Bargo Congress Trades

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

With no annotations, the description should disclose behavioral traits like read-only nature, authentication needs, or error handling. It implies a read operation but does not explicitly state safety or side effects. The performance summary addition is useful context, but key behavioral details are missing.

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 with no fluff. The first sentence immediately states the output and the second gives input guidance. Every word 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 description covers the primary output and key input, but given the lack of output schema and annotations, it omits pagination details (limit parameter behavior), error cases, and return format. Adequate but leaves gaps for a complete understanding.

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 adds meaning for member_slug by specifying its origin and format, but does not mention the limit parameter or its constraints (min, max, default). The description partially compensates for schema gaps but is incomplete.

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 returns 'All disclosed trades for one member of Congress plus a performance summary', specifying the exact data and the required input (member_slug). It distinguishes from sibling tools by focusing on a single member.

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 guidance on obtaining the member_slug by referencing get_congress_trades and gives an example ('nancy-pelosi'). It implicitly indicates when to use this tool (when a specific member slug is available) but does not explicitly exclude other scenarios or 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?

No annotations provided. Description indicates a read-only aggregation, which is safe. Does not elaborate on data freshness, caching, or any side effects. With no destructive potential, a score of 3 is appropriate; it adds minimal behavioral context beyond the obvious.

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?

Single, complete sentence that front-loads the purpose ('Aggregate Congress-trading stats') and lists all returned metrics efficiently. No redundant words; every phrase 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 parameterless tool returning aggregated stats, the description covers the key fields and time range (90 days). Could mention if data is real-time or cached, but overall complete enough for selecting and invoking.

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?

Input schema has no parameters, so schema coverage is 100%. The description adds value by detailing the output: total trades, members, tickers, buy/sell counts, dates, and most-traded tickers. This helps the user understand what the tool returns without needing an output 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?

Description clearly states the tool aggregates Congress-trading stats over the last 90 days, listing specific metrics (total trades, members, tickers, buy/sell counts, dates, most-traded). It distinguishes from siblings: get_congress_member and get_congress_trades are likely for individual members/trades, while this provides aggregate overview.

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 a high-level summary over 90 days but does not explicitly state when to use this tool vs alternatives. No when-not-to-use or prerequisites mentioned. With sibling tools providing more granular data, this is adequate but lacks 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 full burden and discloses data source, returned fields, sorting order, and a caveat about 45-day delay. It does not mention authentication, rate limits, or pagination, but provides sufficient behavioral context for typical use.

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 purpose, then lists data fields, filters, sorting, and a caveat with no redundant information.

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 5 optional parameters and no output schema, the description provides meaningful context about data content, filters, and timeliness. It does not mention output format or error handling, but covers the essential usage scenarios.

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 compensates by explaining the filtering parameters (ticker, member substring, chamber, type) with relevant details. However, the 'limit' parameter is absent from the description, missing an opportunity to fully clarify its role.

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 retrieves U.S. Congress stock trades from official reports and distinguishes itself from sibling tools (get_congress_member, get_congress_stats) by detailing the specific data fields and filters.

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 usage context including available filter options (ticker, member, chamber, type) and a caveat about the disclosure delay, but does not explicitly state when not to use this tool or outline alternatives.

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