AlphaLabs Intelligence
Server Details
Live trading-pipeline intelligence for AI agents: signal evaluation by a real calibrated engine with glass-box explanations, pipeline calibration telemetry, recorded outcome reports, and SEC-filing catalysts. Free beta keys + x402 USDC pay-per-call.
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- Healthy
- Last Tested
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- Streamable HTTP
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Tool Definition Quality
Average 3.8/5 across 6 of 6 tools scored. Lowest: 3.2/5.
Each tool has a distinctly different purpose: calibration telemetry, signal evaluation, explanation, feature attribution, catalog, and outcome reporting. No two tools overlap in function.
All tools share the 'alphalabs_' prefix and use snake_case. However, some names are verb_noun (evaluate_signal, explain_decision, get_catalog) while others are noun_noun (calibration_report, feature_attribution, outcome_report), showing slight inconsistency.
With 6 tools, the server is well-scoped for its purpose of evaluating and explaining trading signals. Each tool earns its place without redundancy.
The tool set covers the core evaluation lifecycle (evaluate, explain, attribute, report) and includes product info. A minor gap is the lack of a tool to list prior evaluations by ID, requiring the user to already have evaluation IDs.
Available Tools
6 toolsalphalabs_calibration_reportAInspect
Live paper-trading pipeline calibration telemetry: stage funnel, gate failures, near-misses. Derived analytics only — no positions, orders, or account data exist on this surface.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It explicitly states the tool provides derived analytics and contains no positions, orders, or account data, indicating a safe, read-only operation. While it does not detail authentication or rate limits, the safety profile is clearly communicated for a telemetry tool.
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 two sentences, front-loading the core purpose in the first sentence and adding clarifying constraints in the second. Every word serves a purpose with no redundancy.
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 the simplicity of the tool (no parameters, no output schema, no annotations), the description is fully adequate. It defines the resource, the nature of the data, and what is excluded, leaving no ambiguity about its function.
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 input schema has zero parameters and 100% schema description coverage, so the description has no parameters to explain. Baseline for 0 parameters is 4, and the description adds no additional param info, which is appropriate.
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 identifies the tool as providing calibration telemetry from a live paper-trading pipeline, including stage funnel, gate failures, and near-misses. The sibling tools (evaluate_signal, explain_decision, etc.) are distinct in purpose, so this description effectively distinguishes it.
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 states 'Derived analytics only' and 'no positions, orders, or account data exist on this surface,' giving context that it is for analytical insights only. However, it does not explicitly mention when to prefer this tool over siblings or provide exclusion criteria, so usage guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_evaluate_signalAInspect
Score YOUR trade idea through the live AlphaLabs deterministic engine: composite score, tier, per-component sub-signals, floors. Price/volume confirmation is not evaluated (no vendor market data). Returns an evaluation_id for alphalabs_explain_decision.
| Name | Required | Description | Default |
|---|---|---|---|
| bias | Yes | ||
| thesis | No | Why it should move the stock | |
| ticker | Yes | Symbol, e.g. NVDA | |
| catalyst | No | What just happened (headline/event) | |
| confidence | No | Your own conviction 0-1 (echoed, not scored) | |
| catalyst_type | No | Optional label, e.g. 'Government Contract' | |
| catalyst_score | No | Optional 0-100 materiality if you scored it |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description effectively discloses behavioral traits: the engine is deterministic, the output includes composite score/tier/sub-signals/floors, and an evaluation_id is returned. It also transparently states the limitation of no price/volume confirmation. No annotation contradictions exist.
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 concise at two sentences, with the first sentence immediately stating the core function and output, and the second adding valuable limitations and workflow linkage. 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?
The description covers the purpose, key output components, workflow (evaluation_id), and a critical limitation. Given the moderate parameter count and lack of output schema, it is mostly complete, though the exact return format is not specified.
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 input schema already describes 6 of 7 parameters (86% coverage), and the missing bias parameter is self-explanatory via its enum. The description adds context (e.g., 'trade idea') but no additional parameter-level detail beyond the schema, so baseline 3 is appropriate.
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 evaluates trade ideas to produce a composite score, tier, and sub-signals. It differentiates from siblings by specifying it returns an evaluation_id for alphalabs_explain_decision and notes that price/volume confirmation is not included.
