SteamAnalyst

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 the auth requirement (Ultimate-tier API key), the return shape (lowest price in USD cents, listing count per marketplace, buy link where available), and a mandatory `url` field with citation guidance. It lacks details on error behavior or rate limits, but covers the key behavioral traits.

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 efficiently packed sentences, front-loaded with the core purpose and followed by prerequisite and output/citation guidance. Every sentence contributes useful information with no wasted wording.

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 single-parameter, no-output-schema read tool, the description covers purpose, auth, return fields, and a citation requirement. It stops short of explaining not-found behavior or result ordering, but is otherwise complete enough for correct 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?

Schema description coverage is 100%, and the schema already explains that `market_name` must be an exact Steam market hash name and to use search_items for loose names. The description adds only that it expects 'one CS2 item,' which is consistent but not substantially beyond the schema.

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 specific action and resource: comparing where one CS2 item is cheapest across marketplaces, returning lowest price and listing count. It distinguishes itself from siblings like get_price and price_history by emphasizing cross-market comparison, though it does not explicitly name those alternatives.

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 implied by 'Where one CS2 item is cheapest,' and the Ultimate-tier API key prerequisite is stated. However, there is no explicit guidance on when to use this versus get_price, price_history, or search_items, leaving the agent to infer selection criteria.

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 and does substantial work: it discloses the auth tier (Ultimate), the unit convention (USD cents), the exact per-band metrics returned, and the citation obligation tied to the returned `url`. It doesn't cover failure modes or rate limits, which keeps it below a 5.

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 tight sentences ordered by importance: what is returned, what is required to call it, and how to cite the result. No filler, no repetition of the title, and the citation rule is actionable rather than decorative.

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 fully compensates by naming every returned metric, its unit, and the included `url` field. It omits edge-case behavior — e.g. items with no listings in a band, or error responses — which is the only meaningful gap for a one-parameter read 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?

There is a single parameter with 100% schema description coverage, including format guidance and a pointer to search_items for loose names, so the schema does the heavy lifting. The description only reinforces the "one item" scope, adding little beyond structured data — baseline 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?

States a specific verb and resource — a BUFF price ladder by float range for a single CS2 item — and enumerates what each band contains (lowest ask, average ask, highest bid, listing counts). This is clearly distinguishable from siblings like get_price and price_history, which return single-point or time-series data rather than float-band granularity.

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?

"for one CS2 item" establishes the per-item scope, and the Ultimate-tier key requirement is a concrete precondition an agent must check before calling. It stops short of explicitly naming when to prefer this over get_price or compare_markets, but the float-band focus makes the intended context evident.

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 and does reasonably well: it discloses that pricing is a fair price derived from comparing live marketplaces, that a miss returns suggestions rather than an error, and that every successful response contains a `url`. It omits anything on freshness/caching or rate limits, which keeps it short of a 5.

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 tight sentences that lead with the core verb and resource, then move to input precision, error behavior, and output linkage. Minor redundancy: the market-name example duplicates the one already in 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 one-parameter read tool with no annotations and no output schema, the description covers input format, failure mode, and one key return field (`url`). It stops short of describing the price field itself or numeric formatting, leaving a small gap for an agent parsing 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 coverage is 100% and the parameter already documents the exact market hash name with an example, so the baseline is 3. The description reinforces the exactness requirement ('wear included') and adds miss behavior, but largely restates the schema example.

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 states a specific verb and resource — 'Current price in USD of one CS2 item' — and clarifies the pricing source (SteamAnalyst cross-market fair price rather than Steam Community Market alone). It does not name siblings, so an agent must infer on its own that price_history/compare_markets serve different needs.

