Silicon Analysts

get_forecasts

Read-onlyIdempotent

Forecast Vintages — third-party forecasts (research firms like TrendForce/WSTS/SEMI, and company capex/bit-growth guidance) archived with their ORIGINAL publication date. Query the REVISION HISTORY, not just the latest number: "what did TrendForce say about 2026 HBM bit growth in January vs July?". Each row: originator, originator_type, metric, target_period (e.g. CY2026, 2027H1), value (num or low/high), unit, as_of (publication date), a source URL, and a verbatim quote. This is the vintage archive of OTHER organizations' forecasts — distinct from our own scenario models.

USE THIS for: forecast revision tracking, "how has the 2026 capex outlook moved across TSMC's earnings calls?", comparing what different firms projected for the same target period, building a consensus-vs-time view.

DO NOT USE for: current cost/pricing values (use get_wafer_pricing / get_accelerator_costs); Silicon Analysts' OWN frozen and graded projections (use get_track_record).

Filters: originator, originator_type (research_firm|company_guidance|government|bank|industry_body|other), metric, target_period, entity_id. group='series' additionally returns a "chains" array — the rows already collapsed by originator + metric + target period, oldest print first, with the change between prints — which is usually what you want instead of reassembling them yourself. Latest slice for all tiers; full history (from/to/since/all/group=series) needs a free API key — anonymous callers get the latest slice with a note, never an error. Cite as "Silicon Analysts — Forecast Vintages".

Input Schema

TableJSON Schema

Name	Required	Description	Default
`to`	No
`all`	No
`from`	No
`group`	No
`limit`	No
`since`	No
`metric`	No
`entity_id`	No
`originator`	No
`target_period`	No
`originator_type`	No

Schema Changelog

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

Changed1 schema field changedAug 30, 2026

addedInput schema / properties / group

Added value: +{
+  "enum": [
+    "none",
+    "series"
+  ],
+  "type": "string"
+}

AddedJul 20, 2026

TDQS

A5/5.0

Behavior5/5

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

Annotations already mark this as read-only, idempotent, and non-destructive, and the description adds substantial behavioral context beyond that: the vintage/snapshot semantics, the group='series' chain collapsing behavior, the API-key tiering where anonymous callers get only the latest slice with a note rather than an error, and the required citation string. No contradiction with annotations.

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 long but every sentence earns its place: it front-loads the core concept with a concrete example, then covers usage exclusions, filters, grouping behavior, auth limits, and citation in a scannable structure. The bolded 'USE THIS for' and 'DO NOT USE for' labels make it easy for an agent to parse quickly.

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 having no output schema and 11 optional parameters, the description tells the agent exactly what each returned row contains, how to query for revision history vs. latest data, what happens for anonymous callers, how to cite the results, and which sibling tools should be chosen instead. There is no critical missing context for correct invocation.

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 description coverage is 0%, so the description carries the full semantic burden, and it delivers: it names the meaningful filters (originator, originator_type, metric, target_period, entity_id), explains what group='series' does to the rows, and clarifies how from/to/since/all relate to the API-key tier. It goes well beyond the bare 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 names a specific resource ('Forecast Vintages' archive of third-party forecasts) and a specific purpose ('Query the REVISION HISTORY, not just the latest number'), with concrete examples like 'what did TrendForce say about 2026 HBM bit growth in January vs July?'. It also explicitly distinguishes itself from our own scenario models, so it is easy to tell apart from siblings.

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 has explicit 'USE THIS for' and 'DO NOT USE for' sections, naming specific alternatives: get_wafer_pricing / get_accelerator_costs for current pricing, and get_track_record for Silicon Analysts' own projections. This is the gold standard for routing an agent to the correct tool.

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.

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