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ecosystem_index_update

Scans repositories to compute ecosystem index differences, optionally previewing changes with dry_run before persisting. Use to update profiles and alert on new entries.

Instructions

Trigger ecosystem index update — runs scanner + computes diff.

Maps to POST /api/ecosystem/index_update. Scan config comes from the project's ecosystem settings (min_stars gate, focus_topics queries — empty falls back to the built-in Claude-ecosystem query set, alert_max_new_per_scan threshold), then runs the full pipeline: gh search → classify active status → diff against DB → alert threshold check → (if dry_run=False) persist index_diff + status_changes. When dry_run=True, no writes touch ecosystem_repo_profiles / ecosystem_index_diffs / ecosystem_status_changes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNoWhen True (default), simulate the scan and return diff preview only. When False, persist profile upserts + index_diff + status_changes.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.9.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it names the config source (min_stars, focus_topics, alert_max_new_per_scan), the full pipeline order, and precisely which tables are written (ecosystem_repo_profiles / ecosystem_index_diffs / ecosystem_status_changes) and that dry_run=True touches none of them. It lacks failure/rate-limit behavior, so it is not a 5.

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?

Front-loaded with the core action, then the pipeline and the dry_run boundary. The pipeline enumeration is long but each stage is informative; the only mild noise is the escaped-backtick formatting and the redundant restatement of the write behavior between the prose and the schema.

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 values need not be explained. Given no annotations and a side-effecting tool, the description adequately covers configuration source, pipeline stages, and the write/no-write boundary — everything an agent needs to call it safely.

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 coverage is 100% and there is only one parameter, so baseline is 3. The description goes beyond the schema by spelling out that dry_run=False persists profile upserts + index_diff + status_changes across three named tables, adding real semantic weight to the flag.

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 specific verb and resource ('trigger ecosystem index update') and immediately summarizes the behavior ('runs scanner + computes diff'). It does not explicitly differentiate itself from near-siblings like ecosystem_scan, ecosystem_refresh, or ecosystem_index_diff_latest, so it falls just short of a 5.

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?

No explicit when-to-use/when-not guidance relative to the many ecosystem_* siblings (scan, refresh, scan_periodic). Usage is only implied through the dry_run default (simulate first) and the description of what a full run does, which is enough to infer intent but leaves alternative selection to the agent.

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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