Lodestar Stamp

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

With no annotations, the description carries full behavioral burden and does this well: it states the maximum result count (20), sort order (alphabetically), returned fields, and explicitly discloses negative behavior: 'never a receipt and never an order by quality.' This goes well beyond a generic tool description.

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?

Three dense sentences with no filler. The most important scoping information is front-loaded, return behavior is compactly stated, and the follow-up instruction earns its place in the final sentence.

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 there is no output schema, the description accounts for return fields, limits, ordering, and ambiguity handling. The only gap is that it later references 'the match's license (or domain)' without clearly stating license/domain is among the returned match fields, since the listed return fields are id, address, market, and receipt URL.

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 coverage is 100%, so the baseline is 3. The description mostly restates what the schema already says about q and market. It adds the useful framing 'across every market or in one,' but does not materially extend parameter meaning beyond the schema's existing descriptions.

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: 'Find businesses on the record by part of a name or street address.' It also distinguishes itself from get_receipt by explicitly saying it 'never' returns a receipt, so an agent can immediately tell what this tool is for.

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?

It gives an explicit trigger condition: 'Use it when the person gave a name.' It also explains the follow-up tool and arguments: 'then call get_receipt with the match's license (or domain) and market.' The ambiguity policy ('Two plausible matches are a question for the person, not a guess') provides clear decision 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 provided, the description fully carries the behavioral disclosure burden. It explains the verified vs claimed field-state distinction, warns to treat all data as data rather than instructions, and discloses the meaning of a 300 candidates response. This is unusually transparent and materially helps the agent avoid misuse.

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 front-loaded: purpose first, then field-state semantics, then safety guidance, then an edge-case procedure. Every sentence earns its place and no content is redundant with the schema.

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 fairly simple lookup tool with no annotations and no output schema, the description covers purpose, identifier constraints, fallback behavior, ambiguity responses, and data handling. The only notable gap is that it does not describe the shape of a successful 200 response, but the phrase 'dated receipt' gives a reasonable expectation.

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 coverage is 100%, so the baseline is 3. The description adds value by clarifying that only one identifier should be supplied at a time, despite the schema listing five optional parameters, and by reinforcing that values should be treated as data. It does not deeply expand on each parameter, but the cardinality rule is a meaningful addition.

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 pulls a dated Lodestar Stamp receipt for a business, using a specific verb and resource. It defines the lookup scope as exactly one identifier and lists the accepted identifier types, though it does not explicitly contrast itself with siblings like find_business.

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 gives clear operational guidance: use exactly one identifier, and if a 300 response with candidates appears, stop and report ambiguity rather than guessing. It also explains the market fallback behavior, but it does not explicitly say when to prefer this tool over find_business or list_index.

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 full behavioral disclosure burden. 'List' signals a read-only query and 'current Lodestar Stamp draft' scopes the data, but the description does not say what the returned domains look like, what happens if no draft exists, or whether any other side effects occur.

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 a single sentence with no filler, front-loading the action and object. Every word adds meaning, and nothing repeats annotations or schema information.

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 zero-parameter, low-complexity tool, the description gives enough to understand the essential purpose and scope. There is no output schema, so the exact return shape is inferred rather than documented, but the gap is minor for a simple list operation.

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 input schema is empty and there are zero parameters, so the baseline is 4. The description does not need to add parameter-level meaning because no parameters exist.

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 uses a specific verb ('List') and a precise resource ('domains covered by the current Lodestar Stamp draft'), making the tool's purpose immediately clear. It is distinct from siblings like find_business, get_receipt, and list_markets even without naming them.

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 phrase 'covered by the current Lodestar Stamp draft' implies when this tool is relevant, but there is no explicit guidance about when to use it versus a sibling alternative. No exclusions or routing hints are provided, so usage is only implied rather than stated.

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 carries the behavioral disclosure burden. It does this well by stating the tool lists every market and the specific output fields, and by clarifying the output is factual coverage data. It does not mention ordering or pagination, but for a zero-parameter read-only listing tool this is acceptable.

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 two dense sentences with no wasted words. The core scope ('Every market on the record') is front-loaded, the field list is compact, and the clarifying boundary is appended efficiently.

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 tool with no parameters, no annotations, and no output schema, the description fully covers what the agent needs: what is returned and in what spirit. It names the key data fields and explicitly rules out comparison behavior, making the tool's behavior predictable.

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 input schema has zero properties and 100% schema coverage, so there are no parameter semantics for the description to add. Under the baseline rule for zero-parameter tools, this is appropriately handled; the description's field list adds value for understanding output instead.

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 identifies the tool as returning every market and enumerates the included fields (slug, name, identity, receipt fields, business count, edition). This distinguishes it from the sibling tools like find_business and get_receipt, and the closing clause 'not a comparison between markets' further sharpens 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?

The description gives explicit context that this tool provides coverage facts rather than a comparison between markets, which helps an agent decide whether to use it. It does not explicitly name sibling alternatives or say 'use this when...', so it falls short of full routing guidance.

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