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Glama

Agent Einstein — Crypto & Market Intelligence

by io.emc2ai

US Congress Stock Trades

get_congress_trades
Read-onlyIdempotent

Recently disclosed stock and asset transactions by members of the US Congress, newest first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows to return (1-25).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoDisclosures in this pass.
limitNoRow cap applied to every list in this payload.
staleNoTrue when the snapshot is past its refresh interval.
reasonNoWhy the payload is absent, when `available` is false.
shortsNoShort signals.
sourcesNoPer-source status — which filing feeds answered.
availableNoFalse when this call has no data — a snapshot that is not warm yet, a domain switched off, or an argument that was rejected. NOT an error, and NOT a statement about the market.
fetchedAtNoWhen the underlying snapshot was refreshed (epoch ms, or ISO-8601 for the Bitcoin cycle feed). Age matters: these are cached reads, not live queries.
longSignalsNoScored buy-side signals.
shortSignalsNoScored sell-side signals; same fields as `longSignals`.
actionableLongsNoLong signals that cleared the scoring gate.
sectorAggregatesNoDisclosed dollars by sector.

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already establish readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful ordering behavior ('newest first') and recency framing ('Recently disclosed'), but it does not elaborate on pagination, data freshness limits, or any other operational nuances beyond what annotations and schema already convey.

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?

A single sentence conveys the resource, scope, and ordering without extraneous words. Information is front-loaded and every element earns its place, making it easy for an agent to parse quickly.

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?

For a simple, one-parameter read-only tool with full schema coverage, a rich annotations block, and an output schema, the description is complete. It communicates the essential result shape and ordering; nothing necessary for correct invocation is missing.

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

Parameters3/5

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

Schema description coverage is 100%: the single 'limit' parameter is fully documented with type, default, and range. The description does not mention parameters at all, but with complete schema coverage, the baseline of 3 is appropriate. No additional semantic burden is placed on the description.

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

Purpose4/5

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

The description clearly identifies the resource—recently disclosed stock and asset transactions by US Congress members—and specifies ordering as newest first. It lacks an explicit imperative verb like 'list' or 'retrieve,' but the intent is unmistakable and sufficiently distinguishes it from sibling tools focused on other data domains.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives such as get_government_contracts, get_market_sentiment, or get_trade_signals. There is no mention of use cases, exclusions, or contextual conditions, leaving the agent to infer applicability solely from the tool name and description.

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

B3.3/5.0
Disambiguation2/5

With 40 tools, many share overlapping domains: get_smart_money_flow vs get_smart_money_inflow, scan_launchpads vs get_launchpad_radar, track_whales vs get_hyperliquid_whales, and check_token_safety vs analyze_token_security. The detailed descriptions help, but the boundaries are not always clear, making misselection likely.

Naming Consistency2/5

The tool names employ a wide variety of verbs (get_, analyze_, scan_, track_, find_, generate_, recommend_, run_, list_, ask_, assess_, detect_) with no consistent pattern. While all use snake_case, the inconsistent verb choices and occasional deviations like forecast_chart prevent predictability.

Tool Count2/5

40 tools is well above the typical 3-15 well-scoped range and exceeds the 25+ threshold for 'too many'. While the broad 'crypto intelligence' purpose justifies some breadth, the sheer number makes the surface unwieldy and suggests a lack of focused scoping.

Completeness4/5

The tool set covers a wide range of crypto intelligence domains: market analysis, forecasting, whale tracking, yield/arbitrage, security checks, prediction markets, backtesting, and even content generation. Missing operations are minor (e.g., no direct portfolio management), but core analysis and data retrieval workflows are well represented.

Resources