QuantConnect MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes targeting specific resources (projects, backtests, optimizations, live algorithms, files, collaborators, authentication) with clear action verbs. However, some overlap exists between read_backtest and read_backtest_chart/read_backtest_insights/read_backtest_orders, which could cause confusion about which tool to use for specific backtest data retrieval.
Naming Consistency5/5
Excellent consistency throughout with a clear verb_noun pattern (create_project, read_backtest, update_file_content, delete_optimization, list_live_algorithms). All tools use snake_case exclusively, and verbs are appropriately chosen for their operations (create, read, update, delete, list, abort, authorize, configure, etc.).
Tool Count2/5
With 50 tools, this is an extremely large surface that feels heavy and overwhelming for the QuantConnect domain. While the domain is complex (algorithm development, backtesting, optimization, live trading), many tools could potentially be consolidated (e.g., multiple backtest/live read operations) or organized differently. The count will likely cause cognitive overload for agents.
Completeness5/5
The tool surface provides comprehensive coverage of the QuantConnect platform, including full CRUD operations for projects, files, backtests, optimizations, live algorithms, and collaborators. It covers authentication management, compilation, deployment, monitoring, and data retrieval across all major workflow stages with no apparent gaps.
Average 3.2/5 across 50 of 50 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
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- Last stable release on August 24, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('abort') but lacks details on permissions required, whether the abort is reversible, side effects (e.g., if partial results are saved), rate limits, or error conditions. This is a significant gap for a mutation tool with zero annotation coverage.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by structured sections for args and returns. However, the 'Returns' section is vague ('Dictionary containing abort result') and could be more specific, slightly reducing efficiency.
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's complexity (a mutation with no annotations) and schema richness (0% coverage, but with an output schema), the description is partially complete. It covers the basic action and parameter but lacks behavioral context and detailed return info. The output schema exists, so the description needn't fully explain returns, but overall it's adequate with clear gaps.
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 documents the single parameter ('optimization_id') and its purpose ('ID of the optimization to abort'), adding meaning beyond the bare schema. However, it doesn't specify the ID format (e.g., numeric string, UUID) or where to obtain it, leaving some ambiguity.
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?
The description clearly states the verb ('abort') and resource ('an optimization that is currently running'), making the purpose specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'delete_optimization' or 'stop_live_algorithm', which might handle related but different operations.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., the optimization must be running), exclusions (e.g., cannot abort completed optimizations), or compare to siblings like 'delete_optimization' or 'stop_live_algorithm', leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It states this creates something but doesn't disclose permissions needed, whether this is an asynchronous/long-running operation, rate limits, or what happens if optimization fails. The 'Returns' section mentions a dictionary result but gives no details about success/error responses.
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 well-structured with clear sections (Args, Returns) and reasonably concise. However, the parameter explanations could be more efficient - some are overly brief ('ID of the project to optimize') while others could benefit from examples or constraints.
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 7 parameters with 0% schema coverage, no annotations, but with an output schema present, the description is moderately complete. It covers all parameters at a basic level and acknowledges the return type, but doesn't provide enough context about optimization behavior, dependencies, or error conditions for a complex creation tool.
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 partially compensates by listing all 7 parameters with brief explanations. However, it doesn't provide crucial semantic details: what valid node_type values are, what parameters dictionary should contain, format of compile_id, or examples of output_target values like 'Sharpe Ratio'. The explanations are too basic for such complex parameters.
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?
The description clearly states the tool creates an optimization with specified parameters, providing a specific verb ('create') and resource ('optimization'). It distinguishes from siblings like 'create_backtest' or 'create_project' by focusing on optimization, but doesn't explicitly differentiate from 'update_optimization' or 'estimate_optimization_time'.
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 is provided about when to use this tool versus alternatives. The description doesn't mention prerequisites (like needing a compiled project), when to choose optimization over backtesting, or relationships with sibling tools like 'compile_project' (which provides compile_id) or 'list_optimizations'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool deletes a file, implying a destructive mutation, but does not disclose critical behavioral traits such as permission requirements, whether deletion is permanent or reversible, error conditions (e.g., if the file doesn't exist), or rate limits. This is inadequate for a destructive operation with zero annotation coverage.
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 well-structured and appropriately sized. It front-loads the purpose in a clear sentence, followed by concise parameter explanations and return value note. Every sentence adds value, with no wasted words, though minor improvements could enhance completeness.
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's complexity (destructive mutation with 2 parameters), lack of annotations, and presence of an output schema (which handles return values), the description is partially complete. It covers the basic purpose and parameters but misses behavioral context like safety warnings or error handling. The output schema reduces the need to explain returns, but more disclosure is warranted for a delete operation.
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 schema provides no parameter descriptions. The description adds basic semantics by explaining 'project_id' as 'ID of the project containing the file to delete' and 'name' as 'Name of the file to delete', which clarifies what each parameter represents. However, it does not provide format details (e.g., case sensitivity for 'name') or constraints, leaving gaps.
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?
The description clearly states the tool's purpose: 'Delete a file from a QuantConnect project.' It specifies the verb ('Delete') and resource ('a file from a QuantConnect project'), making the action unambiguous. However, it does not explicitly differentiate from sibling tools like 'delete_backtest' or 'delete_project', which would require a 5.
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?
The description provides no guidance on when to use this tool versus alternatives. It lacks context about prerequisites (e.g., authentication status), exclusions (e.g., cannot delete read-only files), or comparisons to similar tools like 'delete_backtest'. This leaves the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states it's a deletion operation, implying a destructive mutation, but doesn't disclose critical behavioral traits such as whether deletion is permanent, requires specific permissions, has side effects (e.g., removing associated files), or rate limits. This is a significant gap for a destructive tool with no annotation coverage.
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 appropriately sized and front-loaded, with the main purpose stated first followed by structured 'Args' and 'Returns' sections. Every sentence adds value, though the 'Returns' section could be more specific, but overall it's efficient with zero waste.
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's complexity (destructive mutation), lack of annotations, and an output schema that exists but isn't detailed in the description, the description is moderately complete. It covers the basic purpose and parameters but misses key behavioral context like permanence or permissions, making it adequate but with clear gaps for safe usage.
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?
The description adds minimal semantics: it explains that 'project_id' is 'The ID of the project to delete,' which clarifies the parameter's purpose. However, with 0% schema description coverage and only one parameter, this is adequate but not comprehensive—it doesn't specify format constraints or examples. The baseline is 3 since the schema lacks descriptions, and the description compensates partially.
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?
The description clearly states the verb ('Delete') and resource ('a project from QuantConnect'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'delete_backtest' or 'delete_file', which would require mentioning it's specifically for projects rather than other entities.
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 is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing project), exclusions, or comparisons with similar tools like 'delete_backtest' or 'delete_file', leaving the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states this is an estimation tool (implying it's likely read-only and non-destructive), but doesn't disclose any behavioral traits like whether it requires authentication, has rate limits, what happens with invalid inputs, or if it performs any side effects. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 well-structured and appropriately sized. It starts with a clear purpose statement, then lists parameters and return value in separate sections. Every sentence serves a purpose, though the parameter descriptions could be more informative. It's front-loaded with the main functionality.
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's moderate complexity (4 parameters including a nested object), no annotations, and an output schema (which handles return values), the description is partially complete. It covers the basic purpose and parameters at a high level, but lacks crucial context like usage prerequisites, parameter details, and behavioral constraints that would be needed for reliable tool invocation.
