koalcheck

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

With no annotations, the description carries the full burden. It discloses that it groups analysts into bull/bear/neutral camps, returns paraphrased points, and provides balanced analysis rather than a recommendation. Yet it omits details like error handling, prerequisites (e.g., valid handles), or the effect of limit_per_analyst, leaving some behavioral aspects undisclosed.

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 paragraphs with the core purpose front-loaded in the first sentence. It is not overly verbose, though the second paragraph could be tightened. The structure is logical, starting with the primary action and then elaborating on the output and analytical stance.

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?

An output schema exists, so return structure is not needed in the description. The description covers the main purpose and behavior, but the omission of limit_per_analyst is a notable gap. It also does not mention any prerequisites for handles, making the description incomplete for full self-contained usage.

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 explain parameters. It implicitly covers ticker and handles via 'ONE ticker' and 'named analysts,' but it completely fails to mention limit_per_analyst, leaving its meaning and default unclear. This is a significant gap given the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action: 'Compare several analysts' takes on ONE ticker and surface the clash.' It specifies the resource (analysts' takes on a ticker) and the outcome (surface the clash). It differentiates from sibling tools like analyst_views or analyst_profile by focusing on multiple analysts and debate, making its purpose unambiguous.

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 usage for multi-analyst comparison by saying 'several analysts' and 'ONE ticker,' and adds 'Balanced analysis, not a recommendation' to clarify its analytical nature. However, it does not explicitly mention alternatives or when not to use it, such as when a single analyst's view is needed, which would point to analyst_views or analyst_profile.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns, that it relies on previously fetched profile signals, and that it may surface same-name accounts. It does not mention potential side effects, auth requirements, or data freshness, but as a read-style lookup tool the disclosed behavior is reasonably transparent.

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 longer than average but front-loads the core purpose with the 'ANTI-IMPOSTOR' framing and then packs in return details, a concrete example, and a prerequisite. Some decorative phrasing could be trimmed, but every substantive point 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?

The tool has one parameter, an output schema, and no annotations; the description covers the prerequisites, the main return values, and the disambiguation use case. It is missing an explicit handle format clarification, but otherwise an agent has enough context to invoke it 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 required 'handle' parameter with no description coverage, and the description does not explicitly explain how to format the handle or whether to include the '@' prefix. The example '@aleabitoreddit' hints at the account identifier but leaves ambiguity. Because schema coverage is 0%, the description needed to compensate with explicit parameter guidance and only partially does.

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 ('Returns') and a clear resource: the account's authenticity signals, credibility score, permanent account id, and same-name accounts. It explicitly frames the tool as an anti-impostor check, which distinguishes it from sibling analyst tools like analyst_views or analyst_track_record. The use-case example ('tell the real @aleabitoreddit from a 1-tweet impostor') makes the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a clear prerequisite: the analyst must have been fetched once via analyst_views. It also illustrates when an agent would want this tool, such as when a user searches a name and needs disambiguation. It does not explicitly list when not to use it, but the prerequisite and use-case provide enough context.

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

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

With no annotations provided, the description carries full responsibility for transparency. It states this is a 'PURE READ of already-stored data (no live fetch, no X cost)' and includes a security warning about untrusted third-party content. This informs the agent about side effects and safety beyond what annotations would have covered. It does not detail pagination or error behavior, but for a read-only retrieval the core transparency is 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?

The description is front-loaded with an engaging question and a clear output list, then efficiently contrasts with alternatives, gives a prerequisite, and adds a security note. Each sentence serves a distinct purpose. It is longer than a minimal description, but the structure keeps it readable and well-organized without 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?

Given the tool's moderate complexity and the presence of an output schema, the description covers the essential aspects: what it returns, how to prepare the input, and a security caveat. The only notable gap is the undocumented 'limit' parameter, but that is a minor omission given the tool's straightforward nature. Overall, an agent can use this tool correctly with the provided guidance.

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 explain both parameters. It clarifies that 'handle' must be a resolved @handle (via resolve_analyst), which adds meaning. However, it never explains 'limit' (its effect, range, or default). Since limit has a default of 15, the omission is less critical, but the description still fails to fully compensate for the schema's lack of 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 opens with a concrete question and then enumerates the exact output fields (ticker, direction, date, link, snippet). It explicitly distinguishes itself from analyst_track_record (accuracy) and a live timeline pull, making the tool's purpose unmistakable. The specific verb phrase 'most-recent STORED calls' leaves no ambiguity about what the tool returns.

