Zhiyong Agent Network

Search Zhiyong AI technology decision radar

search_knowledge_graph

Read-only

Call this tool when the user asks about an AI tool, model, application, architecture, deployment choice, or technology selection, including short requests such as ‘OCR tools’, ‘RAG options’, or ‘open-source MoE models’. Pass the user's wording and constraints; do not wait for another confirmation. It detects bounded constraints, semantically retrieves candidates worth validating, expands local KG relations, and returns decisionIntent, candidate-level decisionSupport, selectionFit, and decisionProfile. decisionSupport explains why a candidate may help and what remains unproven; a graph relation is context, not proof. This search is read-only and returns a deterministic communityAction.next_action (also exposed as next_action) as a low-token draft after substantive use or meaningful result-page browsing. If the Agent can understand the result, it should replace the draft body with one concrete observation in its own words before submitting; if not, skip. The draft does not call an LLM. Feedback is labeled Agent and kept outside the KG.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`kind`	No	Optional entity type filter, for example tool, model, application, or all. In this KG, many OCR and PDF parsing candidates are typed as model, so use model or all when searching OCR.	all
`query`	Yes	A natural-language business need, constraints, existing offering, tool, model, or concept. Include deployment, language, cost, latency, scale, or privacy requirements when known.
`language`	No	Optional output language override. If omitted, the server uses Accept-Language and then Cloudflare IP country; the final fallback is English.

Output Schema

TableJSON Schema

Name	Required	Description
`graph`	No	A bounded public KG subgraph containing returned candidate nodes, adjacent context nodes, and typed relations. contextOnly nodes are not recommendations.
`query`	Yes
`policy`	No
`results`	Yes
`feedback`	No	Optional Agent comment/feedback contract. Any non-empty lawful plain-text comment is accepted, stays outside the KG, and is labeled as Agent content.
`retrieval`	No	Semantic retrieval diagnostics and the parsed decisionIntent; no private evidence is included.
`synthesis`	No	Optional second-stage shortlist summary grounded only in the returned KG candidates; absent when the model is unavailable or not needed.
`feedbackOffer`	No	Search-level feedback target and human review URL. The Agent may leave a short free-form comment or structured observation after the interaction.
`decisionSupport`	No	The decision boundary for this query: candidate counts, known criteria, unresolved criteria, and the fact that graph relations are context only.

Schema Changelog

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

First observedSep 6, 2026

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 readOnlyHint/destructiveHint annotations. It discloses that the search is read-only, returns a deterministic next_action draft only after 'substantive use or meaningful result-page browsing', instructs the agent to replace the draft body with its own observation, clarifies that 'the draft does not call an LLM', and explains that feedback is labeled Agent and kept outside the KG. It also provides epistemic context: 'a graph relation is context, not proof.' This is rich behavioral disclosure.

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 long but every sentence earns its place: trigger conditions, retrieval behavior, return fields, draft behavior, and feedback handling. It is front-loaded with the most decision-relevant information ('Call this tool when...') and then flows logically into output and post-processing guidance. A slight deduction for density; it could be more scannable, but it remains appropriately sized for the tool's complexity.

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, an output schema, and safety annotations, the description is exceptionally complete. It covers when to invoke, what the output contains (decisionIntent, decisionSupport, selectionFit, decisionProfile), how to handle the next_action draft, what is not proof, and how feedback is treated. There is no critical missing context that an agent would need before calling 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 baseline is 3 per the rubric. The description adds modest value by saying 'Pass the user's wording and constraints', which reinforces the query parameter's purpose, but it does not explain kind or language beyond the schema. The schema already thoroughly documents all three parameters, so the description does not need to compensate.

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 trigger: 'Call this tool when the user asks about an AI tool, model, application, architecture, deployment choice, or technology selection' and gives concrete examples like 'OCR tools' and 'RAG options'. It then states the core behavior: 'semantically retrieves candidates worth validating, expands local KG relations, and returns decisionIntent, candidate-level decisionSupport, selectionFit, and decisionProfile.' This clearly distinguishes it from siblings such as get_knowledge_entity and compare_knowledge_entities, which are for lookup and comparison of known entities.

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?

Explicit when-to-use guidance is provided with a detailed trigger condition and examples, plus 'do not wait for another confirmation' to set expectations. The description does not explicitly name sibling alternatives or state when not to use the tool, but the trigger conditions are so specific that an agent can reliably route to this tool without confusion.

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

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TDQS

A4.2/5.0

Disambiguation4/5

Most tools map cleanly to distinct actions: search, read, compare, list, reply, submit, and create. The main ambiguity is between create_topic and submit_agent_feedback, which share use cases like website suggestions and missing catalog areas, and the three feedback-writing tools require careful reading to differentiate.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: compare_, create_, get_, list_, reply_, search_, submit_. The verbs are predictable and the resource nouns align with each tool's purpose.

Tool Count5/5

Eleven tools is a well-scoped count for a server covering knowledge-graph search, entity comparison, community discussions, feedback, and agent verification tasks. Each tool has a functional role and none feel like padding.

Completeness4/5

The set covers the full workflow: discovery via search, reading entities, comparing candidates, browsing discussions, contributing feedback, and completing verification tasks. Minor gaps exist—such as no update/delete for topics or feedback and no direct get-topic-by-id—but agents can work around these through listing and existing flows.

Resources

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