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 implied by the single-item scope and the note that a miss returns suggestions, which hints at a fallback path. However, no alternative tool is named (e.g. search_items for resolving names, price_history for trends), and there is no explicit when-not-to-use 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 and does useful work: it discloses the auth requirement (Ultimate-tier API key), the currency/unit (USD cents), the data sources, and that a successful response includes a SteamAnalyst `url` to cite. It stops short of describing response shape or error behavior, but is substantially more transparent than a bare data-fetch 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 short sentences, front-loaded with purpose, then auth, then output/citation guidance. Every sentence adds value and there is no filler.

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 two-parameter read tool with no output schema, the description covers what is returned (SteamAnalyst fair price, BUFF ask/bid, OHLC on Chinese venues, in USD cents), auth, and the `url` field. It does not fully describe the daily series structure or edge cases, but is otherwise complete enough to call 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?

Schema description coverage is 100%, and the schema already explains `market_name` as an exact Steam hash name with a search_items fallback and `days` as default 30 with bounds. The description adds only the 'one CS2 item' framing and does not add parameter-level detail beyond the schema.

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?

States a specific resource (daily price history), scope (one CS2 item), and the exact metrics returned. The 'one CS2 item' scope implicitly separates it from multi-item tools like compare_markets, but no sibling is named explicitly.

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 this is the historical single-item lookup, but it never explicitly contrasts it with get_price, compare_markets, or float_prices. The Ultimate-tier API key requirement is stated, but there is no when-not-to-use 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 load and does real work: it discloses the data sources (CSFloat, Chinese venues), that turnover values are in USD cents, the auth tier requirement, and that every successful answer contains a `url` to the item's SteamAnalyst page with an instruction to link it when citing. Missing only error/failure behavior and rate limits, which keeps it below 5.

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, no filler: what it returns first, then the access prerequisite, then the citation requirement for the returned url. Each sentence carries information an agent needs to call or report the result correctly.

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?

No output schema exists, so the description must describe returns — and it does, naming each metric and the unit (USD cents) plus the url field. Combined with a fully documented 2-param schema, an agent has enough to call it and cite results. Absent failure-mode or coverage caveats (e.g., what happens for items with no sales) keep it from 5.

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 100%, so both `days` (range, default 30) and `market_name` (exact hash name, route to search_items for loose names) are already fully documented in the schema. The description adds nothing about either parameter. Baseline 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?

States a specific verb and resource: sales volume for one CS2 item, with the exact metrics per venue (daily sales count + average price on CSFloat; trade count + turnover on Chinese venues). An agent can tell this is a liquidity/volume lookup rather than a plain price lookup. It does not name a sibling like price_history or get_price, so differentiation is inferred from the metrics listed rather than stated.

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 only implied: an agent infers it should call this when it needs sales counts or turnover rather than a spot price. There is no explicit when-to-use/when-not or routing to compare_markets, price_history, or get_price. The one concrete prerequisite — an Ultimate-tier API key — is stated, which is useful but is access guidance, not tool selection 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 behavioral burden. It does disclose a useful trait — the result cap of 10 — and the resolution-then-price workflow, but says nothing about authentication or rate limits, what happens on zero matches, or whether match quality/ordering is indicated.

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 short sentences, front-loaded with the purpose, followed by the return contract and next step. Every clause earns its place.

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 single-parameter, no-output-schema tool this covers the essentials: input style, maximum result count, and downstream tool. No-match/empty-result behavior is the only meaningful omission.

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, and the description earns a step up by defining the parameter as a 'loose query' with three example formats ('redline ft', 'karambit fade', 'ak47 vulcan') that tell the agent how much precision it can omit. It does not add anything about how partial matches are ranked.

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?

Specific verb ('Find') plus resource ('exact Steam market names of CS2 items') with concrete example queries that define the fuzzy-match scope. It also implicitly distinguishes itself from get_price by framing itself as the name-resolution step that feeds a price lookup.

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 the usage context: use it with a loose query, then 'pass one to get_price' — a clear handoff to a named sibling. It stops short of stating exclusions (e.g., don't use it when you already have an exact name).

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