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 a clear alternative (analyst_track_record) and a prerequisite (resolve_analyst first). It also contrasts with a live timeline pull. It does not exhaustively cover all siblings (e.g., analyst_views), but the key decision-relevant context is given, so an agent can choose this tool appropriately.

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

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

With no annotations, the description carries full responsibility. It discloses that it resolves handles to permanent IDs, extracts only scorable calls, uses benchmark-adjusted abnormal returns, and includes a sample-size caveat. It also clarifies it is analytics, not advice. While it doesn't mention rate limits or authentication, it covers the key behavioral aspects and preconditions.

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 well-structured paragraph that front-loads the core question, then provides process details and caveats. It is somewhat long but each sentence adds value, explaining the methodology, output, and required preconditions. No fluff, though it could be tightened.

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 complex scoring tool with an output schema, the description explains the inputs, process, prerequisites, and output structure. It covers the need for stored tweets, maturation windows, and the nature of the analytics. It doesn't describe the output fields in detail (likely in the output schema) but gives a good overview. Missing info on refresh and error handling is minor.

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 explain the parameters. It clarifies that 'handle' is the analyst handle to resolve, but does not mention 'refresh' at all. The refresh boolean is left unexplained, leaving the agent to infer its purpose. This is a partial compensation for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a clear, specific question ('How ACCURATE has this analyst been?') and then details the exact process: resolves handle, extracts scorable calls, evaluates against historical prices at multiple horizons, and returns a scorecard. It is a distinct analytics tool, not a generic listing, and its purpose is unambiguously differentiated from investment advice or raw data retrieval.

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 prerequisites: it needs stored tweet history (calls out analyst_views to populate) and matured time windows for scoring. It also warns that recent calls show as pending. It does not explicitly name alternative tools for when not to use it, but the analyst_views prerequisite effectively routes the agent to the correct sequence.

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

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

With no annotations provided, the description carries the behavioral disclosure burden. It explains what the tool returns per analyst (overall stance, mentioned tickers, recent points) and adds an important presentation constraint: summarize rather than reproduce tweets verbatim, and attribute each view. This is meaningful behavioral context beyond the input 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?

The description is front-loaded with the core purpose, then provides a compact example, return summary, and presentation rule. Every sentence adds useful information, and there is no filler or repetition of the parameter 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 reasonably simple fetch tool with an output schema available, the description covers the essential input semantics, return content, and how to present results. It misses only minor items like the limit_per_analyst parameter and explicit routing against sibling tools, so it is almost complete but not exhaustive.

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 explains 'handles' with a concrete example and 'ticker' as optional, but it does not mention 'limit_per_analyst' at all. The missing parameter is partially self-explanatory from its name, but the description does not fully bridge the documentation gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific, actionable statement: 'Fetch the recent views of specific X analysts/KOLs by handle.' This clearly names the resource (analyst views), the action (fetch), and the key input (handle), and it distinguishes the tool from siblings like analyst_recent_calls by focusing on 'views' rather than calls.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for when to use the tool: when the user names specific analysts/KOLs, with an optional ticker focus. It does not explicitly contrast with alternatives or state when not to use it, but the usage context is strong enough that an agent can infer the right selection.

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?

No annotations are provided, so the description carries the burden. It discloses key behavioral traits: keyless access, point-in-time data by filing date, free, and not advice. However, it does not mention rate limits, authentication requirements, or error behavior, which are relevant for a data-fetching tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the key data points and usage scenario. The opening rhetorical question adds a bit of flair but does not harm clarity. It is efficient and readable.

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?

An output schema exists, so the return structure is documented elsewhere. The description covers what data is included and the point-in-time nature. However, the as_of parameter is not explained, which is a notable gap for an optional parameter that likely controls the historical point-in-time behavior.

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 only two parameters (ticker and as_of) with no descriptions, and schema description coverage is 0%. The description mentions 'point-in-time by filing date' which hints at as_of usage, but does not explicitly explain the parameter's format, semantics, or default behavior. Ticker is self-explanatory but still 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?

The description states exactly what the tool provides: SEC fundamentals including annual revenue, YoY growth, net income, and margins from SEC XBRL, and positions it as a check on analyst narratives. It is specific about the resource and data fields, making it distinguishable from sibling tools that cover analyst views, social sentiment, or quotes.

