alpha-forge-mcp

Official

by alforge-labs

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes. run_optimize and run_walk_forward both optimize but are differentiated by description (single vs. walk-forward). get_result, get_strategy, and get_journal serve different retrieval needs. Minor overlap but clear descriptions guide selection.
Naming Consistency4/5
Tools predominantly follow verb_noun pattern with underscores (e.g., run_backtest, get_strategy). One tool, exploration_status, uses noun_noun, breaking the pattern slightly. Overall consistent and readable.
Tool Count5/5
17 tools are well-scoped for a quantitative backtesting platform, covering data fetching, strategy management, backtesting, optimization, Monte Carlo, walk-forward, and results. Each tool has a clear role without bloat.
Completeness4/5
The tool surface covers the full lifecycle from data fetch to strategy registration, backtesting, optimization, and result analysis. Minor gaps like missing delete/update tools for strategies or results, but core workflows are intact.
Average 4.2/5 across 17 of 17 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- 21 of 21 community issues answered or closed in the last 6 months
- 31 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly and idempotent. Description confirms generation with no mutation, but adds no new behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with clear verb and output format. Could be slightly more structured but remains concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Basic overview is provided, but missing parameter explanations and usage context. Output schema exists but description doesn't detail return structure fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and description does not explain parameters (e.g., what 'with_webhook' does). It only mentions strategy_id in output, not input semantics.
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 clearly states the tool generates Pine Script v6 for a strategy and returns the script, distinguishing it from sibling tools like run_backtest or run_optimize.
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?
No guidance on when to use this tool versus alternatives (e.g., for script generation before backtesting). No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds that results are 'saved backtest results' and optional filtering, but does not disclose additional behavioral traits beyond annotations. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It could be improved by adding a bit more detail without becoming verbose, but it is concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description doesn't need to explain return values. However, for a tool with many siblings, the description is minimal and could clarify what 'results' refer to (e.g., backtest summary metrics). Adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only mentions 'optionally filtered by strategy_id' without explaining what strategy_id represents or providing format/syntax details. This adds minimal meaning beyond the 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 clearly states the tool lists saved backtest results with optional filtering by strategy_id. It uses specific verb 'list' and resource 'saved backtest results', differentiating from 'get_result' (single) and 'list_strategies' (strategies).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions optional filtering but provides no explicit guidance on when to use this tool versus alternatives like 'get_result'. Context implies listing multiple results, but no when-not or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds 'full JSON definition' but does not elaborate on behavior beyond that. It is 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?
The description is a single sentence of 11 words with no redundancy. It is appropriately concise for a simple get-by-ID operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and the tool's simplicity (one parameter, no errors or prerequisites mentioned), the description is mostly sufficient. However, it could mention that the strategy must exist.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 only repeats the parameter name ('strategy_id') without adding format, source, or validation details.
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 clearly states the action ('Get'), the resource ('full JSON definition of a registered strategy'), and the identifier ('by its strategy_id'). This distinguishes it from siblings like list_strategies and get_indicator.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a specific strategy's full definition is needed, but it provides no explicit guidance on when not to use it or how it compares to alternatives like list_strategies.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds useful behavioral info beyond annotations: mentions 300-second timeout and progress reporting. No contradiction with annotations (readOnlyHint=false, destructiveHint=false are consistent with run action).
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?
Three sentences, each adding distinct value: purpose, prerequisite, performance details. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers key aspects: required prerequisites, runtime behavior, and hints at return (via output schema). Missing details about error states or what happens if cache is missing, but generally sufficient.
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?
With 0% schema description coverage, the description adds some value (date format YYYY-MM-DD, lists required params) but does not fully explain each parameter's meaning or constraints.
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?
Clearly states it runs a backtest for a symbol with a registered strategy. However, does not distinguish from siblings like run_optimize or run_monte_carlo, which are different backtest variants.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a prerequisite (call fetch_data first) and mentions long-running behavior with timeout. But lacks guidance on when to use this vs. other backtest-related sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds value by specifying the contents (snapshots, runs, tags, notes) beyond what annotations provide. However, it does not disclose limitations or error conditions.
