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ecosystem_shallow_queue_status

Retrieve the status of the shallow-scan queue for an active project, including counts of active profiles, pending scans, in-flight dispatches, failures, and self-learning progress.

Instructions

Show Stage 0 shallow-scan queue status for the active project.

Returns counts for active profiles, pending shallow scans, in-flight dispatches, terminal failures (shallow_failed), and deleted/private-flagged repos. The self_learning_pending map shows how many distinct repos have hit each failure class so far (a class fires a pattern_record entry once the count reaches 3).

Returns: {project_id, active_total, pending_shallow, in_flight, shallow_failed, deleted, private_now, concurrency, self_learning_pending}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so the description carries full burden. It details the self_learning_pending map behavior (fires pattern_record after count 3) and lists all return fields, providing adequate transparency for a read-only status tool. However, it omits potential side effects or error conditions.

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 a single concise paragraph: first sentence states purpose, then lists return fields, and finally explains a special derived field. Every sentence adds value, no wasted words.

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?

Tool complexity is low (no params, no annotations) and description fully covers the return fields with behavioral detail (self_learning_pending threshold). The output schema is effectively documented in text, making it complete for the agent to understand what it returns.

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?

Tool has zero parameters and schema coverage is 100% (trivially). Per calibration rules, 0 parameters yields baseline 4. The description does not need to add param info, but it compensates by explaining the return structure.

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 'Show Stage 0 shallow-scan queue status for the active project' (specific verb+resource) and lists all returned fields (counts, map), distinguishing it from sibling tools like ecosystem_scan_status which cover general scan state.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives such as ecosystem_scan_status or ecosystem_deep_review_status. Lack of when-not-to-use or prerequisite information leaves the agent to infer from context alone.

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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