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 scoring trade ideas and mentions a follow-up tool (alphalabs_explain_decision). It also explicitly states what is not evaluated (price/volume confirmation), providing a clear exclusion. However, it does not enumerate alternative tools or specific when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_explain_decisionAInspect
Glass-box breakdown of a prior evaluation by evaluation_id: every sub-signal, weight, floor, and the composite reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| evaluation_id | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explicitly describes the tool as a 'glass-box breakdown' and lists the components included, implying a read-only introspection operation without side effects. This is good transparency, though it could mention output format or limitations.
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 sentence that front-loads the main purpose ('Glass-box breakdown of a prior evaluation') and details what is included. It is concise with no unnecessary words.
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 explains what the breakdown contains but does not describe the output format or structure, which is needed since no output schema exists. It also does not clarify how this tool relates to siblings. For a single-parameter tool, it is adequate but not fully complete.
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%, and the description adds minimal meaning beyond the schema—it identifies the parameter as an evaluation ID but provides no format, constraints, or examples. The description should compensate for the low coverage but does so insufficiently.
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 provides a detailed breakdown of a prior evaluation, listing specific components (sub-signals, weight, floor, composite reasoning). It is a specific verb+resource that distinguishes from sibling tools like alphalabs_evaluate_signal or alphalabs_outcome_report.
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?
No guidance is given on when to use this tool versus alternatives such as alphalabs_calibration_report or alphalabs_feature_attribution. The description only explains what it does, not the context of use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_feature_attributionBInspect
Which engine inputs actually predict outcomes, measured on recorded live results: Spearman rankings, median-split deltas, dead inputs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions it measures on 'recorded live results', indicating it uses historical data, but does not disclose read-only status, potential side effects, or prerequisites. With no annotations, the description partially addresses behavior but is incomplete.
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 concise and front-loaded with a clear question. However, it could be restructured or expanded slightly to improve readability without losing brevity.
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?
Despite having no parameters or output schema, the description is too brief. It does not explain what the output looks like, how to interpret results, or any caveats. For a complex analytical tool, more context is needed.
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 tool has zero parameters, and the schema coverage is 100% (empty schema). Per guidelines, a baseline of 4 applies since no parameter information is needed; the description adds no param detail, which is acceptable.
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 measures which engine inputs predict outcomes, listing specific metrics (Spearman rankings, median-split deltas, dead inputs). However, it does not explicitly differentiate from sibling tools like alphalabs_evaluate_signal or alphalabs_explain_decision, leaving some ambiguity about scope.
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?
No guidance is provided on when to use this tool versus alternatives, such as for troubleshooting specific issues or when feature importance is needed. The description lacks context for appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_get_catalogAInspect
Free: list AlphaLabs Intelligence products, prices, and auth model.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that it is 'Free' but does not mention rate limits, authentication needs, or whether it is read-only. The tool is straightforward, so minimal disclosure is acceptable.
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 concise sentence that is front-loaded with key information ('Free: list...'). Every word serves a purpose with no redundancy.
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 the tool has no parameters, no output schema, and a simple purpose (listing catalog), the description covers the essentials. It could mention output format or pagination, but is sufficient for a straightforward listing 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?
There are no parameters (schema has no properties, coverage 100%). The description adds no parameter-specific info, but there is nothing to add. A baseline of 4 is appropriate for zero-parameter tools.
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 lists AlphaLabs Intelligence products, prices, and auth model. It uses specific nouns and distinguishes from sibling tools, which focus on calibration, evaluation, and reports.
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 by stating what it lists ('products, prices, and auth model'), but does not explicitly state when to use versus alternatives or when not to use. No prerequisites or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_outcome_reportAInspect
Recorded outcomes of the live pipeline's own decisions: hit rates, score-band tables, accepted-vs-rejected edge, gate near-miss regret. Aggregated engine telemetry — percent moves and counts only.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It implies the tool is read-only (recorded outcomes) but does not explicitly state it is non-destructive or safe. The mention of 'aggregated engine telemetry' hints at output but not behavioral impacts.
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, no wasted words. Front-loaded with the core purpose, followed by specific details. Every sentence 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?
Given no parameters and no output schema, the description provides a solid overview of the tool's content. However, it lacks explicit mention of return format or scope (e.g., timeframe), which could be helpful. Still, it is largely complete for a simple report 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?
The tool has zero parameters, so the input schema fully covers them. The description adds no parameter information, which is acceptable since there are none. Baseline 4 applies due to high 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 states the tool provides recorded outcomes of pipeline decisions, including specific metrics like hit rates and score-band tables. It implicitly distinguishes from siblings by focusing on internal pipeline decisions, but does not explicitly name alternative tools.
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 no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The agent is left to infer context from the tool name alone.
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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