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?
Single sentence of 14 words, front-loaded with the main purpose. No extraneous information; every part is essential.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no nested objects, output schema exists), the description is mostly complete. It lists the journal components, though it omits prerequisite conditions (e.g., strategy must exist) and does not clarify if the output is paginated.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter (strategy_id) with 0% description coverage. The description merely restates that it is 'for a strategy_id' without explaining its format, source, or constraints, leaving the agent with insufficient semantic information.
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 clearly states the action ('Get'), the resource ('full journal'), and the qualifier ('for a strategy_id'). It lists included components (snapshots, runs, tags, notes), distinguishing it from siblings like list_journals which likely provide a summary.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
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. While the description implies it is for detailed retrieval of a single strategy's journal, it does not mention exclusions or alternatives like list_journals or get_strategy.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, idempotentHint=true, and openWorldHint=false. The description adds context by specifying that the tool returns metrics and trades, which is beyond the annotations. It does not contradict 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?
A single, front-loaded sentence efficiently conveys the tool's purpose and the key parameter nuance. No superfluous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter and an output schema (not shown), the description is minimal but sufficient for a simple retrieval tool. However, it lacks guidance on how this tool differs from siblings like get_strategy or list_results, which may be needed for correct selection.
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 coverage is 0%, so the description carries the burden. It adds that result_id can be a strategy_id or run_id, providing some meaning beyond the schema. However, it does not specify format, constraints, or example values, leaving ambiguity.
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 clearly states the tool retrieves 'metrics and trades' for a saved backtest result, distinguishing it from sibling tools like get_strategy or list_results by specifying the resource and action. The parenthetical on result_id clarifies the valid identifier types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that result_id can be a strategy_id or run_id, but does not explicitly state when to use this tool over alternatives like get_strategy or list_results. No exclusions or contextual cues for selection are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows this is a safe, repeatable read. The description adds that it returns a coverage map and that goal defaults to 'default'. This is adequate but does not disclose further behavioral traits (e.g., performance, auth limits) beyond the 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?
Two concise sentences front-loaded with the main action. No redundant words. Efficient use of text.
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?
Given the low complexity (1 optional param, no required params, output schema exists), the description is complete. It covers purpose, parameter behavior, and default. No critical gaps.
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?
Schema description coverage is 0%, so the description must compensate. It explains that 'goal' filters by exploration goal and defaults to 'default'. This adds meaning beyond the schema's type-only definition. However, it could be more specific (e.g., possible values, effect of null).
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 clearly states the tool's purpose: 'Show the strategy-exploration coverage map (explored vs. untried combos).' This is a specific verb-resource pair that distinguishes it from sibling tools like run_optimize or fetch_data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions the optional goal parameter and its default, but does not provide explicit when-to-use guidance or comparisons with sibling tools. The context signals indicate many sibling tools, but the description offers no direction on when to choose exploration_status over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds behavioral details beyond annotations: 'Long-running: up to a 600-second timeout; reports progress to capable clients'. Annotations are neutral (readOnlyHint=false, destructiveHint=false), and description does not contradict them.
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?
Three efficient sentences: purpose, defaults/workflow, runtime. No redundant information. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists, so return values need not be described. Description covers runtime and workflow context. Minor gap: lacks mention of prerequisites like having run optimization first (though implied by workflow).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, description only provides defaults for two optional parameters ('windows defaults to 5, metric to sharpe_ratio'). No explanation of required parameters symbol and strategy_id. Insufficient compensation for schema gaps.
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?
Description clearly states verb 'Run' and resource 'walk-forward optimization for symbol', with explicit purpose 'out-of-sample robustness check'. Distinguishes from siblings like run_backtest and run_optimize.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: 'Run it after run_optimize to compare in-sample vs out-of-sample behaviour (the optimize_and_verify workflow)'. Provides clear usage context, though does not list all when-not scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses important behavioral traits not covered by annotations: it is long-running (up to 600s), reports progress to capable clients, and has a save option that persists data (implies writing). Annotations indicate non-read-only and non-destructive, which aligns, but the description adds specific details about execution duration and progress reporting.
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?
