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Silicon Analysts

by io.github.SiliconAnalysts
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get_market_dataset

Read-onlyIdempotent

Curated market-data TIME SERIES with per-point sourcing — the datasets behind siliconanalysts.com/market-data. Includes: hbm-pricing (HBM contract + blended $/GB by generation, HBM2→HBM4, anchors 2017→2026 — series_keys like 'hbm3e-contract'; NOTE: no public HBM spot market exists — HBM sells via LTAs, and the dataset says so rather than fabricating a spot curve), component-lead-times (CoWoS-S/CoWoS-L/HBM3E/TSMC-N3 lead times in weeks back to 2022), wafer-price-tsmc (wafer price by node back to 65nm), semiconductor capex, DRAM/NAND pricing, and more. Every point carries value_low/mid/high, confidence, data_type (Confirmed|Estimate|Projection), source_name, source_date, source_note — estimates are typed as estimates, never dressed as observations.

USE THIS for: HBM contract price history by generation and basis ("what did HBM3E contract $/GB do through the 2023 shortage?" — note the revenue-implied vs per-stack bases are ~1.7x apart and must not be compared across series), lead-time trend series, wafer price history by node, memory price cycles — any question needing the dated SERIES rather than the current snapshot.

DO NOT USE for: current HBM market snapshot (use get_hbm_market_data); current wafer price bands (use get_wafer_pricing); IR-derived per-node ASP/utilization (use get_foundry_economics); allocation status (use get_foundry_allocation).

Params: dataset (id; pass 'list' to enumerate the catalog), series_key (optional filter, e.g. 'hbm3e-contract'). Tiering: anonymous → recent points; free key → recent + newest-3-per-series history preview; Pro → complete series. Each dataset returns methodUrl — a published page describing HOW the series was built (typing rules, derivations, deliberate gaps) — or null; read it before reasoning about modelled points. Pro callers can pull the whole series as flat CSV/JSONL in one call: GET /api/v1/export?dataset=&format=csv. Cite as "Silicon Analysts — Market Data".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetYesDataset id, e.g. 'hbm-pricing', 'component-lead-times', 'wafer-price-tsmc'. List ids via get_market_dataset with dataset='list'.
series_keyNoOptional series filter, e.g. 'hbm3e-contract' or 'hbm4-contract'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations indicate read-only, idempotent, non-destructive behavior; description adds tiering (anonymous vs free key vs Pro), methodUrl for methodology, and explains data point typing (Confirmed/Estimate/Projection) with no contradiction.

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 fairly long but well-structured with clear sections and bullet points. Every sentence adds value; minor redundancy could be trimmed but remains efficient for the complexity.

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?

Despite no output schema, description details return fields (value_low/mid/high, confidence, data_type, source), explains methodUrl, and mentions CSV/JSONL export for Pro callers, covering all necessary user context.

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% (baseline 3). Description adds context: dataset='list' enumerates catalog, series_key is optional filter, and tiering affects data access, providing extra guidance beyond 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?

The description clearly states it provides curated market-data time series with per-point sourcing, listing specific datasets (HBM pricing, component lead times, wafer price, etc.) and distinguishing itself from sibling tools for current snapshots or IR-derived metrics.

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?

Explicit sections 'USE THIS for' and 'DO NOT USE for' list appropriate use cases (e.g., HBM contract price history) and forbidden ones (e.g., current HBM snapshot, use get_hbm_market_data instead), guiding the agent to correct tool selection.

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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