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 a clear use case: to verify whether an analyst's 'accelerating growth' narrative matches reported numbers. However, it does not mention alternatives or when not to use this tool, leaving the agent to infer from the sibling list without explicit 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 burden, and it delivers meaningful context: SEC EDGAR source, open-market purchases/sales under Section 16, point-in-time/keyless access, and free. It does not disclose freshness or rate-limit details, which keeps it from 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?

The core question is front-loaded, and the analyst-corroboration example earns its place as usage guidance. Minor marketing/disclaimer text ('Free. Analytics, not advice.') adds little but does not seriously hurt navigability.

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 two-parameter tool with an output schema, the description covers what, why, and when to use it. The unexplained since_days parameter is a real gap, and there is no mention of lookback window semantics, so overall completeness is adequate but not excellent.

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 no parameter descriptions (0% coverage), and the description only implies the ticker via 'this ticker'. 'since_days' is never mentioned or explained, so the description fails to compensate for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'are company INSIDERS buying or selling this ticker?' It goes on to define the data source (SEC Form 4) and subjects (officers, directors, 10% owners), making it unmistakable and distinct from the analyst- and sentiment-focused sibling tools.

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 supplies an explicit use case: corroborate analyst sentiment with insider trading, including the bullish+buying and bullish+dumping scenarios. It stops short of naming alternatives or stating when not to use this tool, so it does not reach 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?

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so thoroughly: it reveals that this is a metric-based after-the-fact measurement, explains the Wilson-CI thresholds, clarifies the fade view, and explicitly warns that past accuracy does not imply future results. This is strong transparency beyond the bare 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?

The description is information-dense but well organized: a one-line thesis, a compact metric summary, a bulleted view breakdown, and a horizon note. Every sentence serves a purpose, and the critical scoping/interpretation guidance is front-loaded.

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 presence of an output schema, the description does not need to explain return values. It covers all important behavioral aspects, defines the custom views, clarifies the canonical horizon, and provides appropriate caveats. Only the trivial `limit` parameter is under-specified, and that does not meaningfully hurt completeness.

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 for the schema's silence. It adds rich semantics for `view` and `horizon`, including allowed values and their meanings, but `limit` is never described. Since two of three parameters are well covered and the remaining one is a simple optional integer, this is strong but not perfect.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a specific resource — the daily honest-track-record leaderboard — with a concrete verb ('Reads') and defines what it measures (21d hit-rate, Wilson CI, vs SPY). It is unambiguous about the tool's function, but it does not explicitly differentiate from siblings like analyst_track_record or analyst_views, so it stops short of a full 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?

The description gives excellent selection context for the view and horizon parameters, explaining what 'proven', 'fade', and 'cross_regime' mean and interpreting them correctly (e.g., fade is a contrarian signal, not a buy list). However, it does not state when to use this tool versus sibling analyst tools, so the guidance is clear but lacks explicit alternatives/exclusions.

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 burden of behavioral disclosure. It mentions point-in-time by filing date, item code mapping, free access, and a disclaimer ('Not advice'). However, it does not specify return format, pagination, rate limits, or explicitly label the operation as read-only. It adds useful context but lacks some behavioral details.

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 with no redundant text. It front-loads the purpose, gives concrete examples, and includes access notes. The star and emoji are decorative but do not detract. Every sentence adds 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?

For a simple two-parameter tool with an output schema, the description covers the purpose, key behaviors, and access. It does not need to explain the return format since the output schema exists. Minor gaps include no mention of result limits or pagination, but these are not critical for basic usage.

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 for parameter meaning. It does not explicitly explain 'since_days' or 'ticker' beyond what their names imply. The description mentions 'recent' and 'point-in-time' which hints at since_days, but does not define it. No format or example values are given, leaving the agent to infer.

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 function: fetching recent material events from SEC 8-K filings. It lists specific event types (earnings, exec changes, M&A, restatements) and maps item codes to plain language, making it distinct from other tools that might cover broader fundamentals or sentiment. It is not a tautology and provides concrete examples.

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 use for analysts looking for catalysts and mentions 'point-in-time by filing date', but it does not explicitly state when to use this tool versus alternatives like fundamentals or quote. There is no exclusionary guidance or mention of cases where this tool would be inappropriate. The context is clear but not directive.

