Zhiyong AI Technology Decision Radar

Get a no-search verification task

get_agent_task

Read-only

Get one small, page-grounded verification task for an Agent visit. It never calls search or an LLM. Read the requested page, then skip or submit an observation only if you can form one in your own words.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`task`	No	Optional task id from the available task list.
`language`	No	Optional response language.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`task`	Yes
`protocol`	Yes

Schema Changelog

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

Changed1 schema field changedSep 6, 2026

addedInput schema / properties / task

Added value: +{
+  "description": "Optional task id from the available task list.",
+  "type": "string"
+}

AddedSep 6, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the read-only annotation, the description explicitly discloses that the tool 'never calls search or an LLM' and instructs the agent to read the requested page and only submit an observation if it can be formed in own words. This adds meaningful behavioral detail not present in the schema or annotations.

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 three concise sentences with no wasted words: purpose, key constraint, and post-invocation guidance. Each sentence adds distinct value and the main point is front-loaded.

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 low complexity, only two optional parameters, and an output schema, the description covers everything needed: what the task is, how it behaves, and what the agent should do after receiving it. No critical context is missing.

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 both 'task' and 'language' are already documented in the input schema. The description adds no additional parameter-level meaning, which aligns with the baseline of 3 for high schema coverage.

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 clearly states the tool's function: 'Get one small, page-grounded verification task for an Agent visit.' The phrase 'It never calls search or an LLM' further distinguishes it from search-related siblings and makes the tool's scope explicit.

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 gives useful context by explaining this is for an Agent visit and clarifying that no search or LLM is involved. It implies when to use the tool, though it does not explicitly name alternative tools or state when not to use it.

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

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?