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 schema provides no parameter descriptions. The description lists the four parameters in the 'Args' section with brief labels (e.g., 'ID of the project to optimize'), which adds some meaning beyond the bare schema. However, it doesn't explain parameter formats, constraints, or provide examples (e.g., what 'node_type' values are valid, what 'parameters' dictionary should contain), leaving key details unclear.
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?
The description clearly states the tool's purpose: 'Estimate the execution time of an optimization with the specified parameters.' It specifies the verb ('estimate') and resource ('execution time of an optimization'), but doesn't explicitly differentiate it from sibling tools like 'create_optimization' or 'read_optimization' beyond the estimation focus.
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 is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a successful compilation first), nor does it contrast with sibling tools like 'create_optimization' (which might actually run the optimization) or 'read_optimization' (which might retrieve results). The description only states what it does, not when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists backtests but doesn't describe key behaviors: whether it's paginated, sorted, or filtered; what permissions are required; if it's read-only (implied but not stated); or error conditions. For a list operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by structured 'Args' and 'Returns' sections. There's no wasted text, though the structure is basic. Every sentence earns its place, but it could be more polished (e.g., integrating the return info into the main description).
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's low complexity (single parameter, list operation) and the presence of an output schema (which handles return values), the description is minimally complete. However, with no annotations and 0% schema description coverage, it lacks behavioral context (e.g., pagination, errors) and usage guidelines. It meets the bare minimum but leaves the agent to guess about important operational aspects.
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?
The description adds minimal semantics beyond the input schema. It explains 'project_id' as 'ID of the project to list backtests from', which clarifies the parameter's role but doesn't provide format details (e.g., integer type, where to find it) or constraints. With 0% schema description coverage and only 1 parameter, this is adequate but not comprehensive—baseline 3 is appropriate as the description compensates slightly for the schema gap.
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?
The description clearly states the verb ('List') and resource ('backtests for a project'), making the purpose specific and understandable. It distinguishes from siblings like 'read_backtest' (which gets details of a single backtest) by focusing on listing multiple backtests. However, it doesn't explicitly differentiate from 'list_optimizations' or 'list_live_algorithms' which are similar list operations for different resources.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing project), exclusions, or comparisons to siblings like 'read_backtest' (for single backtest details) or 'list_optimizations' (for listing optimizations instead). The agent must infer usage from the name and context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks behavioral details. It doesn't disclose whether this is a read-only operation, requires specific permissions, has rate limits, returns paginated results, or what happens if no filters are applied. The mention of 'optional filters' is minimal and doesn't cover behavioral traits.
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 appropriately sized with a clear purpose statement followed by structured Args and Returns sections. Every sentence adds value, though the 'Returns' line is somewhat redundant given the output schema. It's front-loaded and efficiently organized.
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's moderate complexity (3 optional parameters, list operation), no annotations, but with an output schema, the description is partially complete. It covers the basic purpose and parameters but lacks usage context, behavioral transparency, and deeper parameter semantics, leaving gaps for an AI agent to infer 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 lists the three parameters with brief examples (e.g., 'Running', 'Stopped' for status, Unix timestamp for times), adding meaning beyond the schema's generic titles. However, it doesn't explain parameter interactions, format details beyond examples, or default behaviors when null.
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?
The description clearly states the verb ('List') and resource ('live algorithms') with optional filtering. It distinguishes from siblings like 'read_live_algorithm' (singular read) and 'create_live_algorithm' (creation), but doesn't explicitly differentiate from other list tools like 'list_backtests' or 'list_optimizations' beyond the resource type.
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 is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication), compare with 'read_live_algorithm' for single-algorithm details, or specify scenarios where filtering is beneficial versus retrieving all live algorithms.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool lists optimizations but doesn't disclose behavioral traits such as whether it's read-only (implied by 'list'), pagination, rate limits, authentication needs, error conditions, or what happens if the project_id is invalid. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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 appropriately sized and front-loaded with the main purpose in the first sentence. The Args and Returns sections are structured but could be more concise (e.g., integrating into a single paragraph). No wasted sentences, though it's slightly verbose for such a simple tool.
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's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is mostly complete for basic use. However, with no annotations and 0% schema coverage, it lacks behavioral context (e.g., safety, errors) and deeper parameter guidance, making it adequate but with clear gaps.
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 schema provides no parameter descriptions. The description adds basic semantics by explaining 'project_id' as 'ID of the project to list optimizations for', which clarifies the parameter's purpose. However, it doesn't provide details like format constraints, valid ranges, or examples, leaving some ambiguity. With one parameter, this is minimally adequate.
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?
The description clearly states the verb 'list' and resource 'optimizations for a project', making the purpose specific and understandable. It distinguishes from siblings like 'read_optimization' (singular) and 'create_optimization', but doesn't explicitly differentiate from other list tools like 'list_backtests' or 'list_live_algorithms' beyond the resource name.
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 is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., if the project must exist), exclusions, or comparisons to siblings like 'read_optimization' for single optimizations or other list tools. Usage is implied by the name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool 'reads' insights, implying a read-only operation, but doesn't disclose behavioral traits like authentication needs, rate limits, error conditions, or what 'insights data' entails. The description is minimal beyond stating the basic action.
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 appropriately sized and front-loaded, starting with the core purpose followed by structured 'Args' and 'Returns' sections. Every sentence earns its place, though it could be more concise by integrating the parameter details more seamlessly.
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 complexity (4 parameters, no annotations, but an output schema exists), the description is moderately complete. It covers the basic purpose and parameters but lacks context on usage, behavioral details, and what 'insights data' means, though the output schema mitigates the need to explain return values fully.
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 value by explaining parameters in the 'Args' section, clarifying 'start' and 'end' as indices with defaults and a max range. However, it doesn't fully cover semantics like what 'insights' are or how indices relate to the data, leaving gaps given the low schema coverage.
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?
The description clearly states the verb ('read') and resource ('insights from a backtest'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'read_backtest' or 'read_live_insights', which would require more specific context about what makes backtest insights distinct.
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 is provided on when to use this tool versus alternatives. With siblings like 'read_backtest' (likely for general backtest data) and 'read_live_insights' (for live algorithm insights), the description lacks context on when this specific tool is appropriate, offering only basic parameter info without usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states 'Read' which implies a read-only operation, but doesn't disclose behavioral traits such as authentication requirements, rate limits, error conditions, or what 'available and selected nodes' entails in practice. The description is minimal and lacks context beyond the basic action.
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 appropriately sized and front-loaded with the core purpose in the first sentence. The 'Args' and 'Returns' sections are structured but could be more integrated; however, they add necessary clarity without waste. Every sentence earns its place, though slight verbosity in formatting reduces perfection.
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's complexity (simple read operation), no annotations, and an output schema exists (implied by 'Returns'), the description is minimally complete. It covers the purpose and parameter semantics but lacks behavioral context and usage guidelines. With the output schema handling return values, the description meets basic needs but has clear gaps in guidance and transparency.
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 the meaning of 'project_id' as 'ID of the project to read nodes for', which clarifies the parameter's purpose beyond the schema's title 'Project Id'. However, with only one parameter and no additional details like format or constraints, the value added is moderate, aligning with the baseline for minimal parameter info.
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?
