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Glama

ko-mcp

by io.github.SharpLu

get_congress_trades

Search individual stock trades disclosed by U.S. Congress members (House and Senate) under the STOCK Act. Returns a markdown table of transactions: member name, chamber, ticker, buy/sell type, transaction date, disclosure date (the gap between the two reveals reporting delay), dollar amount range, and owner (self/spouse/joint). Use for questions like 'What did Nancy Pelosi trade recently?', 'Which members bought NVDA?', or 'Show the largest Senate trades this quarter'. Filter by chamber, party, state, ticker, or member name; sort by traded value, trade count, or recency. For one member's profile and complete trading history, use get_congress_member instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number for pagination (default 1)
sortNoSort order — volume (most traded value), trades (most trades), recent (latest first)volume
limitNoTrades per page, 1-50 (default 20)
partyNoFilter by party — D=Democrat, R=Republican, I=Independentall
stateNoMember 2-letter U.S. state code, e.g. CA or TX
searchNoFull or partial member name, e.g. 'Pelosi' or 'Dan Crenshaw'
tickerNoStock ticker symbol to filter by, e.g. NVDA or AAPL
chamberNoCongressional chamber: house, senate, or all (default all)all

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Without annotations, the description fully explains the return format (markdown table with specific columns: member name, chamber, ticker, buy/sell type, transaction date, disclosure date, dollar amount range, owner). It discloses the behavior of filtering and sorting. However, it does not address potential limitations like data freshness, rate limits, or authentication, which would have pushed it to 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.

Conciseness5/5

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

The description is concise at approximately 100 words, with no redundancy. It is front-loaded with the core purpose, then details the output format, example queries, filter/sort options, and a pointer to the sibling tool. Every sentence serves a clear purpose.

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

Completeness5/5

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

Given 8 parameters, no output schema, and no annotations, the description covers all essential aspects: input, output format, usage context, and alternative tool. It provides enough information for an agent to correctly invoke the tool and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explicitly listing the filterable fields (chamber, party, state, ticker, member name) and sort options (volume, trades, recent) in a coherent sentence, complementing the schema descriptions. Example queries further clarify parameter usage.

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

Purpose5/5

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

The description clearly states the verb 'Search' and the resource 'individual stock trades disclosed by U.S. Congress members (House and Senate) under the STOCK Act.' It also differentiates from the sibling tool get_congress_member by specifying that this tool is for searching trades, while the sibling is for one member's complete history.

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

Usage Guidelines5/5

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

Explicit guidance is provided on when to use this tool (for questions about member trades, filtering by various criteria) and when to use the alternative (get_congress_member for one member's full profile). The description lists filter and sort options, giving clear context for appropriate usage.

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

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TDQS

A3.9/5.0
Disambiguation5/5

Each tool targets a distinct resource and action, and descriptions explicitly disambiguate similar pairs (e.g., get_congress_member vs get_congress_trades, get_crypto_holder vs get_crypto_holders). No two tools appear to do the same thing.

Naming Consistency4/5

The dominant pattern is get_<noun>, with list_<noun> for enumerations. Minor deviations exist: a bare 'search' tool and the 'sec_' prefix on SEC filing tools break the uniform verb_noun style, but the pattern remains predictable.

Tool Count4/5

At 24 tools, this is on the heavier side, but the server spans multiple financial data domains (SEC filings, stocks, insider trading, crypto, rates, economics), so each tool has a clear purpose. It is just below the 'too many' threshold.

Completeness5/5

The tool surface is remarkably complete for financial data retrieval: SEC filing lifecycle is covered (list -> index -> document), institutional ownership is available from both stock and institution perspectives, and insider/congress/crypto/economic data are all present. No obvious gaps or dead ends.

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