QSignals AI Stock Scores

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

readOnlyHint already covers the safety profile, so the description's added context is that this is a bulk, plan-scoped read sorted by horizon. It does not describe return fields or score freshness, but it adds useful behavioral context beyond the annotation.

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 sentences, front-loaded with the main action and followed by the key routing advice. There is no redundant or filler text.

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 read-only bulk retrieval with one fully documented parameter, the description is mostly complete. The main minor gap is that it does not describe the returned score structure or freshness, and there is no output schema to cover that.

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 only parameter, sort_by, has 100% schema description coverage with enum meanings for short, mid, and long. The description mentions sorting by the chosen horizon but adds no syntax or semantic 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?

The description states a specific verb and resource: get scores for every curated stock on the plan in one call. It also explicitly distinguishes the bulk operation from the per-stock sibling get_stock_score.

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 direct usage guidance by saying to use this instead of calling get_stock_score once per stock. That names the alternative and the condition that should select this 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?

readOnlyHint=true already tells the agent this is a safe read with no side effects, so the description's remaining burden is smaller. It does add real value by disclosing the methodology (QQQ trend and momentum) and the output value space, but says nothing about freshness/staleness of the regime, or whether the classification is cached or computed on request.

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?

A single sentence that front-loads the verb and resource, then adds scope, value set, and method in the right order. No filler, no restatement of the name.

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 carries the return-value burden and does so by enumerating the three regime labels plus the basis for the classification. For a zero-parameter read tool that is essentially complete; only the update cadence / time horizon of 'current' is left unstated.

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 takes zero parameters, so there is nothing for the description to disambiguate. The baseline of 4 applies; the description correctly signals a no-argument call by not implying any filtering options.

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 (get market regime), narrows the scope to tech stocks, enumerates the possible values (bullish, neutral, bearish), and names the underlying signal source (QQQ trend and momentum). An agent can distinguish this from get_stock_score or get_all_scores purely on subject matter — aggregate regime vs. per-stock scores.

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 rather than stated: the tech-stock/QQQ framing suggests when the tool is relevant, but there is no explicit 'use this when you want a macro read vs. get_stock_score for a single name' routing. No prerequisites or exclusions are mentioned.

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?

readOnlyHint=true already covers the safety profile, and the description adds genuine context beyond it: three horizons, a one-line reason per horizon, and the important coverage caveat that uncurated US tickers receive only a technical-only score requiring Pro. It does not mention rate limits or what a missing/unsupported ticker yields, which is the remaining gap.

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 sentences, no filler, with the primary output (score and signal per horizon) front-loaded and the coverage caveat following it. Every clause carries information an agent needs.

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 does the work of describing the return shape: a 0-100 score, a signal, and a reason line per horizon. The main omission is whether horizons are labeled fields in the response and what happens for unknown tickers, but for a one-parameter read tool this is close to sufficient.

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 single parameter already documents its format with examples (NVDA, SHOP). The description still adds meaning by clarifying that the ticker may be a stock or an ETF and that non-curated US tickers are accepted but downgraded to technical-only output.

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 ('Get the QSignals score (0-100) and signal') and bounds scope to a single stock or ETF across short/mid/long horizons. The 'one stock' framing implicitly sets it apart from get_all_scores, but no sibling is named explicitly, so differentiation is inferred 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 context is implied by the single-ticker scope and the note that any US ticker works while curated stocks get the full score. There is no explicit 'use this instead of get_all_scores when…' routing or prerequisite guidance, leaving the agent to infer when this beats the batch sibling.

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, so safety is covered and the description usefully adds the entitlement requirement ('Pro keys only'). It says nothing about tie-breaking, score scale, or which universe is ranked, which matters for a ranking tool with no output schema.

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, front-loaded sentences with no filler; the access restriction is appended rather than buried. Slightly telegraphic, but nothing is wasted.

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 low-complexity, three-parameter read-only tool with full schema coverage, the definition plus annotations plus schema are nearly sufficient. The remaining gap is what 'top pick' is drawn from and how ties or insufficient coverage are handled.

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%, with both enums fully documented (side, horizon) and limit bounded 1-10 default 5, so the schema carries the parameter burden. The description only restates the side semantics already in the enum, adding no format or default detail beyond it — baseline 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?

States a specific verb and resource ('Get ... stocks') with a clear scope qualifier ('highest- or lowest-scored ... for a horizon'). It implicitly distinguishes itself from get_all_scores and get_stock_score by returning a ranked subset, though it never names those siblings.

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?

'Pro keys only' is a useful eligibility condition, so an unentitled agent knows to stop. However, there is no explicit guidance on when to prefer this over get_all_scores or get_stock_score, leaving the agent to infer the ranking-vs-raw-scores distinction.

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?

The readOnlyHint annotation already establishes the safety profile, and the description goes beyond it by disclosing the substantive shape of the result: hit rate, top-10 vs bottom-10 spread, and benchmarking against QQQ, broken down by horizon. With no output schema present, this return-value disclosure is doing real work. It stops short of stating the lookback window or the set of horizons, which are the remaining behavioral unknowns.

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?

A single sentence, front-loaded with the core action and then unpacked into the three concrete metrics. Every clause adds information; nothing is padding.

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, read-only tool with no output schema, the description covers what the agent most needs: what the tool returns and how it is segmented. The gaps are modest but real: no lookback period, no enumeration of which horizons exist, and no hint of result size or format.

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 takes zero parameters, so there is no parameter semantics to explain and the baseline of 4 applies. The phrase 'per horizon' is worth noting: it reads as a dimension of the returned breakdown rather than a caller-supplied filter, which is consistent with the empty 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 uses a specific verb ('Get') for a specific resource (historical performance of past QSignals scores) and enumerates the exact metrics returned: hit rate, top-10 vs bottom-10 average return, and performance vs QQQ. It implicitly separates itself from siblings like get_stock_score and get_top_picks (current/forward-looking picks vs retrospective performance), though it never names an alternative 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?

Usage is only implied: 'past ... performed' signals this is the backtest/evaluation tool rather than a live-picks tool, so an agent can infer when to reach for it. There is no explicit when-to-use statement, no exclusion, and no mention of the sibling tools (get_all_scores, get_top_picks) that an agent might confuse it with.

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?

readOnlyHint=true already tells the agent this is a safe read with no side effects, so the bar is lower. The description does add real context by defining 'covered', meaning these tickers receive the full QSignals score and the AI strategic analysis, but it says nothing about what the list elements look like (ticker only, name, sector) or whether the count is fixed.

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 with zero filler; the scope of the list is front-loaded and the second sentence explains why the list matters. Nothing needs to be trimmed or moved.

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 is the only place to learn what a returned entry contains, and it doesn't say whether entries are tickers, names, or sector groupings. For a simple read-only enumeration this is a modest, not fatal, gap.

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 takes no parameters, so the schema imposes no burden. Per the zero-parameter baseline this scores 4; the description correctly adds no parameter discussion because there is nothing to document.

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 precisely bounded resource: 83 curated AI, space, energy and robotics stocks and ETFs. The card is clear about what it returns, but unlike the best examples it doesn't explicitly name a sibling (e.g. get_stock_score or get_all_scores) to explain the boundary between the covered universe and the general score set.

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: because the tickers returned are the ones that 'get the full QSignals score, including the AI strategic analysis', an agent can infer this is the tool to call before get_stock_score. But there is no explicit when-to-use, when-not-to-use, or named alternative.

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