Opinion Alpha

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds return-content context (counts, bullish/bearish breakdown, five most-discussed stocks), which is genuinely useful since there is no output schema. It says nothing about rate limits, data recency/lag, or behavior when the date has no data, so it stops at adequate.

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 dense sentence that front-loads the scope ('one calendar day of the feed in a time zone') before enumerating the returned fields. Every clause carries information; there is no filler, hedging, or restatement of the title.

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 correctly takes on the burden of describing return contents, and does so concretely. The main residual gaps are the format of the 'five most-discussed stocks' entries and the no-data/empty-day behavior, which are minor for a read-only aggregate.

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 50%: 'date' is documented as YYYY-MM-DD, but 'timeZone' has only a UTC default and no description. The phrase 'in a time zone' clarifies that the aggregation window is zone-dependent and that timeZone affects day boundaries, adding some meaning beyond the schema. With half the parameters undocumented in the schema, this is baseline-but-not-compensating.

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 names the specific resource and scope: one calendar day of the feed, returning opinion counts, bullish/bearish calls, and top five discussed stocks. It is a noun phrase rather than a verb-first statement, but an agent can tell this is an aggregated daily-summary read distinct from per-opinion siblings like latest_opinions or stock_opinions. It does not explicitly name a sibling to differentiate against, keeping it short of a 5.

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?

There is no when-to-use guidance, no exclusions, and no mention of alternatives such as latest_opinions or stock_opinions for finer-grained queries. The intended use (daily rollup) is only implied by the word 'summary' and the date parameter. Nothing tells the agent when this tool is preferred over the many sibling 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=false, so the safety profile is covered. The description adds the useful detail of what fields are returned, but says nothing about pagination, ordering, or behavior of the limit parameter. With annotations carrying the safety burden, this is an adequate but not rich disclosure.

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 front-loaded sentence listing resource and return payload with no filler. Efficient, though the parenthetical field list makes it slightly dense.

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 helpfully enumerates the returned profile fields and the inclusion of latest opinions, which is a genuine plus. But the limit semantics remain ambiguous and there is no guidance on when to prefer this over overlapping siblings, so it is only minimally complete for a lookup 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 coverage is 50%: influencerId is documented ('id or handle from search') while limit only has type/default/min/max with no description. The description's 'latest opinions' loosely implies limit bounds the opinions returned, but it never states this or clarifies the relationship. It fails to compensate for the undocumented limit parameter.

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 (a single influencer) and enumerates the returned profile fields (name, platform, channel, subscribers, opinion counts) plus latest opinions. An agent can tell it retrieves one influencer's details. However, it does not distinguish itself from the siblings influencer_performance or latest_opinions, which overlap conceptually.

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?

There is no explicit when-to-use guidance, no prerequisites, and no mention of alternatives such as influencer_performance for metrics or latest_opinions for opinion-only reads. The usage is left entirely to inference from the name and sibling list.

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=false, establishing a safe, offline read. The description adds useful behavioral context by flagging that the results are a 'simulated backtest' rather than real returns, but gives no detail on computation cost, data freshness, or limitations of the simulation.

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 dense sentences that front-load the returned metrics and follow with the mode enumeration; little waste. Slight redundancy ('conviction-conviction-weighted') but overall tight and well ordered.

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 burden of describing return values falls on the description, and it delivers by listing the metrics produced. Mode parameter meaning is covered; the only gap is period semantics and the absence of any when-to-use framing, which is minor for this read-only analytical 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 only 33% (just influencerId: 'id from search'), so the description must compensate. It does meaningfully by expanding the opaque mode enum values into semantics (WLO = conviction-weighted, LS = long-short), which the schema only lists as bare codes. The period enum is left unexplained but its values (1W, 1M, YTD) are largely self-evident.

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+resource ('an influencer's simulated backtest from their public calls') and enumerates the exact output metrics (return, excess vs SPY, Sharpe, max drawdown, win rate, holdings). This is clearly distinguishable from siblings like rank_influencers, influencer, or stock_opinions without opening a schema.

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 never says when to use this tool versus alternatives such as rank_influencers or the base influencer tool, nor when to prefer one mode over another. The mode glosses describe what the modes are, not when to select them, so selection guidance is essentially absent.

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=false, so the safety profile is covered. The description adds genuinely new behavior: results are filtered to 'approved' opinions, ordered 'newest first', and span all influencers — useful disclosure beyond the structured fields.

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 front-loaded sentence with no filler; the scope and ordering come first, followed by the field list. The field enumeration makes it slightly list-heavy 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?

With no output schema, enumerating return fields is valuable. However, as a filtered list tool it omits pagination/limit behavior and any indication that direction and since act as filters — an agent must guess how to narrow results.

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 33% (only 'since' is documented with its format), so the description must compensate — but it says nothing about limit, since, or direction semantics. The word 'direction' appears only as a returned field, not as filter documentation, leaving two parameters undocumented.

