What Led To

Ask a why or how question

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Answer a why/how question as a chain: an ordered set of dated, sourced entries from the timelines with one line on why each led to the next, written by the editor rather than generated. Returns the best-matching chain with every step's entry (URL, date, summary, sources), or, when no chain fits, the questions that do exist and how to research the answer with the other tools.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`since`	No	Also return what changed on the chain since this day (its changelog entries after it); pass the updated_at you last saw.
`question`	Yes	The question in plain words, e.g. "Why did Nvidia become so dominant in AI?"

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 14, 2026

addedInput schema / properties / since

Added value: +{
+  "description": "Also return what changed on the chain since this day (its changelog entries after it); pass the updated_at you last saw.",
+  "pattern": "^\\d{4}(-\\d{2}(-\\d{2})?)?$",
+  "type": "string"
+}

AddedSep 14, 2026

TDQS

A3.7/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses that chains are editor-written rather than generated, that the fallback path returns existing questions plus research guidance, and what each returned step contains. It omits auth needs, rate limits, and matching behavior under partial fits, so it is strong but not exhaustive.

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?

Two front-loaded sentences, with the primary behavior stated first and the fallback second. The prose is dense and slightly run-on, but every clause (dated/sourced, editor-written, fallback content) carries information an agent needs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description must explain returns, and it does: each step's entry with URL, date, summary and sources, plus the no-match fallback. Combined with 100% schema coverage on inputs, an agent has enough to call this correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so both `question` and `since` are already fully documented in the schema, including the changelog semantics of `since`. The description adds no format or syntax detail beyond that, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb and resource: answers a why/how question by returning an ordered chain of dated, sourced timeline entries. It implicitly separates itself from search_entries and get_entry by returning a curated chain rather than raw matches, but it never names those siblings directly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: use this for 'why/how' questions where a causal chain is wanted. The description does route the agent elsewhere in the fallback case ('how to research the answer with the other tools'), but it never states an explicit when-not condition or names the specific alternative tools.

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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