Live trading-pipeline intelligence for AI agents: signal scoring, calibration, recorded outcomes.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 2.8/5.
Each tool targets a distinct aspect: pipeline telemetry, signal evaluation, decision explanation, feature attribution, product catalog, and aggregated outcome reports. No two tools overlap in purpose.
All tools share the 'alphalabs_' prefix and are descriptive. However, some are verb-noun (evaluate_signal, explain_decision, get_catalog) while others are noun-noun (calibration_report, feature_attribution, outcome_report), creating a minor inconsistency.
Six tools cover the core analytics and evaluation functionality without being excessive. Each tool serves a clear, necessary role in the workflow.
The set covers signal evaluation, explanation, attribution, and aggregated reports. Missing a tool to list prior evaluations or manage them, but the core analytical surface is well covered.
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 present, so the description carries full burden. It correctly labels the tool as returning derived analytics only, implying a read-only behavior. However, it does not disclose any other behavioral traits such as response time, pagination, or rate limits. The description is adequate but not rich.
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 front-loads the key purpose and includes a clarifying exclusion. Every part adds value with no redundancy or wasted 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?
Given there are no parameters and no output schema, the description fully explains what the tool returns (derived analytics) and what it does not (positions, orders, account data). This is complete for a reporting tool with no inputs.
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 no input schema constraints (100% schema coverage). The description adds significant meaning by specifying the content of the report (stage funnel, gate failures, near-misses) and clarifying what it does not contain (positions, orders, account data). This compensates for the lack of 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 provides live paper-trading pipeline calibration telemetry including stage funnel, gate failures, and near-misses. It emphasizes that it is derived analytics only, differentiating it from siblings that might involve positions, orders, or account data.
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 gives clear context: it is for calibration telemetry and explicitly states what it does not cover (positions, orders, account data), guiding users away from using it for those purposes. However, it does not name specific alternative tools, leaving some inference required.
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 the full burden. It discloses it is a live deterministic engine and lacks price/volume confirmation. However, it does not mention idempotency, rate limits, side effects, or authentication requirements, leaving gaps for a scoring 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?
Three concise sentences: output overview, limitation, and return value. Front-loaded with key purpose, no fluff. Every sentence is informative.
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 the key outputs (composite score, tier, sub-signals, floors) and the evaluation_id for chaining. It lacks details on whether the tool stores results or side effects, but given no output schema, it covers essentials.
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 does not add meaning beyond the schema's parameter descriptions; it only mentions output components without linking to inputs.
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, produces a composite score, tier, sub-signals, and floors. It explicitly mentions returning an evaluation_id for use with the sibling tool alphalabs_explain_decision, distinguishing its role.
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 explains the tool evaluates trade ideas, notes that price/volume confirmation is not evaluated, and indicates the returned evaluation_id is for use with alphalabs_explain_decision. It provides clear context but does not explicitly state when not to use or compare to other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_explain_decisionBInspectGlass-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 carries full burden. It describes the output content but does not disclose behavioral traits such as whether the tool is read-only, idempotent, or any side effects. The phrase 'glass-box breakdown' suggests transparency but lacks explicit safety or effect info.
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 front-loads the key concept ('Glass-box breakdown') and then lists the included elements. Every word adds value 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's complexity (explaining decision logic with sub-signals, weights, etc.) and the absence of an output schema, the description is adequate for high-level understanding but lacks details on the return format or structure. An example or mention of the output shape would improve completeness.
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%, so the description must compensate. It mentions the parameter 'evaluation_id' in the text but offers no additional details about format, constraints, or examples. While the purpose is clear, the lack of parameter guidance reduces usability.
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, specifying exactly what is included (sub-signal, weight, floor, composite reasoning). This verb-resource combination is specific and distinguishes it from sibling tools like 'alphalabs_evaluate_signal' which focuses on evaluation execution.
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 after an evaluation by requiring an evaluation_id, but it does not explicitly state when to use this tool versus alternatives like 'alphalabs_calibration_report'. No when-not or alternative tools are mentioned.
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?
With no annotations provided, the description bears full responsibility for behavioral disclosure. It mentions 'recorded live results' but does not state whether the tool is read-only, requires authentication, has side effects, or any constraints. The absence of any behavioral disclosure is a significant gap.
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 with a colon and list, conveying key information without fluff. However, the structure could be improved with clearer separation of purpose and output metrics.
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 and no output schema, the description provides some context by listing output types, but it omits details like data sources, caching behavior, or performance implications. It is adequate but not comprehensive.
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, so the schema coverage is 100% vacuously. Baseline for 0 parameters is 4, and the description adds value by specifying the output metrics (Spearman rankings, median-split deltas, dead inputs), giving the agent context beyond the empty schema.
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's purpose: it determines which engine inputs predict outcomes using specific metrics (Spearman rankings, median-split deltas, dead inputs). It distinguishes itself from sibling tools like alphalabs_explain_decision by focusing on input attribution rather than explanation of a single decision.
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 alphalabs_evaluate_signal or alphalabs_explain_decision. The description does not mention prerequisites, limitations, or typical use cases.
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?
No annotations exist, so the description must carry the full burden. It only states what is listed, with no mention of side effects, idempotency, or safety. The 'Free' label is ambiguous regarding behavioral 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?
One sentence, concise and front-loaded with 'Free'. Every word earns its place, though a more structured list of output contents could improve scannability.
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 zero parameters and no output schema, the description is nearly complete for a simple listing tool. It lacks return format details, but for the use case it is adequate.
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?
With zero parameters, the baseline is 4. The description adds value by indicating the output contains products, prices, and auth model, which goes beyond the empty schema.
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 uses a specific verb 'list' and specifies the resource (AlphaLabs Intelligence products, prices, auth model). It clearly distinguishes from sibling tools which focus on calibration, evaluation, explanation, etc.
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 'Free:' prefix hints at zero cost or no authentication, but no explicit guidance on when to use versus alternatives. Sibling tool names provide implicit differentiation, but no direct usage conditions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
alphalabs_outcome_reportAInspectRecorded 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?
With no annotations, the description carries full burden. It discloses the tool returns aggregated engine telemetry (percent moves and counts only) and implies read-only access. However, it does not address staleness, caching, or any constraints beyond content.
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 with no redundancy. The first sentence lists key content; the second clarifies scope (aggregated telemetry, percent moves and counts). 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?
For a parameterless report with no output schema, the description adequately describes the data nature. It could specify time context or filtering options, but the current level is sufficient for understanding what the tool returns.
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 input schema, so the baseline is 4. The description adds no parameter information, which is appropriate as 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?
The description clearly specifies the tool provides recorded outcomes of the live pipeline's decisions, including hit rates, score-band tables, and edge metrics. It distinguishes from siblings by focusing on aggregated engine telemetry rather than calibration or explanations.
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 versus alternatives like alphalabs_calibration_report or alphalabs_explain_decision. The description only states what the tool contains, not the context for its use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