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+resource ('latest approved stock opinions') with clear scope ('across all influencers') and ordering ('newest first'). It enumerates the returned fields, helping distinguish it from stock_opinions, but never explicitly contrasts the two 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?

The phrase 'across all influencers, newest first' implies this is the global chronological feed, which contrasts implicitly with a per-stock lookup like stock_opinions, but no when-to-use, when-not-to-use, or named alternative is given.

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=false, so the safety profile is covered. The description adds useful behavior beyond that (ranking is by simulated total return, sorted best first, results include key stats), but says nothing about pagination, how the limit interacts with ranking, or what happens if fewer influencers qualify.

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 front-loaded sentence with no filler; the ranking metric and sort order come first. It is efficient, though it could have spent a few words explaining the mode values without becoming bloated.

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 four parameters, cryptic enum values, low schema description coverage, and no output schema, the description should carry more explanatory weight than one sentence. It omits what 'key stats' are returned and what the mode abbreviations mean, so an agent cannot invoke it correctly without outside knowledge.

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 only 25% (only 'inverse' is documented) and the enums WLO/LO/LS and the period values are cryptic. The description mentions 'a mode and a period' but never decodes those abbreviations or explains what mode changes, leaving the agent to guess at the most consequential parameter.

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 names a specific verb and resource ('Influencers ranked by simulated total return') and states the ordering ('best first'), which clearly differentiates it from siblings like influencer or influencer_performance. It stops short of explicitly naming those siblings or contrasting itself with them, so it is clear but not sibling-differentiated.

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?

There is no when-to-use or when-not-to-use guidance. An agent cannot tell from the text when to call rank_influencers versus influencer_performance or influencer, nor whether filtering by mode/period is required. Usage is only implied by the presence of ranking language.

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=false, so safety is covered. The description adds the return-value behavior (ids for other tools), which matters since there is no output schema. It does not describe match semantics, ambiguity handling, or result limits.

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, zero filler, with the purpose front-loaded and the return-value note second. Nothing to trim.

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-param, annotation-covered lookup with no output schema, the description covers purpose, input form and output shape (ids). Adequate for correct invocation; only deeper match-behavior detail is absent, which is minor here.

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 0%, so the description must carry the burden. It does add the key semantic that the single 'query' param accepts a name OR a ticker, which the schema does not state. Still missing format/validation detail (e.g. partial matches, case sensitivity) for a param capped at 100 chars.

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: 'Find tracked influencers and stocks by name or ticker.' Clear what it does, though it does not differentiate from the sibling search_transcripts, which also performs a search-like 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?

'Returns ids to use with the other tools' implies this is the entry-point lookup that feeds downstream calls, so usage context is implied. There is no explicit when-to-use vs when-not, and no mention of search_transcripts or suggest_influencer as alternatives.

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 only supply readOnlyHint and openWorldHint, so the description carries the real behavioral burden and delivers: matching is literal substring, all word forms must be supplied in a single call, results are capped at 3 excerpts per item, links deep-link to the exact second, and non-Pro tiers inherit the opinions historyFrom window. These are non-obvious operational constraints an agent could not infer from structured data.

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?

Front-loaded with purpose and scope before drifting into variant-spelling examples and tier caveats. The example lists are dense and the final sentence about historyFrom arrives late, but every sentence carries operational information rather than 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?

There is no output schema, and the description compensates by describing the result shape (influencer, title, publish time, extracted stock calls, up to 3 timestamped excerpts) plus the instruction to quote excerpts with their link because they are partial. Nothing needed to call or interpret this tool is missing.

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 already 86%, so the baseline is 3, but the description meaningfully extends it by explaining how to populate `terms` (every spelling form in one call, with worked examples like 费半/SOX/SOXX and Simplified vs Traditional) and by clarifying the return-shaping role of `context`. It leaves `from`/`to`/`limit` formatting largely to the schema, and references a historyFrom that is not itself a parameter.

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 (search) and a precisely scoped resource (full transcript/post text across all tracked influencers, newest first), and immediately distinguishes itself from the sibling stock_opinions/latest_opinions by framing it as 'anything beyond the extracted stock opinions.' An agent can route between this and the opinion tools without opening either schema.

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?

Gives an explicit when-to-use rule ('anything beyond the extracted stock opinions') plus concrete triggering cases: indexes and sectors, macro, rates, market views, passing stock mentions. It also sets the counter-expectation that literal substring matching is in play, so the agent knows when this tool will and will not return useful hits.

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=false, so safety is covered. The description goes beyond them by disclosing result ordering ('newest first') and an unusual return trait ('each with the other stocks in the same statement'), which is genuinely useful since there is 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?

A single front-loaded sentence with the resource stated first and ordering/content details after; no filler. The parenthetical example interrupts the flow slightly but carries real disambiguation value.

