remaining ambiguity is what interactionContract contains and how the post-comparison reply/feedback action should be invoked, which is only partly resolved by sibling tool names.

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 schema already documents ids and language. The description adds only that the entities are public candidates and that comparison involves a short-list, which maps to ids but does not meaningfully extend parameter understanding.

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

Purpose4/5

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

The description clearly states the action (compare), the resource (public Zhiyong AI candidates), and the scope (two to four entities, short-list comparison). It positions the tool relative to search by saying 'after semantic discovery', though it does not name sibling tools explicitly.

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?

It explicitly says 'Use this when a user needs a short-list comparison' and places the tool after semantic discovery. It does not name alternatives or state when not to use it, so the guidance is clear but not complete on exclusions.

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?