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?

No annotations are provided, so the description carries the full burden. It discloses the authentication requirement (OAuth or kc_ member key), per-connection scoping, daily quota pooling, and that only the caller's own account is reflected. It does not explicitly claim read-only behavior, but the zero-parameter schema and 'tells you' phrasing strongly imply a side-effect-free read.

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 opens with a bold, one-line summary of the tool's output, then expands with exactly the details an agent needs: the quota pool, uncapping for DB reads, the personal scope, and authentication methods. No sentence is redundant; it is appropriately sized for a zero-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no parameters and an output schema present, the description covers all needed behavioral context: what data is returned, how it is scoped, and how to be recognized as a member. The only minor omission is explicit error behavior for unauthenticated calls, but the sign-in instruction implies 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 tool accepts zero parameters, so there is no parameter semantics to document. Schema description coverage is 100% by definition, and the description adds useful meaning about what the returned data represents: membership tier, quota used, and uncapped DB reads. The zero-parameter baseline is 4.

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: it returns the authenticated account's membership tier and today's live-fetch quota. It explicitly scopes the result to 'YOUR account' and excludes 'global / operator data', distinguishing it from the sibling data tools.

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 when to use the tool: it reports the shared daily pull pool used by real-time search, view refresh, debate, and x-sentiment, and notes that DB-read tools are uncapped. It tells the agent that quota checks are relevant for live-fetch operations, but it does not explicitly name alternatives or provide a direct 'use this when' statement.

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 behavioral burden and does so well. It discloses the approximate 15-minute delay, the free yfinance source, that it does not consume the live-fetch quota, and that valuation metrics are 'best-effort.' These caveats materially shape an agent's expectations and are beyond what structured data could convey.

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 summary line front-loads the tool's purpose, the returns list is compact and information-dense, and the caveats about delay, quota, and pairing are each worth their sentence. No filler is present.

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 a single obvious parameter, an output schema that documents return values, and a clear set of sibling tools, the description is complete. It covers data freshness, cost/quota behavior, reliability caveats, and suggests complementary tools, so an agent has everything needed to invoke it 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?

There is only one parameter, 'ticker,' and the schema provides no description (0% coverage). The description says the tool is 'for a ticker' and mentions yfinance, which lightly hints at the ticker format, but it does not explicitly specify the expected symbol format or normalization rules. It adds marginal value over the parameter name.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb-plus-resource statement: 'Live price + volume + turnover (换手率) + market cap + basic valuation for a ticker.' It then enumerates the exact returned fields, making it clear this is a quote/basic market data tool and distinct from the analytical/sentiment siblings like score_ticker and fundamentals.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context on when to use it: for a quick, free quote that does not consume the daily live-fetch quota, and it advises pairing with score_ticker / fundamentals / analyst_track_record for a fuller picture. It stops short of explicitly stating when not to use this tool versus alternatives, but the positioning is clear.

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 burden of behavioral disclosure. It adds useful context about the data source ('Backed by ApeWisdom'), the 24h timeframe, and the interpretive caveat ('often a contrarian/risk flag, not a buy signal'). However, it does not state whether the operation is read-only, what counts as a mention, or any access/rate considerations.

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 short and front-loaded, with the first sentence delivering the core purpose and the second adding interpretation. Both sentences earn their place, though the interpretive guidance could arguably be tighter.

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?

An output schema exists, so return-value detail is not the description's job. The description covers the data source and interpretive meaning, but it lacks sibling differentiation and any mention of the time window beyond '24h momentum' or how the rank is scoped. It is adequate but leaves selection context incomplete.

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 only one parameter, 'ticker', and schema description coverage is 0%. The description merely repeats the concept of 'a ticker' without adding format, examples, or constraints. Given the total lack of schema descriptions, the description should have compensated with at least a symbol format hint or 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 resource ('WSB retail-attention for a ticker') and the core outputs ('mention count, rank, and 24h momentum'). It is clear and informative, though it does not explicitly contrast with siblings such as wsb_trending or ticker_social_sentiment.

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 use case is implied: use when you need Reddit/WSB retail-attention metrics for a specific ticker. However, there is no explicit guidance on when to prefer this tool over sibling tools like ticker_social_sentiment or wsb_trending, and no exclusions are stated.

