AIWorkhub

by shrec

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

aiworkhub_task_show

Show a single task card by task_id.

Instructions

Show a single task card by task_id.

Summary-first by default: retained baseline hash maps (tree, workspace and parent baselines) are folded to entry count plus sha256, and the evidence blobs (terminal_review.evidence.{validation, quality_gate, quality_review, quality_review_receipt, worker_mcp_gate, workspace} and accept_evidence.{validation, quality_gate}) are folded to {summarized, bytes, sha256, verdict/status, failed_count, first_failure}. terminal_failure and rework_predecessor are folded to the facts a reviewer reads (substatus/reason/category/first_error and task_id/request_id/outcome/reason); template_provenance.expanded_contract is folded to identity only. detail="evidence" keeps all of the above exact (baselines still folded); detail="full" or full=True returns the exact stored card. Summary/evidence renders are compact JSON; full keeps the indented one.

The manager-authored bodies (review_feedback, recovery_feedback, history) fold to identity plus their counts in the summary render, and every summary/evidence reply carries card_sha256/card_bytes plus review_findings_count when the card records reviewer reports: measured avg 11.5 KB/call over 31 calls (NF-2026-01447).

Input Schema

Name	Required	Default
`full`	No
`detail`	No	summary
`task_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 20, 2026

TDQS

A4.2/5.0

Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly: it explains folding behavior for baselines, evidence blobs, terminal_failure, rework_predecessor, feedback bodies, and history, and distinguishes exact versus compact renders for each detail mode. It also discloses output size and card hashes, giving the agent unusually rich behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The first sentence is clear and front-loaded, but the remainder is extremely dense, jargon-heavy, and parenthetical, with long lists of internal field paths. It is informative but not concise or easy to scan.

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 an output schema exists, the description does not need to enumerate return fields, and it instead focuses on the important render-mode behavior and folding semantics. It is nearly complete, though it could more clearly state precedence between detail='full' and full=True.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It does this well for 'detail' and 'full', explaining the three detail modes and the full=True alias, but it does not add format or lookup guidance for 'task_id' beyond naming it.

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 first sentence states a specific verb and resource — 'Show a single task card by task_id' — which clearly distinguishes this from list-oriented sibling tools such as task_list. An agent can identify the core operation without inspecting the schema.

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

Usage Guidelines3/5

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

The description establishes that summary is the default and explains what evidence and full return, but it does not explicitly say when an agent should choose each mode or when to use this tool versus dashboard_task_detail or task_review_summarize. Usage is implied rather than guided.

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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