feeds

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

With no annotations available, the description carries the full behavioral burden and does a good job: it explains the three timestamp fields, the ~45-day STOCK Act disclosure delay, and the key limitation that Senate data is parsed to ticker level while House filings are metadata only. It does not cover rate limits, pagination, or return formatting, but the most decision-relevant behaviors are disclosed.

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

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

The description is appropriately sized at four sentences, each serving a distinct purpose: scope/source, field definitions, temporal semantics, and data-granularity caveat. It is front-loaded with the primary function and contains no filler or repetition. This is a model of concise, information-dense documentation.

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 four optional filters and no output schema, the description provides the return record fields and critical data-quality caveats, which is sufficient for an agent to invoke it correctly. The main gap is the absence of pagination, result limits, ordering, or a note about what 'metadata only' means for House records, which prevents a perfect score.

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 already describes three of the four parameters (name, since, ticker), leaving chamber with only enum values. The description adds important semantic nuance beyond the schema: the Senate-vs-House granularity difference affects how ticker and chamber filters behave, and the disclosed_at guidance clarifies the intended meaning of since for temporal filtering. This goes beyond the structured schema 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 identifies the resource (US Congress stock transactions) and its sources, and implies a query/list operation even though no explicit verb like 'list' or 'get' is used. The domain (Congress) and mention of Senate vs House data distinguish it from the sibling tools focused on ETF flows and insider trades, though that contrast is not explicitly stated.

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

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

The description gives no explicit 'when to use this vs alternatives' guidance. However, it implies the intended use case through domain-specific context, and it offers concrete usage advice about using disclosed_at rather than happened_at for point-in-time work. No exclusions or alternative tool names are mentioned, so usage guidance is mostly implied rather than direct.

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

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

With no annotations, the description carries the full burden, and it delivers rich behavioral detail: it explains that price moves are removed, that the series cannot be backfilled, that every row was banked on its existing day, and that span_days above 1 flags gaps. This goes well beyond a simple 'returns flows' statement.

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 impactful. It leads with the core definition and computation, then adds necessary caveats about backfilling and gap handling. Every sentence earns its place with no filler.

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

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

Given no annotations and no output schema, the description covers the essential behavioral caveats and the meaning of span_days. It could be marginally more complete by describing the full row structure or default time range, but it is sufficient for selecting and invoking the 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%, with both since and ticker documented in the schema. The description adds useful context about output semantics like span_days, but it does not add meaning to the parameters themselves beyond what the schema already provides.

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 resource—spot bitcoin ETF creation/redemption flows—and explains the precise computation (change in shares outstanding times NAV). This clearly distinguishes it from the sibling tools, which cover congressional trades and insider transactions.

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 domain is clearly ETF flow data, so the use case is evident relative to the sibling tools. However, the description does not explicitly state when to use this tool versus alternatives, nor does it mention 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?

With no annotations, the description carries the behavioral disclosure burden and does so well: it explains the data source, the code-P-only filter, the exclusion rationale, and the meaning of observed_at versus happened_at. It does not cover ordering, pagination, or rate limits, but those are minor for a simple read-only feed.

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: scope, exclusion rule, rationale, field semantics, and sibling routing each get one clear sentence. There is no redundant or promotional language.

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 three-optional-parameter read feed with no output schema, the description provides enough context for an agent to decide when to use it and what it returns. The only minor gap is lack of detail about default result ordering or size limits.

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 already documents since and min_value, and the description does not add parameter-level meaning beyond that. The ticker parameter has no schema description and is not mentioned in the tool description, but its meaning is obvious from the context, so the 67% schema coverage 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 clearly identifies the tool as a feed of SEC Form 4 open-market insider buys and immediately narrows it to code P transactions. It explicitly excludes grants, option exercises, tax withholding, and gifts, and names the sibling insider_sells for disposals, making the purpose distinct.

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 states when this feed is appropriate: when an officer or director spends their own money, i.e., conviction buying. It also gives a direct alternative, telling agents to use insider_sells for disposals, and clarifies which transaction types should not be queried here.

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

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

With no annotations, the description carries the full behavioral disclosure burden. It explains the data source, the Form-4 code filter, the distinction between observed_at and happened_at, and adds important interpretive caveats about insider sales. It does not mention pagination, rate limits, or authentication, but for a simple read-style data tool the provided context is substantial.

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 essential purpose, then adds caveats, field semantics, and a sibling pointer. Every sentence contributes distinct value with no repetition or filler.

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

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

For a simple optional-parameter tool with no output schema, the description is mostly complete: it names key returned fields, explains their meaning, notes the data source, and gives an alternative. It does not describe the full output shape or pagination, but the core information an agent needs to select and invoke the tool is present.

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 already describes since and min_value, but ticker has no description and the tool description does not compensate for that gap. The parameter names are mostly self-explanatory, and the description adds useful context about the data source, but it does not meaningfully enrich the meaning of the input parameters themselves.

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 is specific and actionable: it identifies the tool as SEC Form 4 open-market insider sells, restricts to Form-4 code S, and explicitly contrasts it with insider_buys for acquisitions. This makes the tool's purpose immediately distinguishable from its siblings without needing to inspect schemas.

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 usage context: it tells the agent to use this for open-market insider sales, warns that sales carry less informational weight than purchases, and explicitly directs acquisition-related searches to insider_buys. This is strong when-to-use guidance with a named alternative.

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