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 (the datasetId values): wafer_pricing, chip_cost, gpu_secondary, margin_benchmark, foundry_capacity, defect_density, nre_cost, packaging_benchmark, chip_archetype, electricity_price, cloud_pricing, llm_pricing, memory_spot, foundry_economics, market_prints, fab_capacity, hbm_market.

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.

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

TDQS

A4.9/5.0

Behavior5/5

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

With annotations already covering the safety profile (readOnly/idempotent/non-destructive), the description goes well beyond them: it discloses the return shape (direction, pct_delta vs pp_delta, old/new values, as_of/prior_as_of, provenance), explains that window_days_actual may clamp a 30d request because 'the ledger is young,' notes N2/Apple are omitted as conflict-safe, and states that an empty array is returned rather than an error. These are exactly the behavioral traits annotations cannot express.

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?

Front-loaded with the conceptual definition, then usage, then exclusions, then filters — every block earns its place. It is long, and the 17-item domain enumeration plus the return-field inventory add bulk, but that length is largely load-bearing given the 0% schema coverage and absent 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?

For a 5-param, no-required-args tool with no output schema, the description must supply both input semantics and return semantics — and it does: full domain list, filter behaviors, delta field meanings, clamping caveat, and empty-result behavior. An agent has everything needed to call and interpret it.

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% and there are 5 parameters, so the description carries the full burden — and it does: window is enumerated, since is defined precisely (compare latest snapshot against the newest snapshot at/before that date, overriding window for baseline selection), datasetId is constrained to 'one domain' with all 17 valid values listed, and minDelta/minDelta's significance-threshold role is explained.

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?

States a specific verb+resource+scope: 'recent MOVEMENTS in Silicon Analysts' public data over a 7d/30d window, derived from the daily snapshot ledger.' An agent can immediately distinguish this delta-feed from absolute-value siblings like get_wafer_pricing without opening any schema.

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 USE THIS / DO NOT USE blocks with concrete trigger phrases ('what moved in semiconductor costs this week?') and named alternatives for each excluded case (get_wafer_pricing, get_accelerator_costs, get_foundry_allocation with include_history). Nothing is left to inference.

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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