The description clearly states the action ('Read') and the target ('available and selected nodes of a project'), which is specific and distinguishes it from siblings like 'read_project' or 'update_project_nodes'. However, it doesn't explicitly differentiate from other read operations in the sibling list, such as 'read_project' or 'read_project_collaborators', beyond the resource focus.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for selecting nodes, or comparisons to siblings like 'read_project' or 'update_project_nodes'. Usage is implied by the action and resource but lacks explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool stops a live algorithm, implying a mutation operation, but lacks details on permissions required, whether the stop is reversible, side effects (e.g., on trading positions), rate limits, or error conditions. This is inadequate for a mutation tool with zero annotation coverage.
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 appropriately sized and front-loaded, with the core purpose stated first ('Stop a live algorithm.'), followed by brief parameter and return sections. There's no wasted text, though the structure could be slightly improved by integrating parameter details more seamlessly.
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's complexity (a mutation operation with no annotations), the description is minimally complete. It covers the basic purpose and parameter, and an output schema exists, so return values needn't be explained. However, for a tool that stops live algorithms, more context on behavior and usage is warranted to compensate for the lack of annotations.
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, but it only minimally adds meaning. It explains that 'project_id' is the 'ID of the project with the live algorithm to stop', which clarifies the parameter's role beyond the schema's title ('Project Id') and type. However, it doesn't detail format constraints (e.g., integer range) or provide examples, leaving gaps in understanding.
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?
The description clearly states the action ('Stop') and target resource ('a live algorithm'), which is specific and unambiguous. However, it doesn't differentiate from sibling tools like 'liquidate_live_algorithm' or 'abort_optimization', which might have overlapping purposes in stopping algorithmic processes.
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 is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., whether the algorithm must be running), exclusions, or comparisons to siblings like 'liquidate_live_algorithm', leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states this is an update operation (implies mutation) but doesn't disclose behavioral traits like whether it overwrites or merges content, permission requirements, rate limits, or what the 'update result' contains. The description is minimal beyond basic functionality.
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 appropriately sized with a clear purpose statement followed by parameter and return documentation. It's front-loaded with the core functionality. The Args/Returns sections are structured but could be more integrated with the main description.
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 no annotations, 0% schema coverage, but an output schema exists (so return values are documented elsewhere), the description is minimally complete. It covers what the tool does and parameters but lacks behavioral context, error handling, and usage guidance needed for a mutation tool.
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%, but the description provides basic semantics for all 3 parameters (project_id, name, content). However, it doesn't add meaningful context beyond what's evident from parameter names (e.g., format of project_id, file naming conventions, content encoding). With 0% schema coverage, this is adequate but not comprehensive.
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?
The description clearly states the tool updates file content in a QuantConnect project with specific parameters. It distinguishes from siblings like 'update_file_name' (which modifies metadata) and 'create_file' (which creates new files), but doesn't explicitly contrast with 'read_file' or other file operations.
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 like 'create_file' or 'delete_file'. The description doesn't mention prerequisites (e.g., file must exist) or error conditions (e.g., what happens if file doesn't exist).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states this is an update operation, implying mutation, but doesn't disclose behavioral traits like required permissions, whether changes are reversible, rate limits, or what 'update result' entails. The description adds minimal context beyond the basic action.
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 appropriately sized and front-loaded: the first sentence states the purpose clearly. The 'Args' and 'Returns' sections are structured but slightly redundant with the schema. Every sentence adds value, though the 'Returns' line could be more specific given the output schema exists.
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 2 parameters with 0% schema coverage and no annotations, the description provides basic parameter semantics and mentions a return value. However, as a mutation tool, it lacks behavioral context (e.g., permissions, side effects). The output schema exists, so describing return values isn't needed, but overall completeness is minimal for safe use.
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 schema provides no parameter descriptions. The description compensates by documenting both parameters ('optimization_id' and 'name') with brief semantics ('ID of the optimization to update' and 'New name for the optimization'). However, it doesn't add format details (e.g., ID structure, name constraints) or examples, leaving gaps.
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?
The description clearly states the tool's purpose: 'Update the name of an optimization.' This specifies the verb ('update'), resource ('optimization'), and what is being updated ('name'). It distinguishes from siblings like 'update_backtest' or 'update_file_name' by focusing on optimizations specifically. However, it doesn't explicitly differentiate from 'update_project' or 'update_project_nodes' in terms of resource hierarchy.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing optimization), exclusions, or comparisons with sibling tools like 'create_optimization' or 'delete_optimization'. The agent must infer usage from the name and context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Update' implies mutation, the description doesn't specify required permissions, whether changes are reversible, what happens to existing permissions not mentioned, or error conditions. The return format is mentioned but not detailed.
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 efficiently structured with a clear purpose statement followed by Args and Returns sections. The main description is a single focused sentence, though the parameter documentation could be more integrated rather than in a separate section.
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?
For a mutation tool with 3 parameters and no annotations, the description provides basic purpose and parameter documentation. The presence of an output schema reduces the need to detail return values. However, it lacks important context about permissions, side effects, and relationships to sibling tools, making it incomplete for confident agent usage.
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 provides basic parameter documentation in the Args section, explaining what each parameter represents. However, it doesn't fully compensate for the schema gap - it doesn't explain parameter formats, constraints, or that 'live_control' is optional with a null default. The description adds some value but leaves important details unspecified.
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?
The description clearly states the action ('Update collaborator permissions') and resource ('in a project'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'create_project_collaborator' or 'delete_project_collaborator', but the verb 'Update' provides reasonable distinction from creation/deletion operations.
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 is provided about when to use this tool versus alternatives. The description doesn't mention prerequisites (like needing existing collaborators), when not to use it, or how it relates to sibling tools like 'create_project_collaborator' or 'delete_project_collaborator' that also manage collaborators.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Compile') and return format, but lacks details on permissions, rate limits, side effects (e.g., if compilation affects project state), or error handling, which are critical for a mutation tool in this context.
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 well-structured with a clear purpose statement followed by Args and Returns sections, making it front-loaded and efficient. However, the 'Args' and 'Returns' labels are slightly redundant with structured fields, and it could be more concise by integrating this information seamlessly.
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's complexity (a mutation operation with no annotations), the description is minimally adequate but incomplete. It covers the basic action and return format, and the output schema helps, but it lacks behavioral context like side effects or prerequisites, which are important for safe usage in this sibling set.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter 'project_id' by specifying it as 'The ID of the project to compile', which clarifies its role beyond the schema's basic type. With 0% schema description coverage and only one parameter, this adequately compensates, though it doesn't detail format 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?
The description clearly states the verb ('Compile') and resource ('a project in QuantConnect'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'read_compilation_result' or 'create_backtest', which might involve compilation indirectly, so it misses full sibling distinction.
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 is provided on when to use this tool versus alternatives. For example, it doesn't mention prerequisites like project creation or how it relates to tools like 'read_compilation_result' for checking results, leaving the agent to infer usage from context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Create a new file') but lacks critical details: it doesn't specify required permissions, whether the operation is idempotent, potential side effects (e.g., overwriting existing files), rate limits, or error conditions. The mention of 'Returns: Dictionary containing file creation result' is vague and doesn't clarify output behavior. This leaves significant gaps for a mutation tool.
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 well-structured and appropriately sized. It starts with a clear purpose statement, followed by a bullet-point list for parameters and returns, making it easy to scan. Every sentence adds value, with no redundant information. A slight improvement could be integrating the parameter explanations more seamlessly, but it's highly efficient overall.