The description is concise with three sentences, each adding value. It front-loads the core purpose, then explains save behavior and runtime characteristics. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters with no schema descriptions, the description covers main functionality, save parameter details, and runtime behavior. It doesn't explain return values (output schema exists) or 'trials' parameter, but overall it provides sufficient context for an agent to use the tool correctly.
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 0%, so the description must compensate. It adds meaning for 'metric' (default sharpe_ratio) and 'save' (purpose and default), but provides no extra guidance for 'strategy_id', 'trials', or 'symbol' beyond their names. This partial compensation yields a score of 3.
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 clearly states the action ('optimize strategy parameters with Optuna'), the resource (strategy parameters for a given symbol), and the default metric. It differentiates from sibling 'apply_optimization' by implying that this tool generates the optimization result which can then be applied.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the save parameter's role and how it relates to 'apply_optimization', providing context on when to persist results. It also mentions the long-running nature and timeout, hinting at appropriate usage. However, it lacks explicit guidance on when not to use this tool or contrasts with other backtesting siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds marginal value by listing returned fields. No behavioral traits beyond annotations are disclosed.
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?
Single sentence, front-loaded with action, no redundancy. Every word adds value.
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?
Given no parameters, annotations cover safety, and there is an output schema, the description is complete. It tells what the tool returns, sufficient for an agent.
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?
No parameters exist, and schema coverage is 100%. Baseline 4 is appropriate as description does not need to add parameter info.
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 clearly states the verb 'List', the resource 'registered AlphaForge strategies', and the specific fields returned. It distinguishes from sibling 'get_strategy' which presumably returns a single strategy's details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining an overview of all strategies. No explicit alternatives or when-not-to-use, but given the simplicity and no parameters, it's adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description need not repeat. The description adds no extra behavioral details but is consistent.
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?
Single sentence, no unnecessary words, perfectly front-loaded. Every word earns its place.
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 list tool with no parameters and an output schema, the description is complete. It states the input (none) and output (list of strategies with journals).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is 100%. The description adds meaning by specifying what is listed (strategies with journals). Baseline for 0 params is 4, but the clarity elevates it.
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 clearly states the tool lists strategies that have a journal, distinguishing it from siblings like list_strategies (all strategies) and get_journal (individual journal).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
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 list_strategies or get_journal, though the purpose implies selection. Usage context is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds behavioral context beyond annotations: internal steps (validated, written to temp file), progress reporting, and execution timeout. No contradiction with annotations; they are consistent with a non-read-only, non-idempotent write operation.
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?
Description is concise with two main sentences plus a brief note. Every sentence adds value: first states action and clarification, second details processing, third adds context on prerequisite, progress, and timeout. No unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given an output schema exists, the description need not detail returns. It covers validation, file writing, prerequisite for other tools, progress, and timeout. Could be more complete by mentioning error handling or size limits, but adequate for an AI agent.
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?
Schema coverage is 0%, so description must compensate. It explains json_body as a full strategy-definition JSON string, and describes what happens to it (validated, written to file). Does not detail required fields within the JSON, but provides sufficient meaning for an agent to understand the parameter.
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 clearly states the verb 'Register' and the resource 'a strategy', and distinguishes itself by noting it takes JSON body, not a file path. It also mentions it's a prerequisite for run_backtest/run_optimize, setting it apart from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use (to register a strategy for backtesting) and what to pass (full JSON string). Implicitly tells when not to use (if already registered). Mentions validation and prerequisite, but lacks direct comparison to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds the important behavioral detail that the tool never fails on missing binary, returning binary_found=false instead. It also outlines the return fields, enhancing transparency beyond 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?
The description is very concise with two short paragraphs. Every sentence adds value: purpose, return structure, and edge-case behavior. No wasted words.
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?
With no parameters, a comprehensive description of behavior, failure mode, and return structure, plus relevant annotations, the description is fully adequate for an agent to correctly invoke and interpret the tool.
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?
There are no parameters, and schema coverage is 100% (trivially). The description does not need to add parameter info, and it doesn't. Baseline is 4.
