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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Average 3.9/5 across 6 of 6 tools scored. Lowest: 3.2/5.
Each tool has a clearly distinct purpose: calibration telemetry, signal evaluation, explanation, feature attribution, catalog listing, and outcome reporting. No two tools overlap in functionality.
All tools share the 'alphalabs_' prefix and follow a verb_noun or noun_noun pattern. The mix of verb_noun (evaluate_signal, explain_decision, get_catalog) and noun_noun (calibration_report, feature_attribution, outcome_report) is a minor inconsistency, but still predictable and readable.
With 6 tools, the server is neither too sparse nor too heavy. Each tool covers a distinct aspect of the intelligence pipeline, making the set well-scoped for its purpose.
The tool surface covers the full lifecycle of signal evaluation: evaluation, explanation, feature analysis, calibration reporting, and outcome tracking. Missing features like market data access are explicitly out of scope, so no dead ends remain.
alphalabs_calibration_reportAInspectLive 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 | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It clearly states the tool is read-only and provides derived analytics, not raw positions or orders. This fully discloses the non-destructive, analytical nature of the 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?
Two concise sentences, front-loaded with purpose. Every word adds value: first sentence defines the telemetry, second clarifies boundaries. No waste.
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?
This is a simple tool with no parameters or output schema. The description explains the data it provides (stage funnel, gate failures, near-misses) and explicitly states what is excluded, making it complete for its intended use.
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 no parameters and schema coverage is 100% (empty schema). Baseline per instructions is 4 for 0 parameters. The description adds no parameter-specific information, which is acceptable given no parameters exist.
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 explicitly states the tool provides 'calibration telemetry: stage funnel, gate failures, near-misses' from a 'live paper-trading pipeline'. It also clarifies what it does not contain ('no positions, orders, or account data'), clearly distinguishing it from sibling tools like alphalabs_outcome_report or alphalabs_get_catalog.
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 the tool is for calibration monitoring and explicitly excludes trading data, guiding use. However, it does not explicitly state when to prefer this over siblings or provide alternative tool names. The context is clear enough but lacks explicit 'when not to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_evaluate_signalAInspectScore 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the tool is deterministic, does not evaluate price/volume confirmation, and returns specific outputs. It does not mention authentication, rate limits, or if the action is destructive, but for a scoring tool this is reasonable.
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 waste. First sentence states purpose and outputs, second sentence adds a key limitation and links to a sibling. Information is front-loaded and each sentence 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?
Given 7 parameters and no output schema, the description provides a good overview of functionality, outputs, and a limitation. It references a sibling tool for deeper explanation, which helps the agent. Could be improved by briefly listing expected outputs, but sufficient for basic 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 description coverage is high (86%), so baseline is 3. The description adds no parameter-specific meaning beyond the schema; it mentions outputs (composite score, evaluation_id) but not how parameters map to those. The schema itself provides decent descriptions for most parameters.
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 scores a trade idea and returns a composite score, tier, sub-signals, and floors. It mentions the limitation regarding price/volume confirmation and links to a sibling tool for explanation, providing a distinct purpose.
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 indicates that price/volume confirmation is not evaluated, implying when not to rely on this tool for that aspect. It also mentions returning an evaluation_id for alphalabs_explain_decision, suggesting a workflow. However, it does not explicitly compare with siblings like alphalabs_calibration_report or alphalabs_outcome_report.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_explain_decisionAInspectGlass-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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral traits. It indicates the tool is a read-only 'glass-box' analysis, but it does not mention permissions, rate limits, or error handling (e.g., what happens if evaluation_id is invalid). The description is adequate but not exhaustive.
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 sentence that directly states the tool's purpose and contents. Every word contributes value, making it efficient and easy to parse.
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's simplicity (one parameter, no output schema), the description sufficiently explains the function and return components. It could mention what happens on failure or the absence of a result, but for a straightforward breakdown tool, it is largely 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?
The single parameter evaluation_id is described as 'prior evaluation by evaluation_id', adding context that the ID corresponds to an existing evaluation. However, it does not specify the expected format or how to obtain valid IDs. Schema coverage is 0%, so the description provides some additional meaning beyond the raw schema but could be more precise.
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 it provides a 'Glass-box breakdown' of a prior evaluation by evaluation_id, listing specific components (sub-signals, weight, floor, composite reasoning). This distinguishes it from siblings like alphalabs_calibration_report or alphalabs_feature_attribution, which serve different purposes.
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 use when a detailed breakdown of a prior evaluation is needed, but it does not explicitly state when to avoid this tool or suggest alternatives. For example, if only the final outcome is needed, alphalabs_outcome_report might be more appropriate. The context is clear but lacks exclusionary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_feature_attributionBInspectWhich engine inputs actually predict outcomes, measured on recorded live results: Spearman rankings, median-split deltas, dead inputs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits (e.g., read-only, idempotent, destructive). No mention of side effects, auth requirements, or performance implications.
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 sentence, no wasted words. Front-loads the core question and lists key outputs concisely.
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?
Describes the tool's purpose and outputs adequately but lacks details on return format or behavior edge cases. For a tool with no parameters and no output schema, it is somewhat complete but could be clearer about what 'dead inputs' means.
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?
No parameters exist, so schema coverage is trivially 100%. The description adds meaning by explaining the tool's output (Spearman rankings, etc.), though it does not describe param details since none are needed.
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 measures feature attribution on live results using specific metrics (Spearman rankings, median-split deltas, dead inputs). It distinguishes from sibling tools by mentioning these unique outputs.
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 on when to use this tool versus alternatives like alphalabs_calibration_report or alphalabs_explain_decision. No prerequisites or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_get_catalogAInspectFree: list AlphaLabs Intelligence products, prices, and auth model.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool is 'Free' and lists the content types (products, prices, auth model). However, it does not mention rate limits, data freshness, or authentication requirements. Still, it adds meaningful context beyond the empty schema.
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 sentence, front-loaded with key action and scope, no unnecessary words. Every word serves a purpose.
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 simple zero-parameter listing tool, the description is mostly adequate. It lacks details on output format or update frequency, but with no output schema and no annotations, it still provides sufficient context for an agent to decide to call it.
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 100% (no parameters), so baseline 4. The description does not need to add parameter info since there are none.
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 it lists AlphaLabs Intelligence products, prices, and auth model. The verb 'list' and resource are specific, and the tool is distinct from sibling tools which focus on calibration, evaluation, and explanation.
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?
Usage is implied as a catalog listing, but no explicit guidance on when to use this versus siblings, nor any when-not conditions. Given the simplicity, a score of 3 is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_outcome_reportBInspectRecorded 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 | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry behavioral disclosure. It describes the output as aggregated engine telemetry (percent moves and counts), implying a read-only report. However, it does not explicitly state it is non-destructive or mention any side effects.
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, clear and directly informative. Slightly verbose with the list of metrics but no wasted words. Front-loaded with purpose.
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 report tool, the description adequately covers the return value and purpose. Missing details like update frequency or real-time nature, but acceptable given low complexity.
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?
No parameters exist in the schema, so baseline is 4. The description explains what the tool returns without needing parameter details, which is appropriate given zero parameters.
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 records outcomes of live pipeline decisions, listing specific metrics like hit rates and score-band tables. It distinguishes itself from sibling tools like calibration or explanation reports by focusing on aggregated telemetry.
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 explicit guidance on when to use this tool vs alternatives. The description implies it's for aggregated telemetry but does not state when not to use it or mention sibling tools for comparison.
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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