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CronusL-1141

AI Team OS

by CronusL-1141
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Python
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ecosystem_deep_review_list

Lists deep reviews newest-first, optionally filtered by status (queued, completed, failed) to track in-flight or finished reviews. Returns up to 100 rows.

Instructions

List deep-reviews newest-first, optionally filtered by status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows to return (1..100).
statusNoqueued (in flight) / completed / failed, derived from stage_status. 'running' appears only on rows created before stage_status existed, so filter by queued to find in-flight reviews. Empty = all.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.15.0
    • changedInput schema / properties / status / description
      Previous value: -"queued / completed / failed ('running' only matches\npre-v1.6.2 historical rows — status is now a derived\nread-only view of stage_status). Empty = all."New value: +"queued (in flight) / completed / failed, derived from\nstage_status. 'running' appears only on rows created before\nstage_status existed, so filter by queued to find in-flight\nreviews. Empty = all."
  2. First observedv1.9.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description bears the full burden. It does usefully disclose sort order (newest-first) and that filtering is optional, but says nothing about permissions, pagination beyond the limit parameter, or total counts. An output schema exists, so return-shape explanation is not required, but behavioral context is thin.

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?

A single front-loaded sentence with no wasted words; verb, resource, ordering, and the optional filter all appear immediately.

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?

For a two-parameter, non-destructive list tool with a fully documented schema and an output schema, the description covers the essentials. The main gap is absent guidance on when to prefer this over sibling listing/status tools.

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%, with both limit and status documented in depth (including the 'running' legacy nuance), so the schema does the heavy lifting. The description only restates that a status filter exists, adding no meaning beyond the schema baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb (List) and resource (deep-reviews) plus the ordering semantics (newest-first) and the optional filter. It does not distinguish itself from nearby siblings like ecosystem_deep_review_status or ecosystem_deep_review_request, leaving the agent to infer that this is the enumeration tool.

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 when-to-use guidance and no mention of alternatives. The agent cannot tell from the description why it should call this rather than ecosystem_deep_review_status or ecosystem_repo_events when looking for review data.

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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