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 complexity (a mutation tool with 3 parameters, no annotations, but an output schema exists), the description is moderately complete. It covers the basic purpose and parameters but lacks behavioral context like permissions or error handling. The output schema should handle return values, so the vague 'Returns' statement is acceptable. However, for a creation tool in a collaborative environment, more guidance on usage and constraints would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'project_id: ID of the project to add the file to', 'name: Name of the file (e.g., "main.py", "algorithm.cs")', and 'content: Content of the file'. The examples for 'name' are particularly helpful. Since the schema lacks descriptions, this compensates well, though it could detail constraints like file naming rules.
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?
The description clearly states the tool's purpose: 'Create a new file in a QuantConnect project.' It specifies the verb ('Create') and resource ('file'), and while it doesn't explicitly differentiate from siblings like 'update_file_content' or 'delete_file', the action is distinct enough given the context of file operations. However, it lacks explicit sibling comparison, preventing a perfect score.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing project), exclusions, or comparisons to siblings like 'update_file_content' or 'read_file'. Without such context, the agent must infer usage from the tool name alone, which is insufficient for optimal selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states this is a deletion operation (implying destructive action) but doesn't disclose critical details like whether deletion is permanent, requires specific permissions, has confirmation steps, or affects related data. The mention of a return value is vague ('Dictionary containing deletion result') without explaining what that entails.
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 well-structured with clear sections (Args, Returns) and uses minimal sentences. However, the 'Returns' section is somewhat vague ('Dictionary containing deletion result'), which slightly reduces efficiency as it could be more specific about what the dictionary includes.
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 this is a destructive operation with no annotations and an output schema exists (though unspecified here), the description is moderately complete. It covers the basic action and parameters but lacks crucial behavioral context (e.g., permanence, side effects) and detailed return value explanation, leaving gaps for safe agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explicitly lists both parameters with brief explanations ('ID of the project containing the backtest', 'ID of the backtest to delete'), adding meaningful context beyond the schema's 0% description coverage. This compensates well for the schema gap, though it doesn't detail ID formats or validation rules.
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?
The description clearly states the action ('Delete') and target resource ('a backtest from a project'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'delete_file' or 'delete_optimization' beyond the resource name, which prevents a perfect score.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing to identify the backtest first), exclusions, or relationships with sibling tools like 'list_backtests' or 'read_backtest' that might be needed beforehand.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'Delete[s] an optimization', implying a destructive mutation, but lacks details on permissions, reversibility, side effects, or error handling. This is inadequate for a destructive operation with zero annotation coverage.
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 front-loaded with the core purpose, followed by structured sections for Args and Returns. It's efficient with minimal waste, though the Returns section is vague ('Dictionary containing deletion result') and could be more informative.
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 a destructive tool with no annotations, 0% schema coverage, and an output schema present, the description is moderately complete. It covers the purpose and parameter semantics adequately but lacks behavioral details and usage guidelines, leaving gaps for safe agent operation.
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%, but the description compensates by explaining the single parameter 'optimization_id' as 'ID of the optimization to delete', adding clear meaning beyond the schema's title 'Optimization Id'. This is sufficient for the one parameter, though it doesn't cover format 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?
The description clearly states the action ('Delete') and resource ('an optimization'), which is specific and unambiguous. It distinguishes from siblings like 'abort_optimization' or 'delete_backtest' by focusing on optimizations, though it doesn't explicitly contrast with them.
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 is provided on when to use this tool versus alternatives like 'abort_optimization' or 'delete_backtest', nor are there any prerequisites or context for deletion mentioned. The description only states what it does without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns a dictionary without sensitive data, but lacks details on permissions, rate limits, error handling, or what specific header information is included. This is inadequate for a tool with zero annotation coverage.
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 brief and front-loaded with the core purpose, followed by a return statement. It avoids unnecessary elaboration, though the return format could be slightly more informative.
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 no parameters, annotations, but an output schema exists, the description is minimally adequate. It covers the purpose and return type, but lacks behavioral context and usage guidelines, making it incomplete for optimal agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add param details, which is appropriate, but a baseline of 4 is given since it doesn't compensate for any gaps.
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?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('information about authentication headers'), and distinguishes it from sensitive data exposure. However, it doesn't explicitly differentiate from sibling tools like 'get_auth_status' or 'authorize_connection', which prevents a perfect score.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a read operation and describes the return format, but doesn't mention authentication requirements, rate limits, error conditions, or whether the data is cached/live. For a tool accessing potentially sensitive backtest results, this leaves significant gaps in understanding its behavior.
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 well-structured with clear sections (purpose, args, returns) and uses minimal sentences. Every sentence adds value - the first establishes purpose, the args section documents parameters, and the returns section explains output. It could be slightly more concise by integrating the args/returns into flowing prose, but the structure is effective.
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 an output schema (so return values are documented elsewhere) but no annotations, the description does an adequate job. It covers parameters well and states the basic purpose, but doesn't provide enough behavioral context for a read operation that likely requires authentication and has specific usage patterns. The existence of multiple backtest-reading sibling tools makes the lack of differentiation more problematic.
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?
With 0% schema description coverage, the description must fully explain parameters. It successfully documents all three parameters (project_id, backtest_id, chart) with clear explanations of what they represent and which are required. The optional nature of 'chart' and its effect on the response is well explained. The only minor gap is not specifying the format/expected values for IDs.
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?
The description clearly states the action ('Read backtest results and statistics') and resource ('from a project'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'read_backtest_chart', 'read_backtest_insights', or 'read_backtest_orders', which appear to read specific aspects of backtests rather than comprehensive results.
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?
The description provides no guidance on when to use this tool versus alternatives like 'read_backtest_chart' or 'list_backtests'. It mentions the optional 'chart' parameter but doesn't explain when to include it or how it relates to the dedicated chart-reading sibling tool. No context about prerequisites or typical workflows is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it's a read operation, which is clear, but lacks details on permissions, rate limits, error conditions, or whether it requires the algorithm to be active. This is a significant gap for a tool with no annotation coverage.
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 appropriately sized and front-loaded with the core purpose. The Args and Returns sections are structured but could be more integrated; every sentence adds value, though it's slightly verbose with 'comprehensive' and repeated 'live algorithm.'
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's moderate complexity, no annotations, and an output schema present (which covers return values), the description is partially complete. It explains parameters and returns at a high level but lacks behavioral context and usage guidance, leaving gaps for effective agent use.
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 'project_id' identifies the project and 'deploy_id' is optional for a specific algorithm (omit for latest), adding meaningful context beyond the schema's basic types. However, it doesn't clarify data formats 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?
The description clearly states the tool reads 'comprehensive live algorithm statistics, runtime data, and details,' which is a specific verb+resource combination. It distinguishes from siblings like 'list_live_algorithms' (which likely lists multiple) and 'read_live_chart' (which focuses on charts only), though it doesn't explicitly mention these distinctions.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose it over 'list_live_algorithms' for overviews or 'read_live_chart' for specific data, nor does it specify prerequisites like needing a running algorithm.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only minimally describes behavior. It mentions default values and a max range for parameters, but doesn't cover critical aspects like authentication needs, rate limits, error conditions, or what 'live algorithm insights data' entails beyond a dictionary.
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 well-structured with clear sections for Args and Returns, and each sentence adds value. It could be slightly more concise by integrating the max range note into the 'end' parameter description, but overall it's efficient 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?
Given no annotations, 3 parameters with 0% schema coverage, and an output schema present, the description is adequate but has gaps. It covers parameters well and notes the return is a dictionary, but lacks details on authentication, error handling, or insights format, which are important for a live data 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?
