Silicon Analysts

get_recent_changes

Read-onlyIdempotent

"What Changed" — recent MOVEMENTS in Silicon Analysts' public data over a 7d/30d window, derived from the daily snapshot ledger. Each moved metric returns direction (up/down), magnitude (pct_delta for value metrics, pp_delta for percentage metrics), old/new values, the two snapshot dates compared (as_of, prior_as_of), window_days_actual (the REAL lookback — the ledger is young, so a 30d window clamps to available history), and per-record provenance. Domains: wafer_pricing, chip_cost, margin_benchmark, foundry_capacity, defect_density, nre_cost.

USE THIS for: "what moved in semiconductor costs this week?", "did any wafer prices change recently?", "what changed since my last fetch on June 20?" (use since), building a market-change digest, monitoring deltas across the data layer over time.

DO NOT USE for: current absolute values (use get_wafer_pricing / get_accelerator_costs / get_foundry_allocation); allocation lead-time trend specifically (use get_foundry_allocation with include_history).

Filters: window (7d|30d), since (ISO date — compare the latest snapshot against the newest snapshot at/before it; overrides window for baseline selection), datasetId (one domain), minDelta (override the significance threshold), limit. N2/Apple omitted (conflict-safe). Returns an empty array when nothing moved past the significance gate — does not error. Cite as "Silicon Analysts — What Changed".

Input Schema

TableJSON Schema

Name	Required	Default
`limit`	No
`since`	No
`window`	No	7d
`minDelta`	No
`datasetId`	No

Schema Changelog

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

Changed1 schema field changedJul 4, 2026
- addedInput schema / properties / since
```
Added value: +{
+  "maxLength": 40,
+  "type": "string"
+}
```
AddedJun 21, 2026

TDQS

A5/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description richly discloses behavior: returned fields (direction, magnitude, pct_delta vs pp_delta, old/new values, dates, window_days_actual with clamping behavior), provenance, N2/Apple omission, and the empty-array behavior instead of an error. This goes far beyond what annotations alone convey.

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 front-loaded with the core purpose, then uses labeled sections (USE THIS, DO NOT USE, Filters) to organize information. Every sentence adds value—edge cases, parameter semantics, and output details—with no fluff. The length is justified by the tool's complexity and the absence of an output schema.

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?

With no output schema, the description fully specifies the response structure including all fields and their types, explains the clamping behavior for young ledger history, edge cases like empty results, and the citation string. It also covers all parameters and domain list, making it entirely self-sufficient for an agent to select and invoke correctly.

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?

Despite 0% schema description coverage, the description compensates fully by explaining every parameter: window's options and default, since's ISO date semantics and override behavior, datasetId limiting to one domain, minDelta as significance threshold, and limit. It adds meaning far beyond the bare schema types and constraints.

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 'What Changed' — recent MOVEMENTS... clearly identifying the tool's unique purpose of exposing deltas over 7d/30d windows rather than absolute values. It explicitly distinguishes itself from siblings by naming get_wafer_pricing / get_accelerator_costs / get_foundry_allocation for absolute values, and lists domains. This is a specific verb+resource+scope with clear sibling differentiation.

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 explicit 'USE THIS for' and 'DO NOT USE for' sections with concrete example queries and named alternative tools. It contrasts with sibling tools for absolute values and the specific allocation lead-time trend use case, covering both when to use and when not to use.

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

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