Skip to main content
Glama

Actionable Signals

get_actionable_signals
Read-only

[START HERE — 'find me a trade'] Signals the production trade-gate itself marked actionable (action_hint=trade_signal — the engine's per-(type,direction) proven verdict), age ≤ max_age_min. Skips anti-predictive cells. Also returns swing_outlooks: labeled days-scale reads from cells proven at 7-30d horizons, each with its proven horizon and a suggested hold — NOT intraday trades. Pass min_score / min_confidence only if you want additional numeric bars on top of the engine verdict. When the result is empty, the context block points to rank_trades / get_trade_plan — a signal-gate miss does not mean no setup exists (positioning/levels setups aren't signal-driven). ALWAYS check has_trade_signal (true only when a real intraday trade cleared the gate) and render reads — a single array that is NEVER empty when any read exists: it holds the gate-passed trade signals, or, when none cleared, the strongest context read + swing outlooks, each tagged kind and actionable. signals stays strictly gate-passed; items in reads with actionable=false are NOT trades. Next: grade any candidate with check_trade; rank_trades when nothing cleared the gate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assetNo
min_scoreNo
max_age_minNo
signal_typeNo
min_confidenceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
signalsYes
next_cursorNo
market_opensNo
server_timestampNo

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description reveals key behaviors: skipping anti-predictive cells, the distinction between `signals` and `reads`, the never-empty `reads` array, and the meaning of `has_trade_signal`. This level of detail is transparent and consistent with annotations.

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?

Though lengthy, the description is densely packed with operational instructions and uses visual spacing and capitalization for emphasis. Every sentence earns its place, and the critical 'start here' directive is front-loaded.

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?

The description self-sufficiently covers purpose, parameter usage, result structure, fallback behavior, and next-step guidance. It does not rely on the output schema or annotations to be understood, and it addresses edge cases like empty results and the actionable=false tag.

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?

The description explains the semantics of max_age_min, min_score, and min_confidence, but asset and signal_type are left to inference from their names. Given that the schema has no property descriptions, this partial coverage still adds value beyond the raw schema.

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 opens with a clear directive ('find me a trade') and specifies exactly what the tool returns: gate-passed intraday trade signals and swing outlooks. It explicitly names sibling tools (rank_trades, get_trade_plan, check_trade) and contrasts the tool's purpose, making it unmistakable.

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?

The description gives explicit usage context: when to start here, when the empty result should redirect to rank_trades/get_trade_plan, and that check_trade should be used to grade candidates. It also warns that swing_outlooks are NOT intraday trades, preventing misuse.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions, but there are clusters of similar concepts (e.g., get_liquidity_map vs get_liquidation_map, get_state vs get_state_brief, multiple signal-related tools) that could cause misselection despite thorough documentation.

Naming Consistency5/5

All tools follow a consistent lowercase verb_noun pattern, predominantly get_* nouns, with only a few non-get verbs like list_signals, rank_trades, log_trade, etc., but the style is uniform.

Tool Count2/5

With 52 tools, the surface is extremely heavy for an agent to navigate. While the server's scope is broad, the count far exceeds the typical 3-15 range and falls into the 'too many' category.

Completeness4/5

The tool set covers the full lifecycle for journaling, signals, market analysis, and proof, with no major dead ends. Minor gaps exist, such as no dedicated get_trade_by_id (workaround via get_journal) and no get_market_state tool despite being referenced in get_state.

Resources