embedded-docs

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

With no annotations provided, the description must fully disclose behavioral traits. It does mention that effort is an 'internal diagnostics tag' and should be left unset, adding useful context. However, it does not explicitly state that the operation is read-only or non-destructive, nor does it describe any side effects or prerequisites beyond the implied search hit. The word 'expand' suggests retrieval, but the description could be more explicit about safety and behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured. It front-loads the primary action in the first sentence, then provides a clean argument list. Every sentence is informative, with no fluff or repetition.

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

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

For a simple, focused tool, the description is quite complete. It explains the core function, the required parameter's origin, and the internal nature of the optional parameter. The output schema exists, so return-value details are not required. It could be slightly more complete by explicitly stating how this tool relates to request_document, but overall it covers the essential context.

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

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

The schema has 0% description coverage, but the description compensates by explaining both parameters: result_id is sourced from a search_docs hit, and effort is an internal diagnostics tag that clients should not set. This adds meaningful semantic value beyond the raw schema, though it lacks examples or format details.

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

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

The description clearly states the tool's function: 'Expand a previous search hit to its full verbatim section (markdown).' This uses a specific verb (expand) and resource (search hit), and distinguishes it from siblings like search_docs and request_document by focusing on retrieving the full context of a specific hit.

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

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

The description implies when to use it: after a search_docs hit, by referencing 'previous search hit' and 'result_id from a search_docs hit.' It provides clear context for the intended workflow but does not explicitly mention alternatives or when not to use it, so it stops short of full exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden of behavioral transparency. It discloses that requests are human-reviewed and typically fulfilled within 24 hours, and clarifies the tool does not perform searches. Although it doesn't describe side effects or idempotency, the given context (review process, timeline) is valuable and above the bare minimum.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and efficiently structured. The first sentence states the purpose, the second gives the usage condition, the third provides parameter content, and the fourth describes the outcome. Every sentence earns its place with no redundancy.

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

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

For a simple request tool, the description covers the core aspects: what it does, when to use it, what to provide, and what happens after. The main gap is the undocumented `effort` parameter, which the agent would not know about, and potential error cases are not mentioned. However, the tool is simple and the output schema may clarify return values.

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

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

Schema description coverage is 0%, so the description must compensate. It thoroughly explains the `request` parameter ('Pass ONE string with as much as you know...'), but entirely omits the `effort` parameter. The phrase 'Pass ONE string' may misleadingly imply there is only one parameter, leaving the agent uninformed about `effort`.

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

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

The description clearly states the action ('Request that a document be ADDED to the corpus') and explicitly contrasts with search_docs ('This does NOT search; use search_docs for that'), making it distinct from the sibling tool. It also lists concrete document types it covers, leaving no ambiguity about its purpose.

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

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

Usage is precisely defined: 'use this when search_docs returns no confident match' and explicitly excludes the search use case. It provides a list of acceptable document categories and instructs on what to include in the request, giving strong guidance on when and how to use the tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description fully carries the disclosure burden. It states the tool returns verbatim, page-cited text, never fabricates, returns 'no confident match' on a miss, and is 'cheap and safe to call several times per task.' This goes well beyond basic operation details and gives the agent a clear trust model.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very long, but the density is largely justified by the broad corpus coverage and detailed usage triggers. The first sentence states the core purpose, though the massive trigger list appears later in the paragraph, making it less front-loaded than ideal. Every section adds value, but the structure could be tightened with headings or a more prominent 'when to use' callout.

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

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

Given the tool's complexity and the presence of an output schema, the description covers all necessary decision points: what the corpus contains, when to use it over alternatives, what behavior to expect, and how to treat misses. The only minor gap is parameter-level detail, but that does not hinder tool selection or basic invocation.

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

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

The schema has 0% description coverage, so the description must compensate. It does elaborate on the query parameter by listing many example search triggers (hex literals, protocol codes, register names, etc.). However, it gives no explanation of the 'limit' or 'effort' parameters, leaving their semantics to be inferred from defaults and names alone.

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

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

The description opens with a specific verb and resource: 'Search the indexed embedded / firmware / hardware reference corpus; return verbatim, page-cited evidence.' This clearly distinguishes it from siblings by naming the corpus and the nature of the returned evidence, and the later list of concrete search triggers reinforces what the tool does.

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

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

The description explicitly instructs when to use the tool ('Call search_docs the moment you see any of these - before answering from memory and before any web search'), provides comprehensive examples of triggering content, and names the alternative (web search) with reasoning for preference. It also explains behavior on no match, making the usage decision unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.