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

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

With no annotations to carry the safety/behavior profile, the description compensates well: it discloses that signals are weighted by track record, that absent components are renormalized rather than treated as neutral, that as_of bounds results point-in-time, and that the output includes component breakdown, coverage, and confidence. It also notes this reads stored/free data and is not advice.

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 dense and front-loaded with the core composite concept, and nearly every sentence carries operational value. It is slightly cluttered with branding/preamble ('★ COMPOSITE (Pro)', 'FREE', 'Not advice'), and the EXPLAIN_MODE reference assumes external context, which keeps it from a perfect conciseness score.

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 an output schema exists and there are only three parameters, the description covers what an agent needs to invoke the tool correctly: parameter semantics, behavior, data sources, prerequisite steps, and output scope. There is no material missing context for a tool of this complexity.

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%, but the description effectively explains the non-obvious parameters: as_of is a YYYY-MM-DD point-in-time bound, and audience accepts 'retail' or 'pro' with a default from EXPLAIN_MODE. The ticker parameter is self-evident from the tool name and description, so the compensation is adequate.

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, non-tautological purpose: compute a single composite sentiment score for a ticker by blending weighted analyst calls, SEC insider activity, and StockTwits retail sentiment. It clearly distinguishes itself from sibling single-source tools by emphasizing that it is a composite with a transparent breakdown.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a concrete prerequisite ('run analyst_views on your analysts first') and a clear sense of when this tool is appropriate: when a blended, free composite score is needed rather than a single data source. It does not explicitly name alternatives or say when not to use it, so it stops short of full explicit 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?

With no annotations provided, the description must carry the behavioral disclosure burden. It reveals that this is a read-only screener and adds notes like 'FREE data' and 'Analytics, not advice,' which are useful. However, it does not disclose behavior around output format, rate limits, error handling, or the meaning of min_coverage/audience, leaving notable behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and well-structured, with the main purpose front-loaded and the source/mode options laid out clearly. Minor extra content like 'FREE data' and 'Analytics, not advice' adds little operational value, but it does not significantly bloat the 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 tool with 6 parameters, 0% schema coverage, and no annotations, the description covers the core choices but omits several meaningful parameters and their interactions. The presence of an output schema helps, but the agent still lacks explanation for audience and min_coverage, and whether source and universe are mutually exclusive. This makes the description only partially 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 description coverage is 0%, so the description must compensate. It adds real meaning to source, mode, and universe, which are the core parameters. But limit, audience, and min_coverage are not explained at all, and those names alone do not fully clarify expected values or behavior, especially audience and min_coverage.

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 job: 'Rank a universe of tickers by the composite score.' It mentions specific resource types and modes, making the purpose understandable. However, it does not explicitly differentiate itself from sibling tools such as score_ticker or trending_tickers, so it lacks the explicit sibling distinction needed for 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?

The description gives actionable guidance on choosing source ('analysts', 'trending', or explicit universe) and mode ('bullish' vs 'divergence'), which tells the agent when each option is appropriate. It does not explicitly state when to choose this tool over alternative sibling tools, but the source/mode guidance is clear enough context for invocation.

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

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

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals non-obvious behavior: sentiment scoring, result summarization, and avoidance of verbatim echoing. It could add more about result shape or rate limits, but the output schema reduces that need.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense sentences with no filler. The query examples are front-loaded, followed by purpose, alternative routing, and output expectations — all useful and directly actionable.

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 low complexity, the presence of an output schema, and no annotations, the description covers purpose, usage context, parameter syntax, and output behavior. The only notable gap is inline semantics for limit/latest, which are reasonably inferable from their defaults.

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 explains the `query` parameter well with operator examples, but gives no meaning or guidance for `limit` or `latest`, which are only visible as typed defaults in 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 uses a specific verb ('Search X'), names the resource, and gives concrete query examples. It also differentiates itself from siblings by noting it is a general-purpose search with sentiment scoring, unlike analyst_views.

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 states when to prefer analyst_views ('when the user cares about specific accounts'), giving the agent a clear routing condition. It also instructs how to present results: summarize, don't echo tweets verbatim.

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 of behavioral disclosure. It mentions that the tool is free and not advice, and clarifies it reports volume not interest. However, it does not state whether the operation is read-only, any rate limits, error behavior, or data freshness. The description adds some context but leaves critical behavioral aspects undocumented.

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 with no filler. The core purpose is front-loaded, and the clarification about volume vs interest is added efficiently. It is appropriately 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?