Schema description coverage is 0%, so the description must compensate. It effectively explains all three parameters: 'project_id' as the project ID, 'start' as the starting index with default 0, and 'end' as the last index with default 100 and max range 100. This adds clear meaning beyond the bare schema.
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?
The description clearly states the verb 'Read' and resource 'insights from a live algorithm', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'read_backtest_insights' or 'read_live_logs', which would require a 5.
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?
The description provides no guidance on when to use this tool versus alternatives like 'read_backtest_insights' or other live algorithm reading tools. It lacks context about prerequisites or typical use cases, offering only basic parameter defaults.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the constraint 'difference must be < 250' for line range, which is useful behavioral context. However, it lacks other critical details like permissions needed, rate limits, whether logs are real-time or historical, or error conditions. For a read operation with no annotations, this is insufficient.
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 well-structured with clear sections (Args, Returns) and uses bullet-like formatting. It's front-loaded with the core purpose. Every sentence adds value, though the 'Returns' section is somewhat redundant given the output schema exists. Slightly verbose but efficient.
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 5 parameters with 0% schema coverage and no annotations, the description does a fair job explaining parameters and basic behavior. However, it lacks details on authentication, error handling, or log format specifics. The output schema exists, so return values don't need explanation. For a tool with moderate complexity, it's minimally adequate but has 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 clearly explains all 5 parameters: project_id, algorithm_id, start_line, end_line, and format, including the constraint on line difference and default for format. This adds significant meaning beyond the bare schema. It doesn't fully explain format options or line numbering specifics, but covers most semantics well.
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?
The description clearly states the tool reads logs from a live algorithm, specifying the resource (live algorithm logs) and action (read). It distinguishes from siblings like read_live_algorithm or read_live_orders by focusing specifically on logs. However, it doesn't explicitly contrast with other log-related tools (none exist in siblings), so it's not a perfect 5.
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 is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., algorithm must be running), exclusions, or comparisons to other tools. It's a basic functional statement without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a read operation, implying it's non-destructive, but doesn't cover authentication requirements, rate limits, error conditions, or what happens if the ID doesn't exist. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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 extremely concise and well-structured: a clear purpose statement followed by brief 'Args' and 'Returns' sections. Every sentence earns its place with no redundant information. It's front-loaded with the main action and efficiently communicates essential details.
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's low complexity (single parameter) and the presence of an output schema (which handles return values), the description is reasonably complete for basic usage. However, with no annotations and 0% schema description coverage, it lacks important contextual details like authentication needs, error handling, and relationship to sibling tools, making it minimally adequate.
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 by explaining that 'optimization_id' is 'ID of the optimization to read', which clarifies the parameter's purpose beyond the schema's title 'Optimization Id'. However, it doesn't provide format details (e.g., UUID, numeric), validation rules, or examples, leaving some ambiguity.
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?
The description clearly states the action ('Read') and target resource ('an optimization by its ID'), making the purpose immediately understandable. It distinguishes from siblings like 'list_optimizations' (which returns multiple) and 'create_optimization' (which creates new). However, it doesn't specify what 'optimization' means in this context (e.g., financial algorithm optimization), leaving some ambiguity.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing optimization ID), compare it to 'list_optimizations' for browsing, or indicate when 'read_optimization' is appropriate versus other read operations like 'read_backtest'. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions testing 'with current authentication,' it doesn't specify what happens during the test (e.g., whether it makes actual API calls, what errors might occur, if it affects system state, or any rate limits). For a tool with no annotation coverage, this leaves significant behavioral gaps.
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 well-structured with clear sections for purpose, arguments, and returns. It's front-loaded with the main purpose, and each sentence adds value without redundancy. The only minor inefficiency is the repetition of 'default' info that's also in the schema.
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 low complexity (2 simple parameters) and the presence of an output schema (implied by 'Returns' statement), the description is reasonably complete. It covers the purpose, parameters, and return type. However, it lacks details on authentication requirements or error handling, which could be useful given no annotations.
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%, but the description includes an 'Args' section that documents both parameters ('endpoint' and 'method') with their defaults. This adds meaningful semantics beyond the bare schema. However, it doesn't explain valid endpoint values or method options, leaving some ambiguity.
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?
The description clearly states the tool's purpose: 'Test QuantConnect API connectivity with current authentication.' This specifies the verb ('test'), resource ('QuantConnect API connectivity'), and context ('with current authentication'). However, it doesn't explicitly differentiate from sibling tools like 'get_auth_status' or 'validate_quantconnect_auth' which might serve related authentication purposes.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_auth_status' or 'validate_quantconnect_auth' that might overlap in functionality, nor does it specify prerequisites, appropriate contexts, or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is an update operation (implying mutation) but doesn't mention permission requirements, whether changes are reversible, rate limits, or what happens when only partial fields are provided. The description adds minimal behavioral context beyond the basic operation type.
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 appropriately sized and well-structured with clear sections: purpose statement, parameter explanations, and return value indication. Every sentence earns its place, though the 'Returns' section could be slightly more informative. The information is front-loaded with the core purpose stated first.
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 this is a mutation tool with no annotations but with an output schema (implied by 'Returns' statement), the description provides adequate but not comprehensive context. It covers the basic operation and parameters well, but lacks behavioral details about permissions, side effects, or error conditions. The presence of an output schema reduces the need to describe return values in detail.
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?
With 0% schema description coverage, the description must compensate - and it does well by explicitly listing all 4 parameters with clear explanations of what each represents. The 'Args' section adds meaningful semantics: project_id identifies the containing project, backtest_id specifies which backtest to update, and name/note are optional fields that can be modified. This provides good parameter understanding despite the schema gap.
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?
The description clearly states the action ('Update') and resource ('a backtest's name or note'), providing specific verb+resource pairing. It distinguishes from siblings like 'update_file_content' or 'update_project' by specifying the exact resource type (backtest) and fields (name/note). However, it doesn't explicitly differentiate from 'update_optimization' which is a similar operation on a different resource.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a backtest to exist), doesn't specify when to use this versus 'create_backtest' or 'delete_backtest', and offers no context about typical workflows. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool updates a file name, implying a mutation operation, but doesn't disclose critical behavioral traits: whether it requires specific permissions, if the update is reversible, what happens on failure (e.g., if the old file name doesn't exist), or any rate limits. The mention of a return value ('Dictionary containing update result') is minimal and doesn't detail success/error outcomes.
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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured sections for Args and Returns. Every sentence earns its place, with no redundant information. However, the 'Returns' section is vague ('Dictionary containing update result'), which slightly reduces efficiency.
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's moderate complexity (a mutation operation with 3 parameters), no annotations, and an output schema present (implied by 'Has output schema: true'), the description is partially complete. It covers the purpose and parameters well but lacks behavioral details (e.g., error handling, permissions) and relies on the output schema for return values, which is acceptable but leaves gaps in usage context.
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 provides clear semantic explanations for all three parameters: 'project_id: ID of the project containing the file', 'old_file_name: Current name of the file', and 'new_name: New name for the file'. This adds meaningful context beyond the schema's basic titles and types, though it doesn't cover constraints like valid name formats or ID ranges.
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?
The description clearly states the tool's purpose: 'Update the name of a file in a QuantConnect project.' It specifies the verb ('update'), resource ('name of a file'), and context ('in a QuantConnect project'), distinguishing it from siblings like 'update_file_content' (which modifies content rather than name) and 'create_file' (which creates rather than renames). However, it doesn't explicitly differentiate from all siblings, such as 'update_project' (which updates project-level attributes).
