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Silicon Analysts

by io.github.SiliconAnalysts
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get_foundry_economics

Read-onlyIdempotent

Foundry IR ECONOMICS — per-foundry, per-process-node, per-fiscal-quarter wafer ASP (min/max/blended, USD per 300mm-equivalent wafer, $250-grained) and fab UTILIZATION (%), derived exclusively from PUBLIC IR materials (earnings releases/transcripts/decks, trade press) via a documented scaling calculation (rev-mix-v1): reported revenue × reported node revenue-shares × reported wafer shipments, allocated on pinned analyst prior ratios. Covers tsmc | umc | intel | samsung | smic | gf. Every row carries source_urls + release_dates + confidence (high/medium/low); utilization is 'stated' (company said it — UMC/SMIC style) or 'derived' (shipments vs capacity estimate, capped medium) and NEVER fabricated per node. include_facts=true returns the underlying evidence facts (verbatim quote + source per datum).

Also returns node_margin_estimates for TSMC: per-node est. wafer price / est. wafer cost / est. GROSS MARGIN % with ranges (N3/N5/N7/N16/N28+/N2) — single-vintage Silicon Analysts ESTIMATES from public analysis, explicitly labelled (TSMC does not disclose per-node margin; company-level GM is quarterly IR).

USE THIS for: "what does a TSMC 3nm wafer sell for and how has it moved by quarter?", "TSMC blended ASP trend", "UMC utilization last quarter", "N3 share of TSMC revenue over time", "estimated gross margin by node", node-economics history for models.

DO NOT USE for: the current spot wafer price band only (use get_wafer_pricing — that is the live analyst-consensus band this dataset cross-validates against); allocation/lead-time/booking (use get_foundry_allocation); chip-level cost (use calculate_chip_cost / get_accelerator_costs).

Filters: foundry, node (canonical token, e.g. n3 | 22-28nm | 18a), node_group (leading_3nm | class_5nm | ...), quarter (2026Q1 | 2025FY), from/to range, include_facts, limit. LATEST period per foundry is free; multi-period HISTORY (quarter/from/to) requires a Pro key — free callers are clamped to latest with an explanatory meta.note (never an error). Sparse-disclosure foundries (Intel, Samsung) return nulls/low confidence rather than invented numbers. Refreshes weekly (Mon 14:30 UTC) + each earnings season. Cite as "Silicon Analysts — Foundry IR Economics".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
fromNo
nodeNo
limitNo
foundryNo
quarterNo
node_groupNo
include_factsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations that mark the tool as read-only and idempotent, the description adds extensive context: sourcing from public IR materials, a documented scaling calculation, confidence levels, the free-vs-Pro clamping behavior, the handling of sparse-disclosure foundries with nulls/low confidence, and refresh cadence. It also discloses that node margin estimates are explicitly labelled estimates from public analysis.

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 long, but that length is justified by the tool's complexity and the absence of schema descriptions for 8 parameters. It is well-structured with clear sections for data coverage, usage guidance, filtering behavior, and caveats. Every sentence adds necessary information.

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 8 parameters, no output schema, and a mix of free/Pro behavior, the description covers all key aspects: return data, source provenance, confidence levels, node margin estimate details, filter syntax, free-tier limits, error behavior (never an error), and refresh timing. It is exceptionally complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description richly compensates for every parameter. It explains foundry tokens, node canonical examples (n3, 22-28nm, 18a), node_group examples, quarter pattern (2026Q1|2025FY), and include_facts semantics. It also explains the behavioral meaning of the from/to and limit parameters in the context of Pro-key restrictions.

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 a specific, resource-rich definition: 'per-foundry, per-process-node, per-fiscal-quarter wafer ASP and fab UTILIZATION'. It also distinguishes from siblings by naming concrete data sets (get_wafer_pricing, get_foundry_allocation) to avoid overlap.

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 includes explicit 'USE THIS for' and 'DO NOT USE for' sections with concrete example queries and alternative tool names. It clearly states when this tool is appropriate and when other tools are the correct choice.

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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