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ecosystem_apply_shallow_summary

Submit a shallow summary or failure report for a repository review. Pass a summary to persist and advance stage status, or specify error kind for automatic retry or classification.

Instructions

Stage 0 worker callback: write back a shallow summary OR report a failure.

Success path (default): pass shallow_summary (200-400 char Chinese markdown) and deep_review_id; the OS will persist the summary, advance stage_status -> shallow_done, and mark the deep_review row as completed.

Failure path: leave shallow_summary empty and pass error_kind, which routes the failure through the §3.1 classifier so the OS can decide whether to immediate-retry, mark deleted/private, or feed the self-learning loop. Valid error_kind values: http / agent_read / agent_timeout / json_parse / fetch_style.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_idYesEcosystemRepoProfile.id.
error_kindNofailure category hint (failure path only).
http_statusNoHTTP status code when error_kind='http'.
error_messageNoshort message stored in profile.last_fetch_error.
deep_review_idNoassociated deep_review row id (Stage 0 dispatch).
shallow_summaryNo200-400 字中文 markdown 总结 (success path).
rate_limit_remainingNowhen http_status=403, ``0`` indicates rate-limit.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided, so description carries full burden. It explains effects (persist summary, advance stage, route failures) and mentions rate_limit_remaining for 403. However, missing details on idempotency, auth requirements, or consequences of repeated calls—adequate but not thorough.

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?

Well-structured with clear sections for success and failure paths. Front-loaded with main purpose. Efficient, though could slightly condense the error_kind enumeration.

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?

Covers the two main workflows and parameter roles. Output schema exists (not shown) so return values are covered. Lacks mention of prerequisites or whether the agent should call this directly vs. it being system-invoked—but sufficient for most use cases.

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

Parameters5/5

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

Schema coverage is 100%, but description adds significant value by grouping parameters into success/failure paths, explaining the role of deep_review_id, and listing valid error_kind values. This goes beyond schema descriptions.

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?

Clear statement: 'Stage 0 worker callback: write back a shallow summary OR report a failure.' Distinguishes success and failure paths, and the name implies its role among ecosystem sibling 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?

Explicitly describes when to use success vs. failure path, lists valid error_kind values. Lacks explicit 'when not to use' or comparison with other ecosystem apply tools, but context from name and description suffices.

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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