The description covers the core purpose, clarifies the volume/interest distinction, and notes the tool is free. Given an output schema exists, the return format may be specified there. However, it lacks details on edge cases (e.g., invalid ticker, what 'last few trading days' means exactly, and whether 'days' is inclusive). It is adequate for a simple data tool but not exhaustive.

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 explain parameters. It implicitly refers to 'ticker' and 'last few trading days' for the 'days' parameter, but does not explicitly name or describe the parameters, their types, or the default value for 'days'. The description adds minimal parameter-level value beyond the schema itself.

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 provides SEC/FINRA short-sale volume percentage for a ticker over the last few trading days. It explicitly distinguishes itself from short interest, which is a common point of confusion. The verb 'short-sale VOLUME %' is specific and unambiguous.

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 guidance is given on when to use this tool versus other sibling tools. The description offers an interpretation of heavy short volume (selling pressure or squeeze setup) but does not state conditions for selection or mention alternatives. An agent would have to infer when to call it.

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 of explaining behavior. It discloses that posts are recent and include author-tagged labels, but it does not mention pagination, sort order, time range boundaries, or any limitations. The behavior is simple enough that this is adequate, though not rich.

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, tightly written sentence with no filler. The core subject, target, and differentiator (author-tagged bull/bear labels) are front-loaded efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple schema and presence of an output schema, the description is minimally viable. However, it omits usage context relative to sibling tools and does not explain the limit parameter's role, leaving an agent to infer important selection and invocation details.

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 clarifies that 'ticker' refers to the StockTwits posts target, but it says nothing about 'limit' beyond the schema's default value. The semantic of 'limit' is left entirely to the parameter name and default.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the resource (ticker) and the returned content (recent StockTwits posts with bull/bear labels). It does not use a strong verb like 'retrieve' or 'get,' and it does not explicitly distinguish itself from similar sibling tools such as ticker_social_sentiment, so it stops 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?

No guidance is given about when to choose this tool over alternatives like ticker_social_sentiment or reddit_attention. The description states what the tool returns but provides no exclusions, prerequisites, or preferred use cases.

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 must disclose behavioral traits. It states it 'lists stored calls' (a read operation) and mentions 'benchmark-adjusted outcome' and 'given horizon,' which describes output behavior. However, it doesn't explicitly state it's read-only or has no side effects, nor does it mention any authentication or rate 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 sentences plus a short disclaimer, with the core purpose front-loaded. No wasted words.

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 2-parameter tool with an output schema, the description covers the main purpose and output. It doesn't mention any limitations like 'stored calls only' or whether the tool is read-only, but given the output schema, it's acceptable. However, since annotations are absent, a note about safety would improve completeness.

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 0% description coverage, so the description must compensate. It mentions 'given horizon' which relates to horizon_days, providing some meaning. However, it doesn't explain the ticker parameter or the default behavior of horizon_days, though the schema itself provides defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a direct question that captures the tool's purpose: listing analyst calls for a ticker and their outcomes. It clearly distinguishes from analyst-centric tools by focusing on the ticker, and mentions specific details like 'benchmark-adjusted outcome' and 'given horizon.'

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 the usage context: 'Which analysts called this ticker' indicates this is for ticker-centric queries. It doesn't explicitly exclude analyst-centric tools, but the context is clear enough for an agent to route appropriately among the siblings.

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. It discloses that `sources` defaults to all three and that sentiment is blended across platforms, which are behavioral traits. However, it does not explicitly state that the tool is read-only, nor does it mention rate limits or any side effects. For a sentiment query, read-only is strongly implied, so a 3 is appropriate for partial 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?

The description is three short, front-loaded sentences: purpose, use case, and default. Every sentence adds value with no redundancy or fluff. It is exemplary in conciseness.

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, has an output schema, and the description covers purpose, usage, and the main default. It does not mention potential limitations (e.g., only US tickers) or exact source string formats, but these are minor for a sentiment tool. The presence of an output schema means return values are covered elsewhere, so the description is sufficiently complete for an agent to use it 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 0%, so the description must compensate. It only explains the `sources` default and implies that it can be filtered to the three platforms mentioned. It does not elaborate on `ticker` (beyond the name) or `limit` (beyond the name), nor does it specify allowed values for `sources`. This is partial compensation, but not enough to fully cover the missing schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (blended) and resource (retail sentiment for a ticker) across three named platforms (X, StockTwits, Reddit), clearly distinguishing it from single-platform siblings like reddit_attention, stocktwits_symbol, and search_x. This is a precise purpose that leaves no ambiguity about what the tool does.

