Skip to main content
Glama

ecosystem_refresh

Refresh the active ecosystem set on demand by probing each top repo for new pushes, writing status snapshots, and re-queuing shallow scans for updated repos.

Instructions

On-demand incremental refresh of the project's active ecosystem set.

Replaces the retired weekly cron (2026-07-10 decision: CC is not always-on, so long-running timers are pointless — refresh happens when the user asks for it). For each active-set repo (top_n by stars) this probes GitHub once, writes a status snapshot, and re-queues a Stage 0 shallow summary only when the repo has new pushes; 404/403 mark the profile deleted/private.

The response's hint field (present when repos were re-queued) reminds you to run the actual shallow scans via ultracode/Workflow and write results back with ecosystem_apply_shallow_summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNoOptional human-readable note attached to the ScanRun.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations are provided, so the description fully carries the transparency burden. It details the incremental refresh logic, GitHub probes, snapshot writing, conditional re-queuing on new pushes, and error handling for 404/403. It also mentions the response's hint field, providing comprehensive behavioral insight.

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 description is appropriately sized and front-loaded with the main purpose. Each sentence adds value (historical context, per-repo actions, error handling, hint field). It is slightly dense but not wasteful.

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?

Given the tool's simplicity (one optional parameter, no required params), the description covers the refresh process, triggers, error states, and output hint. It is sufficient for an agent to invoke the tool correctly.

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 coverage is 100% for the single parameter 'notes', and the description does not add meaning beyond what the schema already provides. The parameter's purpose is clear from the schema alone.

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 the tool's purpose: 'On-demand incremental refresh of the project's active ecosystem set.' It uses a specific verb-resource pair and distinguishes it from sibling tools by explaining it replaces a retired weekly cron.

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?

The description provides context on when to use (on-demand, when user asks) and what it does (per repo actions). It does not explicitly list when not to use or alternatives, but the context is clear enough for an agent to decide.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/CronusL-1141/AI-company'

If you have feedback or need assistance with the MCP directory API, please join our Discord server