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing file), exclusions (e.g., not for updating file content), or comparisons to siblings like 'update_file_content' or 'delete_file'. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Update' implies a mutation operation, the description doesn't disclose important behavioral traits: whether this requires specific permissions, if changes are reversible, what happens when only one field is provided, rate limits, or error conditions. It mentions a return format but lacks details on what 'update result' contains.
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 appropriately sized and well-structured with clear sections (purpose, args, returns). Every sentence earns its place: the first sentence states the purpose, and the subsequent sections provide necessary parameter and return information. It could be slightly more concise by combining the purpose and args sections.
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 that this is a mutation tool with no annotations but with an output schema (which handles return values), the description is moderately complete. It covers the basic purpose and parameters but lacks behavioral context (permissions, side effects) and doesn't fully compensate for the missing annotations. The output schema reduces the need to explain returns, but the description should still address mutation-specific concerns.
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?
With 0% schema description coverage, the description must compensate by explaining parameters. It does this effectively: it lists all 3 parameters (project_id, name, description), specifies that project_id is required (implied by 'ID of the project to update'), and indicates name/description are optional. However, it doesn't explain parameter constraints (e.g., name length limits) or that null values mean 'no change'.
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?
The description clearly states the tool's purpose: 'Update a project's name and/or description.' This specifies the verb (update), resource (project), and fields (name, description). However, it doesn't distinguish this tool from sibling tools like 'update_backtest', 'update_file_content', or 'update_project_collaborator', which all perform updates on different resources.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing project), exclusions (e.g., what can't be updated), or when to choose this over other update tools like 'update_project_nodes'. The only implied usage is when you want to modify a project's name/description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Update') but doesn't clarify if this requires specific permissions, whether changes are reversible, or what happens to nodes not included in the dictionary. It mentions a return value but lacks details on error handling or 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core action, followed by structured Arg and Return sections. It's efficient with minimal waste, though the 'Returns' line is somewhat vague and could be more specific without adding bulk.
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 an output schema (which handles return values) but no annotations and 0% schema coverage, the description is moderately complete. It covers the basic action and parameters but lacks behavioral details like permissions or error cases, leaving gaps for a mutation tool with nested objects.
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 adds meaningful context: 'project_id' is for identifying the project, and 'nodes' is a dictionary mapping node IDs to boolean active states. This clarifies the structure and purpose beyond the bare schema, though it doesn't specify format details like node ID types.
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?
The description clearly states the verb ('Update') and resource ('active state of the given nodes'), specifying what gets updated (active state to true). However, it doesn't explicitly differentiate from sibling tools like 'update_project' or 'update_project_collaborator' beyond the node focus, missing full sibling distinction.
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 is provided. The description lacks context about prerequisites, such as whether nodes must exist or be in a specific state, and doesn't mention any exclusions or related tools like 'read_project_nodes' for checking current states.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool validates authentication and returns a dictionary, but lacks details on what validation entails (e.g., checks for tokens, permissions, or connectivity), potential errors, or side effects. For a tool with zero annotation coverage, this is insufficient, as it doesn't explain the behavioral traits beyond basic functionality.
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 concise and front-loaded, with two sentences that directly state the purpose and return value. There's no unnecessary information or redundancy. However, it could be slightly improved by integrating usage context, but it's efficient and well-structured, earning a high score for brevity and clarity.
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 0 parameters, 100% schema coverage, and an output schema (implied by 'Returns: Dictionary'), the description is minimally adequate. It covers the basic purpose and return type, but lacks details on validation specifics, error handling, or integration with siblings. For a tool with no annotations, it should provide more behavioral context to be fully complete, so it scores as the minimum viable level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate. A baseline of 4 is applied since the schema fully covers the parameters (none), and the description doesn't need to compensate, but it doesn't reach 5 as it doesn't enhance parameter understanding beyond the schema.
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?
The description clearly states the tool's purpose: 'Validate current QuantConnect authentication configuration.' It specifies the verb 'validate' and the resource 'authentication configuration,' making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_auth_status' or 'test_quantconnect_api,' which may have overlapping functions, so it doesn't reach a score of 5.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., after configuring auth), exclusions, or comparisons to siblings like 'get_auth_status' or 'test_quantconnect_api.' This lack of context leaves the agent without clear usage instructions, scoring low due to the absence of explicit or implied guidelines.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Remove' implies a destructive mutation, it doesn't specify permissions required, whether the action is reversible, side effects (e.g., notifications), or error conditions. The return value is mentioned but not detailed, leaving behavioral gaps for a mutation tool.
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 well-structured with clear sections (purpose, Args, Returns) and uses minimal sentences. The core purpose is front-loaded, and each section adds value without redundancy. It could be slightly more concise by integrating the Args explanations into a single sentence.
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 a mutation tool with no annotations, 2 parameters, and an output schema (implied by Returns), the description is moderately complete. It covers the purpose and parameters adequately but lacks behavioral details (permissions, reversibility) and doesn't fully leverage the output schema to explain the 'removal result' dictionary. More context would improve completeness.
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 explicitly lists both parameters with brief explanations ('ID of the project to remove collaborator from', 'User ID of the collaborator to remove'), adding meaningful context beyond the bare schema. However, it doesn't cover format constraints (e.g., integer/string validation) or examples.
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 specific action ('Remove a collaborator from a project') with the exact resource ('project'), distinguishing it from sibling tools like 'create_project_collaborator' and 'update_project_collaborator'. The verb 'Remove' is precise and unambiguous.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether the user must be a project owner), exclusions, or comparisons to related tools like 'update_project_collaborator' or 'delete_project'. Usage context is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'liquidate all positions' which implies a destructive financial operation, but doesn't specify whether this is reversible, what permissions are required, potential financial consequences, or rate limits. The description is insufficient for a tool with significant financial implications.
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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence serves a purpose with zero waste, making it easy to parse and understand quickly.
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 this is a destructive financial operation with no annotations and an output schema exists (so return values are documented elsewhere), the description is minimally adequate. However, for a tool that liquidates all positions in a live algorithm, more context about irreversible consequences, confirmation requirements, or financial impact would be expected.
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?
With only 1 parameter and 0% schema description coverage, the description compensates well by clearly explaining what 'project_id' represents ('ID of the project with the live algorithm to liquidate'). This adds meaningful context beyond the bare schema, though it could specify format expectations or validation rules.
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?
The description clearly states the action ('Liquidate all positions') and target resource ('in a live algorithm'), providing specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'stop_live_algorithm' or explain how liquidation differs from stopping an algorithm.
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 is provided about when to use this tool versus alternatives like 'stop_live_algorithm' or what prerequisites exist. The description only states what the tool does without context about appropriate usage scenarios or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it's a read operation and mentions the return format, but lacks details on permissions, rate limits, or error conditions. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 extremely concise and front-loaded, with two sentences that efficiently convey the action and return value without any wasted words. Every sentence earns its place by adding clear value.
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's simplicity (0 parameters, no annotations, but has an output schema), the description is minimally adequate. It covers the purpose and return format, but with no annotations and potential complexity in account status data, it could benefit from more behavioral context or usage hints to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for this scenario is 4, as the description appropriately avoids redundant information and focuses on the tool's purpose and output.
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?
The description clearly states the tool's purpose with a specific verb ('Read') and resource ('organization account status'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_auth_status' or 'get_auth_headers_info', which might have overlapping domains, so it doesn't reach the highest score.