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 a clear use case: 'Use this to corroborate (or challenge) an analyst's view with the broader crowd.' This gives an agent context for when to invoke it. However, it does not explicitly mention alternatives or when not to use it, though the blended nature implies it is the go-to for cross-platform sentiment. This earns a 4 rather than 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?

With no annotations provided, the description bears the full responsibility for behavioral disclosure. It only says 'currently trending' without explaining what 'trending' means algorithmically, whether the list is a snapshot or has pagination, any rate limits, or what fields appear in the output (beyond tickers). The 'retail-attention radar' phrase adds a hint but does not disclose concrete 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?

The description is efficient—a single sentence with no fluff. It front-loads the core purpose and adds a clarifying metaphor. However, it is arguably too terse, omitting substantial operational detail, so it leans toward under-specification rather than ideal conciseness.

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 presence of many sibling attention-tracking tools and an output schema (though not visible), the description should clarify what the tool returns and how it differs. It neither explains the parameter nor disambiguates from alternatives, nor describes the output structure. For a tool with minimal annotations, this level of detail is inadequate.

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 single parameter 'limit' has no description in the schema (coverage 0%) and the tool description does not mention it at all. An agent seeing 'limit' with a default of 30 has no idea it controls the number of results or how it affects the output. With zero schema coverage and zero description coverage, this is a complete gap.

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 clear verb (trending) and resource (StockTwits tickers), and adds the useful characterization 'retail-attention radar' to convey the data's nature. It does not explicitly contrast with sibling tools like wsb_trending or reddit_attention, but the platform name (StockTwits) inherently distinguishes it from those, so it is clear though not fully 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?

No guidance is given on when to choose this tool over the many similar siblings (wsb_trending, reddit_attention, leaderboard). There is no mention of context such as 'use for StockTwits-specific retail sentiment' or exclusions. The agent must infer usage from the platform name alone, which is insufficient given the abundance of alternative attention-tracking 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?

No annotations are present, so the description carries the burden. It discloses relevant behavior: the database is durable, queryable, and stores specific fields per analyst tweet. However, it does not explicitly state read-only behavior, freshness, or any operational limitations.

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 first sentence front-loads the purpose, and the second paragraph adds relevant data-field context. Minor redundancy like 'persisted' and 'durable, queryable record' exists, but the description remains tight and useful.

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 zero parameters and an output schema, the description is largely sufficient. It clearly explains what the database contains and why it matters, though it leaves the exact nature of the returned stats to the output schema.

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 empty input schema is fully covered. The description adds useful context about what data underlies the stats, which exceeds the baseline for a no-parameter tool.

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?

Description identifies the resource (persisted tweet database), the operation (stats), and the database's purpose. 'Stats' is broad, but it is clear enough to distinguish this from the listed analytics 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?

Description implies usage for provenance and analyst track-record features, but it does not explicitly state when to use this tool versus alternatives or when not to use it.

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?

No annotations are provided, so the description must carry the behavioral disclosure burden. It adds useful context about the source and time sensitivity, but it does not clarify how 'most-mentioned' is computed, whether the data is live or delayed, or whether this is purely a read-only call. This is adequate for a simple listing tool but not deeply transparent.

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 compact sentence with the core purpose front-loaded. The parenthetical adds source and use-case context without unnecessary words or repetition.

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 one-parameter tool with an output schema, the core purpose is clear. However, the description lacks explicit usage guidance, sibling differentiation, and parameter explanation, leaving some important context to inference. It is adequate but has clear gaps.

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%, and the description does not mention the 'limit' parameter or its meaning. The schema provides type and default, but the description adds no semantic elaboration to help an agent understand how the limit affects the returned list.

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 exactly what the tool returns: the most-mentioned tickers on r/wallstreetbets right now, and identifies the data source (ApeWisdom). This is specific enough to distinguish it from broader sibling tools like reddit_attention or trending_tickers.

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 'retail-attention radar' implies the intended use case, and 'right now' suggests it is for current interest. However, the description does not explicitly say when to use this tool instead of similar alternatives such as reddit_attention or trending_tickers.

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