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ecosystem_diff_period

Calculate a time-period diff from per-repo event logs between two dates, returning summary counts for new repos, topic changes, star jumps, and status changes.

Instructions

Return a time-period diff computed dynamically from the per-repo event log.

Groups events by type to produce summary counts: new repos discovered, topics changed, stars jumped, status changed. Only scans that record events are counted (see ecosystem_repo_events). ecosystem_index_diff_latest instead returns the stored diff of the last ecosystem_index_update run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
to_dateYesEnd date in YYYY-MM-DD format (inclusive).
from_dateYesStart date in YYYY-MM-DD format (inclusive).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.9.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does disclose meaningful behavior: the diff is computed dynamically rather than read from storage, events are grouped by type into named categories, and only scans that record events are counted. It does not state permission requirements or cost/performance characteristics, but the core behavioral model is conveyed.

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?

The what-is-returned statement is front-loaded, followed by the grouping detail, the counting caveat, and the sibling differentiation. Four sentences with little waste, though the parenthetical pointer to ecosystem_repo_events and the sibling note could be tightened.

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?

An output schema exists, so return-value documentation is not required, and the description covers scope, counting rule, and sibling routing. Nothing critical for correct invocation is missing, though prerequisites for the underlying event log (e.g., which scans populate it) are only cross-referenced.

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 from_date and to_date documented as inclusive YYYY-MM-DD bounds, so the schema already carries the semantics. The description adds only the implied period framing ('time-period diff'), which does not extend beyond the schema.

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?

States a specific verb and resource ('Return a time-period diff computed dynamically from the per-repo event log') and names the sibling it is not ('ecosystem_index_diff_latest instead returns the stored diff'). An agent can distinguish this from ecosystem_repo_events and ecosystem_index_diff_latest without opening a schema.

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?

Names the alternative tool (ecosystem_index_diff_latest) and the distinguishing condition (dynamic vs. stored last-run diff), and points to ecosystem_repo_events for the underlying events. It stops short of an explicit 'use this when you want a live period comparison rather than the cached snapshot', so it is clear context rather than a full when/when-not rule.

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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