govauctions.app

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safe-read nature is covered. The description adds valuable behavioral detail: what the usage report contains (total calls, separate valuation allowance with components, reset date). It does not contradict annotations. A slight gap is lack of details on response format or error handling, but the added context is substantive.

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?

Two concise sentences. The first front-loads the core information (what is reported), the second provides a practical usage tip. No filler or redundancy. Perfectly sized for the tool's simplicity.

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 no params, no output schema, and a simple read-only function, the description fully covers what the tool does and when to use it. It mentions the return contents (total calls, allowance breakdown, reset date), satisfying the need to explain return values in the absence of an output schema. No other information is needed for such a lightweight tool.

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 tool has zero parameters, so the description carries no param burden. It appropriately references 'this key' (the auth key) as the implicit input. Baseline 4 is suitable given no params need explanation.

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 reports the current month's API usage: total calls, valuation allowance breakdown, and reset date. It distinguishes itself from sibling tools (which focus on comps, listings, trends) by being the only usage-monitoring tool. The verb 'Call it' implies the action of retrieving usage data.

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?

Explicitly states when to use: 'Call it to pace a long run instead of hitting a 429 mid-task.' This gives a concrete scenario and outcome. While no alternative tool is named, the context makes clear this is the go-to for monitoring usage. No ambiguity.

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 readOnlyHint and openWorldHint annotations already covering safety, the description adds meaningful behavioral context: it reveals the tool's purpose is to expose limitations ('which categories can never be priced') and sets expectations about data coverage. It does not contradict annotations.

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?

Two concise sentences deliver purpose, scope, and usage guidance with no unnecessary words. Every clause earns its place, making it highly efficient.

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?

The tool is simple with one parameter, no output schema, and safe-read annotations. The description covers the core information, usage timing, and limitations. It could slightly benefit from hinting at the return structure, but the conceptual description is adequate for an agent to decide and invoke correctly.

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 one parameter (country) with a clear enum and default, but the description does not explicitly mention 'country' or explain its role. It only refers to 'a market' indirectly. Since schema coverage is 0%, the description relies on the schema's self-explanatory enum, but it doesn't add extra meaning beyond the implicit market reference.

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 reports sold-price data coverage per category for a market and discloses which categories can never be priced. It uses a specific noun-phrase structure and distinguishes itself from sibling tools like get_sold_comps by focusing on coverage rather than actual comps.

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?

Explicitly advises to check this before building on the comps tools, and frames it as 'the honest answer about what we cannot do.' This gives direct when-to-use context and implies it should be used as a prerequisite before those tools.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds context about valuation against a comp archive, which is useful but not extensive. No behavioral pitfalls or additional constraints are disclosed.

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 that efficiently conveys the purpose and outputs. It is front-loaded and contains no redundant 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?

Given low complexity (1 param, no nested schemas) and annotations that cover safety, the description provides enough context for basic use. However, it lacks details on output structure or format, which would be helpful for an agent processing the response.

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 has 0% description coverage for the single parameter 'id', and the description does not explicitly describe the parameter. The mention of 'live GSA listing' hints that id is a listing ID, but this is implicit and insufficient for an agent to understand expected format or source.

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 it provides a 'Flip Score resale signal' for a live GSA listing, listing outputs: estimated value, effective bid, and discount. It implicitly distinguishes from siblings (get_listing, get_sold_comps, search_listings) by focusing on this specific metric.

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?

No explicit guidance on when to use this tool versus alternatives. The description does not mention prerequisites, limitations, or scenarios where other tools would be preferable.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating no side effects and an open world assumption. The description adds 'facts only,' which clarifies that the tool returns factual data rather than computed scores, but does not describe other behaviors like authentication, rate limits, or error cases. The additional context is minor, making a score of 3 appropriate.

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 of 10 words, conveying the essential information without any redundancy. It is front-loaded with the action and resource, making it efficient for an agent to parse.

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 simplicity (1 required parameter, no output schema, annotations present), the description is adequate but not rich. It lacks details on error handling, return structure, or what 'facts only' implies about the output. For a straightforward get-by-id tool, this is minimally viable but could be improved with more 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?

Schema description coverage is 0%, so the description must compensate. It mentions 'by its id,' which explains that the required 'id' parameter is a listing identifier. However, it does not specify the format, constraints, or source of the ID. The added value is modest, justifying a score of 3.

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 'Get one live GSA listing by its id (facts only).' It specifies the verb 'Get', the resource 'listing', and the scope 'one ... by its id'. This distinguishes it from sibling tools like search_listings, get_flip_score, and get_sold_comps, which operate on different resources or actions.

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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or any context about the suitability compared to search_listings (which might return multiple listings) or other get tools. Usage is only implied by the resource and identifier.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable behavior beyond that: the empty-series behavior when sample threshold is not met, and the consistency guarantee with get_sold_comps (never contradicts that range). No contradictions with annotations.

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 sentences, each earning its place: the first states the core output, the second contextualizes data source and consistency, the third explains a caveat. Front-loaded and free of fluff.

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?