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?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_auth_status' that might relate to account information, there's no indication of context, prerequisites, or exclusions, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions a 'max range: 100' constraint, which is useful, but lacks details on permissions, rate limits, error handling, or what the returned dictionary contains. For a read operation with zero annotation coverage, this leaves significant gaps in understanding tool behavior.
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 appropriately sized and front-loaded, with a clear purpose statement followed by structured 'Args' and 'Returns' sections. Every sentence adds value without redundancy, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no annotations, but with an output schema), the description is partially complete. It covers parameters well and notes the return type, but lacks behavioral context (e.g., safety, errors) and doesn't leverage the output schema to explain return values. It's adequate but has clear gaps in usage and transparency.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains 'project_id' as 'Project ID of the live algorithm', clarifies 'start' and 'end' as indices with defaults and a max range, and notes that only 'project_id' is required. This compensates well for the schema's lack of descriptions, though it doesn't detail parameter formats or constraints beyond the range.
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?
The description clearly states the verb ('Read') and resource ('orders from a live algorithm'), making the purpose specific and understandable. It distinguishes itself from siblings like 'read_backtest_orders' by specifying 'live' context, though it doesn't explicitly contrast with other live-related tools (e.g., 'read_live_algorithm').
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 is provided on when to use this tool versus alternatives. While the description implies usage for fetching live algorithm orders, it doesn't mention prerequisites (e.g., needing a running live algorithm) or compare it to similar tools like 'read_backtest_orders' or 'list_live_algorithms' for context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states this is a read operation, but doesn't disclose behavioral traits like authentication requirements, rate limits, error conditions, or what 'live' implies (e.g., real-time data vs. cached). The description is minimal and lacks operational context.
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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence earns its place, and it's front-loaded with the core functionality. No wasted words or redundancy.
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 an output schema (returns a dictionary), the description doesn't need to detail return values. However, with no annotations and minimal behavioral disclosure, it's incomplete for a live system tool. The description covers basics but lacks context on permissions, errors, or real-time implications.
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%, but the description compensates by explaining the single parameter 'project_id' as 'Project ID of the live algorithm', adding meaningful context beyond the schema's title 'Project Id'. With only one parameter, this is sufficient to understand its purpose, though format or validation details are missing.
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?
The description clearly states the verb 'Read' and resource 'portfolio state from a live algorithm', making the purpose unambiguous. It distinguishes this from other read operations like read_live_algorithm or read_live_orders by specifying it retrieves portfolio state specifically. However, it doesn't explicitly differentiate from all sibling tools beyond the resource scope.
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 is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., requires a running live algorithm), nor does it contrast with similar tools like read_live_algorithm or read_account. Usage context is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states this is a read operation ('List'), implying it's non-destructive, but doesn't disclose behavioral traits like authentication needs, rate limits, pagination, or error conditions. For a tool with no annotation coverage, this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose in the first sentence, followed by clear sections for Args and Returns. Every sentence earns its place without redundancy, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, read-only operation) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and incomplete behavioral disclosure, it lacks depth for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter 'project_id' by explaining it's 'ID of the project to list collaborators for', which clarifies its role beyond the schema's basic type (integer). With 0% schema description coverage and only one parameter, this adequately compensates, though it could specify format 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?
The description clearly states the verb ('List') and resource ('collaborators on a project'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'read_project' or 'read_project_nodes', which also read project-related data, so it doesn't achieve full sibling differentiation.
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 is provided on when to use this tool versus alternatives. With siblings like 'read_project' (which might include collaborators) and 'create_project_collaborator'/'delete_project_collaborator' (which manage collaborators), the description lacks context on usage scenarios or exclusions.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool clears auth configuration and returns a status dictionary, which covers basic behavior. However, it lacks details on side effects (e.g., whether this invalidates active sessions), permissions required, or error conditions, leaving gaps for a mutation tool.
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 concise and front-loaded, with the core purpose stated first and return information added efficiently. Both sentences earn their place, though it could be slightly more structured (e.g., separating behavior from returns).
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's complexity (a mutation with no annotations but an output schema), the description is moderately complete. It explains what the tool does and the return format, but lacks context on when to use it, side effects, or error handling, which would be helpful for safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for 0 parameters is 4, as the description appropriately avoids redundant information about inputs.
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?
The description clearly states the tool's purpose with a specific verb ('Clear') and resource ('QuantConnect authentication configuration'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling 'configure_quantconnect_auth' or 'validate_quantconnect_auth', which would have earned a 5.
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?
The description provides no guidance on when to use this tool versus alternatives like 'configure_quantconnect_auth' or 'validate_quantconnect_auth'. It doesn't mention prerequisites (e.g., needing existing auth to clear) or consequences, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It states this is a creation operation but doesn't disclose behavioral traits like whether this is an async/long-running process, what permissions are required, error conditions, or what happens if parameters conflict with project settings. The description mentions 'successful project compilation' as a prerequisite but doesn't elaborate on failure modes.
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 appropriately sized with clear sections (purpose, args, returns). The purpose statement is front-loaded, and each parameter explanation earns its place. Minor improvement could be integrating the example more naturally rather than parenthetically.
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 4 parameters with no schema descriptions and an output schema exists (so return values don't need explanation), the description covers parameter semantics adequately. However, as a creation tool with no annotations, it should provide more behavioral context about the operation's nature (sync/async, side effects, error handling) to be fully complete.
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?
With 0% schema description coverage, the description compensates well by explaining all 4 parameters: project_id identifies the project, compile_id comes from successful compilation, backtest_name names the backtest, and parameters provides optional dictionary with examples. This adds significant meaning beyond the bare schema.
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?
The description clearly states the tool creates a new backtest for a compiled project, specifying the verb (create) and resource (backtest). It distinguishes from siblings like list_backtests (read) and delete_backtest (delete), but doesn't explicitly differentiate from create_optimization which is a related creation operation.
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 context by mentioning 'compiled project' and requiring a compile_id from successful compilation, suggesting prerequisites. However, it doesn't explicitly state when to use this vs. alternatives like create_optimization or when not to use it (e.g., if project isn't compiled).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool reads orders, implying a read-only operation, but doesn't specify permissions needed, rate limits, pagination behavior beyond the default range, or error conditions. This leaves significant gaps for an agent to understand how to use it safely and effectively.
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 well-structured and front-loaded with the core purpose, followed by clear sections for arguments and returns. Every sentence adds value without redundancy, 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no annotations, but with an output schema), the description is reasonably complete. It covers the purpose and parameters well, and the output schema handles return values, so the description doesn't need to explain returns. However, it lacks behavioral details like error handling or usage context.
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 clearly explains all four parameters: 'project_id' and 'backtest_id' as IDs for context, and 'start' and 'end' for pagination with defaults and a max range. This adds meaningful context beyond the basic schema, though it could benefit from examples or format details.
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?
The description clearly states the verb 'read' and the resource 'orders from a backtest', making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'read_backtest' or 'read_live_orders', which would require mentioning it's specifically for historical/backtest orders versus live ones.
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?
The description provides no guidance on when to use this tool versus alternatives like 'read_backtest' (for general backtest data) or 'read_live_orders' (for real-time orders). It also lacks information about prerequisites, such as whether the backtest must be completed or accessible.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states it 'retrieves' data without addressing behavioral aspects like authentication requirements, rate limits, error conditions, or data freshness. It mentions 'live algorithm' but doesn't clarify what 'live' entails operationally.