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-format burden and does so for ordering and cross-stock content. The main gap is the undocumented 'limit' cap and any pagination behavior, but for a simple two-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 description coverage is 0%, so the description must compensate. It does so for ticker by giving the expected format and examples (NVDA, 600938.SS), which adds value beyond maxLength=20. The 'limit' parameter (default 20, max 50) is never mentioned, leaving half the parameters undocumented.

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?

Names a specific resource (influencer opinions) scoped to one stock, with a concrete verb-like framing ('opinions on one stock'). The 'one stock' scope implicitly separates it from latest_opinions, though no sibling is named explicitly, so it falls short of full sibling differentiation.

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 scoping phrase 'on one stock' plus the required ticker implies usage when the agent has a specific symbol, and 'newest first' hints at a recency-list use case. However, there is no explicit when-to-use vs when-not, and no alternative tool (e.g. latest_opinions) is named.

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=true and openWorldHint=false already covering the safety profile, the description adds real context: data is 'simulated', results are ordered 'best first', and the payload is win rate plus sample size. It stops short of noting limits such as no data for untracked tickers or minimum sample thresholds.

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 dense sentence with no filler, and the key identity ('one stock across tracked influencers') is front-loaded. The trailing 'for a mode and period' feels appended rather than integrated, which slightly weakens the structure.

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, and the description does cover the return contents (win rate, sample size, ordering), which is helpful. However, with 0% param coverage and an unexplained mode enum, an agent still lacks enough to select mode/period confidently – the minimum-viable bar, not more.

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 carries full burden, yet it only echoes param names ('for a mode and period') without explaining what the WLO/LO/LS mode values or the period options mean. Ticker semantics are obvious, but the two enum parameters remain opaque to the agent.

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+resource: per-influencer simulated win rate and sample size for one ticker. An agent can tell it is a per-stock, per-influencer stat lookup. It does not explicitly distinguish itself from close siblings like stock_opinions or rank_influencers, so sibling differentiation is left to inference.

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 by scope ('one stock across the tracked influencers') – an agent must infer this is the tool for comparing influencer performance on a single ticker. There is no explicit when-to-use, when-not, or alternative named (e.g. rank_influencers, stock_opinions).

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=false, so safety is covered. The description adds the data granularity (daily rows, open/high/low/close) and the 5-ticker cap, but says nothing about missing tickers, trading-day gaps, or response shape.

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 front-loaded sentence that names the resource first and appends the use case. No redundancy, though the trailing 'to set opinions against what the price did' is slightly wordy for the value it adds.

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 2-parameter read tool with read-only annotations and no output schema, the description covers the essentials: what rows come back (daily closes plus OHLC), the ticker cap, and the date semantics. Only edge cases like invalid or delisted tickers are unaddressed.

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 50% ('from' is documented in-schema, 'tickers' is not). The description restates 'since a date' and 'up to 5 stocks' but adds no format guidance the schema lacks, and never clarifies whether tickers are symbols or names. Baseline 3 for a half-covered 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 concrete verb+resource: daily OHLC price retrieval for a bounded set of stocks over a date range, plus the intended use (benchmarking opinions against price action). It does not, however, name or contrast with the nearest siblings (stock_overview, stock_opinions, day_summary), so an agent must infer the boundary itself.

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 phrase 'to set opinions against what the price did', which hints at pairing with the opinions tools, but there is no explicit when-to-use/when-not or named alternative such as stock_overview for fundamentals. Adequate but leaves the agent to infer the routing.

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 cover the safety profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false) but say nothing about outcomes. The description adds material behavioral detail beyond them: tier-dependent handling (Pro accounts are checked and added immediately), a hard rate limit ('up to 5 a day'), and a pending-review path for everyone else. That is exactly the kind of context annotations cannot express.

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, zero filler. The purpose statement is front-loaded, and the operational caveats (Pro vs. review, daily cap, confirm link first) follow in descending order of importance.

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 tells the agent what happens after the call (added immediately or queued for review) and what to do beforehand. It leaves unstated what the tool returns on success, which is a minor gap for a two-parameter submission 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 100%, so both url and note are already documented, and the description's mention of 'YouTube channel or X profile link' merely restates the url schema description. It adds no syntax, format, or constraint detail beyond the schema, so the 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 ('suggest') and resource ('a finance influencer ... to track'), plus the accepted identifier forms (YouTube channel or X profile link). An agent can tell this apart from read-oriented siblings like influencer, rank_influencers, and influencer_performance. It stops short of naming a sibling it should be preferred over, which is what would earn a 5.

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?

Gives a concrete prerequisite ('Confirm the link with the user before calling') and frames the call as a suggestion/submission rather than a lookup. It does not name alternatives or state when-not to call it (e.g. for an influencer already tracked), so it falls short of the explicit when/where-not/alternatives bar.

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