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 clearly states the tool's function: report alpha-forge capabilities and prerequisites before use. It specifies reading version and doctor status, and distinguishes itself from siblings by being a triage tool for pre-check.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using the tool 'before use' of alpha-forge, providing clear context. However, it does not explicitly mention when not to use it or compare with alternatives among siblings, which would make it a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly and idempotent behavior. The description adds that the tool returns metadata (description, parameters, output, example) and does not run calculations. No contradictions.
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?
Two sentences, front-loaded with the core purpose, and no extraneous information. Every sentence adds value.
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?
With an output schema present and a single parameter, the description covers what the tool returns and the input format. It fully addresses the tool's scope for an agent.
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?
Schema has no description for the 'indicator' parameter (0% coverage). The description adds meaning by stating it is the indicator name and gives examples (RSI, MACD), which is helpful for selecting the correct value.
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 clearly states it retrieves metadata for a technical indicator, specifies the resource, and provides examples (RSI, MACD). It also explicitly clarifies what it does not do (compute calculations), leaving no ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that this tool is only for metadata and does not compute, helping agents decide when not to use it. However, it does not explicitly name sibling tools as alternatives, though the context makes the distinction clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds behavioral context beyond annotations: mentions caching, progress reporting, and execution timeout. No contradiction 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?
Three concise sentences, front-loaded with main purpose, no fluff.
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?
Covers purpose, prerequisite, param details, behavioral notes; output schema exists so return format is sufficiently described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Provides specific examples and default for period, and clarifies no start/end parameters, adding significant value over the schema which has no descriptions and 0% coverage.
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?
Clearly states fetching and caching historical OHLCV for a symbol, and positions it as a prerequisite for run_backtest, distinguishing it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to run this before run_backtest and explains that CLI has no start/end dates, but does not discuss explicit alternatives or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds behavioral information beyond annotations: long-running, progress reporting, execution timeout. Annotations indicate non-readOnly and non-destructive, which the description complements without contradiction.
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?
Concise, front-loaded purpose, then prerequisite, defaults, output, and behavioral notes. No extraneous words; each sentence adds value.
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?
Covers purpose, prerequisites, parameters, output metrics, and runtime behavior. With an output schema existing, the description provides sufficient context for the agent to understand and use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Compensates for 0% schema description coverage by explaining result_id as strategy_id/run_id and simulations defaulting to 1000. This fully clarifies both parameters.
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?
Clearly states the tool runs a Monte Carlo simulation from a saved backtest result, resamples trades, and provides risk metrics. Distinguishes from siblings like run_backtest and run_optimize by requiring their saved output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly specifies prerequisite (saved result from run_backtest/run_optimize) and default simulation count. Does not mention exclusions or alternatives but provides clear context for appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate mutating but not destructive. The description adds important context: non-interactive (--yes), return shape {result_file, strategy_id, output}, progress reporting, execution timeout. No contradictions 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?
Four sentences, front-loaded with purpose, no wasted words. Each sentence adds unique value: prerequisite, execution mode, return, follow-up.
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 tool with 2 params and an output schema (mentioned return fields), the description covers prerequisites, execution behavior, and follow-up. No gaps given the simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, but the description fully explains both params: result_file is the saved_path from run_optimize, and strategy_id is implied from the prerequisite. This adds meaning 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 clearly states the action: 'Apply an optimization result file to a strategy', specifies the naming convention for the output ('saves `<strategy_id>_optimized`'), and distinguishes this tool from siblings like run_optimize which produce the result file.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Prerequisite is explicitly given: 'run_optimize(save=true) — result_file is its saved_path'. It also notes non-interactive execution and suggests follow-up with generating Pine Script. No explicit when-not-to-use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![alpha-forge-mcp MCP server](https://glama.ai/mcp/servers/alforge-labs/alpha-forge-mcp/badges/card.svg)](https://glama.ai/mcp/servers/alforge-labs/alpha-forge-mcp)

Score Badge

Copy to your README.md:

[![alpha-forge-mcp MCP server](https://glama.ai/mcp/servers/alforge-labs/alpha-forge-mcp/badges/score.svg)](https://glama.ai/mcp/servers/alforge-labs/alpha-forge-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/alforge-labs/alpha-forge-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server