Skip to main content
Glama
CronusL-1141

AI Team OS

by CronusL-1141
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

ecosystem_scan

Scans Claude ecosystem repositories on GitHub, filters by minimum stars, and updates ecosystem_repo_profiles.

Instructions

Scan popular Claude ecosystem repos (>=min_stars) and update ecosystem_repo_profiles.

Runs 8-10 gh search queries covering:

  • topic:claude-code / topic:mcp / topic:mcp-server / topic:claude-agent

  • topic:agent-framework + "claude" / topic:ai-agents + "claude"

  • "claude code plugin" / "anthropic agent"

  • anthropics org public repos

Deduplicates + filters >=min_stars + excludes known repos (CronusL-1141/AI-company etc.) Sets needs_deep_review=True for stars < 15000. relevance_category is auto-classified heuristically (based on topics + description keywords).

It also calls gh api once per matched repo to read its topics. The query set is fixed in this tool and the project's ecosystem settings are ignored; no repo events are recorded, so ecosystem_repo_events and ecosystem_diff_period do not see this scan. For a settings-driven scan with a diff, use ecosystem_index_update.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNoWhen True, run every gh query and report what would be written without touching the DB — use it to size a scan before paying for the writes.
min_starsNoPopularity floor for a repo to enter the archive. Lower it (e.g. 1000) for a wide full sweep, raise it to only refresh the well-known head of the ecosystem. Values <= 1000 mark the run as strategy="full", above that as "incremental".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.11.2
    • addedInput schema / properties / dry_run / description
      Added value: +"When True, run every gh query and report what would be\nwritten without touching the DB — use it to size a scan before\npaying for the writes."
    • addedInput schema / properties / min_stars / description
      Added value: +"Popularity floor for a repo to enter the archive. Lower it\n(e.g. 1000) for a wide full sweep, raise it to only refresh the\nwell-known head of the ecosystem. Values <= 1000 mark the run as\nstrategy=\"full\", above that as \"incremental\"."
  2. First observedv1.9.0

TDQS

A4.4/5.0
Behavior5/5

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 discloses that the query set is fixed and project ecosystem settings are ignored, that it makes one gh api call per matched repo, that needs_deep_review is set below 15000 stars, and that ecosystem_repo_events and ecosystem_diff_period do not see the scan. These are exactly the side-effect facts an agent needs before invoking a write-heavy tool.

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 purpose and the mutation target, then structured with a bulleted query list that is long but earns its place by telling the agent what will actually be searched. The caveat paragraph is dense but necessary; the enumeration could be slightly tighter.

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?

For a tool with an output schema already covering return values, the definition supplies everything else an agent needs: scope, side effects, flags set, external calls, and what it does not touch. Nothing material is missing.

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%, so baseline is 3. The schema already documents dry_run and min_stars (including the strategy threshold), and the description only loosely reinforces min_stars via '>=min_stars' without adding syntax or format meaning 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 ('Scan popular Claude ecosystem repos and update ecosystem_repo_profiles') and then enumerates the exact query set, so an agent knows precisely what work this performs and what table it mutates. It is clearly distinguishable from siblings like ecosystem_search or ecosystem_summary_health.

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 routes the agent away from this tool when a settings-driven scan with a diff is wanted, naming ecosystem_index_update as the alternative. It also gives a condition for dry_run. It does not address the closest sibling, ecosystem_scan_periodic, leaving that choice to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools