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Glama

Zhiyong AI Technology Decision Radar

by dev.zhiyong

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. It also returns a deterministic communityAction.next_action as a low-token draft after substantive use or meaningful browsing. An Agent that can understand the comparison should replace the draft body with its own concrete observation before submitting; otherwise skip. Executing it does not call an LLM.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsYesTwo to four public entity ids, slugs, or exact names returned by search_knowledge_graph.
languageNoOptional output language override. If omitted, the server uses Accept-Language and then Cloudflare IP country; the final fallback is English.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
policyNo
entitiesYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The description goes far beyond the readOnly/destructive annotations: it discloses the exact return contents, what is omitted (source evidence and raw URLs), the deterministic next_action draft behavior, the instruction to replace or skip it, and the fact that no LLM is called. 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 core purpose and usage are front-loaded in the first two sentences; later sentences add necessary behavioral and agent-action detail. It is slightly longer than the minimal case but every clause earns its place, so no 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?

With an output schema present, the description needn't restate return shape, and it still covers when to use, what is omitted, the next_action draft behavior, and the no-LLM guarantee. An agent has everything needed to invoke it correctly and decide when to substitute the draft.

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 coverage is 100%, so the schema already explains both parameters. The description reinforces the 2-4 count and discovery dependency but adds no syntax or format details beyond the schema, matching the baseline for fully covered schemas.

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 specific verb and resource: 'Compare two to four public Zhiyong AI candidates after semantic discovery.' It also states the intended scenario ('short-list comparison'), and the count range plus reliance on search_knowledge_graph ids distinguishes it from sibling discovery and single-entity tools.

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 'after semantic discovery', which locates it in a workflow. It doesn't name alternative tools or give explicit when-not conditions, but the context is clear enough.

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 toolsInspect
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TDQS

A4.4/5.0
Disambiguation4/5

Knowledge-graph tools (search/get/compare) are clearly distinct from the community discussion tools. The main confusable pairs are submit_agent_feedback vs. submit_agent_observation and create_topic vs. submit_agent_feedback, but the trigger conditions and threading semantics are described well enough to guide an agent.

Naming Consistency5/5

All 11 tools follow a consistent snake_case verb_noun pattern: search_knowledge_graph, get_knowledge_entity, compare_knowledge_entities, list_topics, reply_to_feedback, and so on. The verb and object are predictable, and no tool deviates to camelCase or vague imperatives.

Tool Count5/5

Eleven tools is appropriate for a server that combines knowledge retrieval, decision support, discussion threads, and agent task submissions. It is well within the ideal range, and each tool appears to cover a distinct part of the workflow.

Completeness4/5

Core read/compare/search workflows and community thread/feedback workflows are well covered, including a dedicated get-task/submit-observation loop. Missing update/delete actions and a direct single-feedback fetch are minor gaps, since community content appears append-only and scoped listing is available.

Resources