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get_fab_events

Read-onlyIdempotent

Fab Milestones — the dated construction & ramp event log: announcement → groundbreaking → equipment move-in → risk production → HVM, plus expansions and SCHEDULE SLIPS recorded as their own events (a delay never overwrites the original plan). Back to 2020. Each row: foundry, fab_name, event_type, event_date, announced_date, a summary, source URL, verbatim quote, and is_projection for forward-dated milestones.

USE THIS for: "which fabs hit a milestone recently?", tracking TSMC Arizona / Samsung Taylor / Intel Ohio / Micron / SK hynix timelines, "which projects have slipped?", validating fab-capacity projections against construction reality.

DO NOT USE for: current capacity numbers (use get_fab_capacity); allocation/lead-time (use get_foundry_allocation).

Filters: foundry, fab_id, event_type (announced|groundbreaking|equipment_move_in|risk_production|hvm_start|expansion|delay|cancellation|conversion), country. Latest slice for all tiers; full history Pro (never a 403). Cite as "Silicon Analysts — Fab Construction Milestones".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
allNo
fromNo
limitNo
sinceNo
fab_idNo
countryNo
foundryNo
event_typeNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive, so the safety profile is known. The description adds valuable behavioral context beyond annotations: 'a delay never overwrites the original plan', 'is_projection for forward-dated milestones', and 'Full history Pro (never a 403)' — all non-obvious behaviors an agent needs to correctly interpret results.

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?

The description is well-structured and front-loaded: a crisp definition, followed by use cases, exclusions, filters, and data shape. Each sentence adds distinct information, and the 'USE THIS'/'DO NOT USE' formatting makes it scannable. No wasted words.

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?

Without an output schema, the description explains the row structure in detail (foundry, fab_name, event_type, event_date, announced_date, summary, source URL, quote, is_projection). It also covers access levels and citation. The only notable gap is the semantics of the date-range parameters (to/from/since/all), but overall the tool is well understood.

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 0%, so the description must compensate. It does enumerate the meaningful filters (foundry, fab_id, event_type with allowed values in parentheses, country), which adds real semantics. However, it leaves to/from/since/all/limit undefined, and the difference between 'since' and 'from' is not addressed. Partial compensation warrants a 3.

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 opens with 'Fab Milestones — the dated construction & ramp event log' which clearly identifies the resource and its purpose. It goes on to list concrete event types and explicitly distinguishes from sibling tools like get_fab_capacity and get_foundry_allocation, making the tool's role unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides an explicit 'USE THIS for' section with concrete examples (tracking TSMC Arizona, Intel Ohio, etc.) and a 'DO NOT USE for' section naming alternative tools (get_fab_capacity for capacity, get_foundry_allocation for allocation). This is exactly the kind of when-to-use/when-not-to-use guidance the dimension calls for.

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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TDQS

A4.4/5.0
Disambiguation3/5

Several tools overlap in domain and purpose, such as get_market_pulse vs get_market_intelligence and get_wafer_pricing vs get_foundry_economics. The detailed USE/DO NOT USE sections help, but the tool names alone do not always make the distinction obvious, requiring careful reading to avoid misselection.

Naming Consistency4/5

18 of 20 tools follow a consistent get_<noun> pattern, with calculate_chip_cost and estimate_lead_time as minor deviations. No chaotic mixing of camelCase or inconsistent verb styles; the overall scheme is predictable and readable.

Tool Count4/5

20 tools is slightly above the ideal 3-15 range but reasonable for the server's broad scope covering cost modeling, capacity, allocation, HBM, policy, and market intelligence. Each tool has a distinct niche, though a few could potentially be consolidated.

Completeness4/5

The server provides comprehensive coverage of semiconductor cost estimation, market data, fab capacity, allocation, HBM qualification, and policy timelines. Minor gaps exist (e.g., no dedicated memory pricing tool or general search), but these are covered through get_market_dataset and other tools; there are no critical missing functions for the stated domain.

Resources