The output behavior is well specified (monthly series, first-to-last change, empty series) given no output schema. It notes the sibling relationship and the source dataset. It does not explain how parameters like category affect results, but the core tool behavior is sufficiently complete for a read-only trend tool.

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 50%, with q, state, and country already described in the schema. The category parameter lacks a schema description and the tool description does not compensate by explaining it or any other parameter details. The description adds no parameter-specific meaning.

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 returns a month-by-month median sale price and first-to-last change, answering a specific question ('is this category softening?'). It distinguishes itself from get_sold_comps by noting it uses the same comps but for trend analysis.

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 provides context for when to use the tool (trend/softening analysis) and explicitly ties it to get_sold_comps, implying a complementary relationship. However, it does not explicitly say when not to use it or point to alternatives for specific scenarios.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the read-only nature is known. The description adds valuable context beyond that: the API key dependency changes the output from coverage-only (count, confidence, band) to actual percentile prices, and it provides a link for free keys. It does not contradict annotations.

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 sentences: the first states the core purpose and market scope, the second explains the key-dependent behavior and key acquisition. It is efficient, front-loaded, and every sentence adds meaningful information without waste.

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?

With no output schema, the description still conveys the main return values: coverage metrics without a key and percentile prices with a key. It covers the market scope and key requirement. However, it omits details about the `state` fallback (which is in the schema) and does not address error handling or the `category` parameter, leaving a minor completeness gap for such a multi-parameter tool.

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 67%, with `country` and `category` lacking descriptions. The description adds global semantic context about the API key affecting output, but it does not explain `category` or `include` beyond what the schema already says. It partially compensates for the coverage gap but not fully.

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 purpose: computing sold-price comps for a described item across the government-surplus market in four countries. It uses a specific verb ('computed') and resource ('sold-price comps'), and it distinguishes itself from sibling tools like get_sold_history or get_comp_coverage by focusing on comparative pricing for a described item.

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 provides clear context about when to use the tool: when you need sold-price comparables, with the caveat that an API key is required to get actual percentile prices versus coverage-only without one. However, it does not explicitly name alternatives or state when NOT to use this tool, so it misses the full 'when-not/alternatives' bar.

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?

Annotations already provide readOnlyHint=true and openWorldHint=true, but the description adds behavioral nuance: it is query-anchored and cannot list the archive. It also describes the return fields (title, price, sale month/state, range), which adds context beyond the annotations without contradicting them.

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 sentences. The first sentence states what the tool does and the output fields; the second provides usage guidance and a constraint. Every word earns its place, and it is front-loaded with the core purpose.

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 5 parameters and no output schema, the description covers the key behavioral aspects: what it returns, when to use it, and its query-anchored limitation. It lacks elaboration on the 'range' calculation or pagination, but the core context is sufficiently complete.

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 moderate (60%), with descriptions for q, limit, state, and country, but category has none. The description only reaffirms that q is an item description ('describe an item') and does not add meaning for the other parameters. This is baseline 3 because the schema already does most of the work.

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 returns individual archived sales with specific fields (title, final price, sale month/state) and the range they produce. It distinguishes from siblings by positioning it as 'the evidence rather than just the number', which differentiates it from tools like get_price_trend or get_sold_comps.

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 explicitly says 'Use when you want the evidence rather than just the number' and notes that the tool is 'Query-anchored — describe an item; there is no way to list the archive.' This gives clear usage context and a limitation, but does not name specific alternative tools, so it falls short of a 5.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe read with changing data. The description adds the useful 'facts only — no images' limitation and the distinction between sanctioned federal sources vs full-market comps. It doesn't disclose pagination, result count behavior, or potential timeouts, but with readOnlyHint already provided, some burden is relieved.

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 sentences cover purpose, scope/constraints, and data-domain distinction with zero filler. Each sentence earns its place, front-loaded with the core action and following with useful scope qualifiers.

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 search tool with 11 parameters, no output schema, and only 36% schema coverage, the description does moderate work. It explains what data is searched, the key distinction between raw listings and comps, and the filter dimensions. Given readOnlyHint/openWorldHint annotations and the absence of an output schema, the description is reasonably complete, though it doesn't describe return structure or result count expectations.

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 only 36%, and 11 parameters are undocumented in the description's prose. However, the zip, radiusMiles, q, state, category, priceMin/Max, and source/country enums are described in the schema itself. The description adds filter dimensions at a high level but does not add per-parameter meaning beyond what the schema conveys. With low coverage and no param descriptions in the tool text, 3 is appropriate.

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 ('Search') plus resource ('live GSA federal-surplus auction listings'), and clearly scopes the domain ('facts only — no images'). It lists concrete filter dimensions (state, category, keyword, price, distance) and distinguishes raw listings from sold-price comps, which separates it from sibling sibling get_sold_comps. This clearly differentiates from siblings get_listing (single listing) and get_flip_score (scoring).

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 states clear context: it searches live GSA auction listings, filters by multiple dimensions, and its scope is 'facts only — no images.' It clarifies that raw listings cover sanctioned federal sources while sold-price comps span the full market, which implies when one might need the sibling get_sold_comps instead. However, it doesn't explicitly name alternatives or say 'when not to use,' so a small gap remains.

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