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 efficiently structured with a clear purpose statement followed by well-organized parameter documentation and return value indication. Every sentence adds value without redundancy.
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 moderate complexity, 5 parameters with 0% schema coverage, and no annotations, the description does well by documenting all parameters semantically. However, it lacks behavioral context about the 'live' aspect and doesn't mention the output schema's existence, though the Returns section provides basic guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides clear semantic explanations for all 5 parameters beyond the schema's 0% coverage, including examples ('Strategy Equity'), defaults (count: 100), and format details (UTC timestamps in seconds). This compensates well for the schema's lack of descriptions.
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?
The description clearly states the verb 'Read' and resource 'chart data from a live algorithm', making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'read_backtest_chart' or 'read_live_algorithm', which would require a 5.
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 is provided about when to use this tool versus alternatives like 'read_backtest_chart' for historical data or 'read_live_algorithm' for general algorithm info. The description lacks any context about prerequisites or appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions this creates a deployment (implying a write/mutation operation), it doesn't disclose critical behavioral traits like required permissions, whether this is a long-running operation, potential costs, rate limits, or what happens if deployment fails. The return format is mentioned but without details.
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 well-structured with clear sections for Args and Returns. Each sentence earns its place by providing essential information. While slightly longer than minimal, the structure makes it scannable and the content is all relevant to tool understanding.
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's complexity (9 parameters, 5 required, nested objects) and no annotations, the description does a decent job but has gaps. It covers parameters well and mentions the return type, but lacks behavioral context about permissions, costs, or operational characteristics. The existence of an output schema helps, but for a deployment tool with significant implications, more context would be beneficial.
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?
With 0% schema description coverage, the description provides excellent parameter semantics that compensate well. It explains what each parameter represents (e.g., 'ID of the project to deploy', 'Compile ID from successful project compilation', 'Brokerage configuration dictionary with credentials and settings'), adds context about defaults, and clarifies optional parameters. Only minor details like format expectations for IDs are missing.
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 specific action ('Create a live algorithm deployment') with the exact resource involved. It distinguishes itself from siblings like 'create_backtest' or 'create_optimization' by specifying it's for live deployment, not backtesting or optimization.
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?
The description provides no guidance on when to use this tool versus alternatives like 'create_backtest' or 'create_optimization'. It mentions prerequisites like 'successful project compilation' but doesn't explicitly state when this tool is appropriate or what alternatives exist for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool creates a project but does not cover critical aspects like required permissions, whether the operation is idempotent, error handling, or rate limits. The mention of a 'unique' name constraint is helpful but insufficient for a mutation tool.
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 well-structured with a clear opening sentence followed by Args and Returns sections. It avoids redundancy and is appropriately sized for the tool's complexity, though the 'Returns' section could be more concise given the output schema exists.
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?
For a mutation tool with no annotations and an output schema, the description covers the basic purpose and parameters adequately. However, it lacks behavioral details like side effects or error conditions, making it incomplete for safe agent use despite the output schema handling return values.
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?
With 0% schema description coverage, the description compensates by explaining all three parameters: 'name' (must be unique), 'language' (options and default), and 'organization_id' (optional with default behavior). This adds meaningful context beyond the bare schema, though it could detail format constraints like length limits.
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 ('Create a new project') and resource ('in your QuantConnect organization'), making the purpose specific and unambiguous. It distinguishes from siblings like 'update_project' or 'read_project' by focusing on creation rather than modification or retrieval.
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?
The description provides no guidance on when to use this tool versus alternatives like 'update_project' or 'create_file', nor does it mention prerequisites such as authentication or organizational context. It lacks explicit when/when-not instructions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Add a collaborator') but doesn't mention whether this requires authentication, what happens on failure (e.g., if the user doesn't exist), or if there are rate limits. This leaves significant gaps in understanding the tool's behavior beyond basic functionality.
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 well-structured and front-loaded with the core purpose, followed by organized sections for arguments and returns. Every sentence adds value without redundancy, and the formatting enhances readability, making it efficient for the agent to parse.
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's complexity (mutation with 4 parameters), no annotations, and an output schema present, the description covers the purpose and parameters well but lacks behavioral context like error handling or permissions. The output schema handles return values, but without annotations, the description should do more to explain mutation risks or prerequisites.
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 description provides detailed parameter information in the 'Args' section, explaining each parameter's purpose and default values. With 0% schema description coverage, this fully compensates by adding essential semantics beyond the bare schema, making parameters clear and actionable for the agent.
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?
The description clearly states the action ('Add a collaborator to a project') with the specific resource ('project'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'update_project_collaborator' or 'delete_project_collaborator', which would require a 5.
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?
The description provides no guidance on when to use this tool versus alternatives like 'update_project_collaborator' or 'delete_project_collaborator'. It also lacks information about prerequisites, such as whether the user needs specific permissions or if the project must exist, leaving the agent without contextual usage instructions.
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?
With no annotations provided, the description carries the burden of behavioral disclosure. It states the tool returns a dictionary with authentication status information, which implies a read-only, non-destructive operation, but doesn't detail aspects like rate limits, error conditions, or what specific information is included (e.g., tokens, expiry). This is adequate but lacks depth for a tool with no annotation support.
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 brief and front-loaded, stating the purpose in the first sentence and the return format in the second. It avoids unnecessary words, but the second sentence could be integrated more smoothly (e.g., 'Returns a dictionary...'), making it slightly less polished.
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 low complexity (0 parameters, no annotations, but has an output schema), the description is reasonably complete. It covers what the tool does and the return type, and with an output schema, it doesn't need to detail return values. However, it could benefit from more context on authentication specifics or usage scenarios to enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, but it could have mentioned any implicit inputs (e.g., context-based authentication). Since there are no parameters, a baseline of 4 is applied, as the description doesn't need to compensate for gaps.
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?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('current authentication status and configuration'), making it understandable. However, it doesn't explicitly distinguish this from sibling tools like 'get_auth_headers_info' or 'test_quantconnect_api', which might also relate to authentication, so it misses full differentiation.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., after authorization attempts), or exclusions, leaving the agent to infer usage from the purpose alone without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool configures credentials but fails to describe critical traits like whether this persists authentication state, requires specific permissions, or has side effects (e.g., overwriting existing config). This leaves significant gaps for a mutation tool.
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 appropriately sized and front-loaded, starting with the core purpose followed by structured parameter and return details. Every sentence adds value, though the 'Args:' and 'Returns:' sections could be integrated more seamlessly into the flow, slightly affecting structure.
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's complexity (authentication configuration with 3 parameters) and the presence of an output schema (which covers return values), the description is moderately complete. It explains parameters well but lacks behavioral context and usage guidelines, making it adequate but with clear gaps for effective agent use.
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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose and source (e.g., 'user_id: Your QuantConnect user ID (from email)'), compensating fully for the schema's lack of documentation and providing clear, actionable context for all three 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?
The description clearly states the specific action ('Configure') and resource ('QuantConnect API authentication credentials'), distinguishing it from sibling tools like 'clear_quantconnect_auth' or 'validate_quantconnect_auth'. It precisely identifies what the tool does without being vague or tautological.
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?
The description provides no guidance on when to use this tool versus alternatives like 'authorize_connection' or 'test_quantconnect_api'. It lacks context about prerequisites, such as whether this should be called before other operations or if it's part of a setup flow, leaving the agent with no usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns 'chart data or loading status', hinting at possible async behavior, but fails to detail critical aspects like authentication requirements, rate limits, error conditions, or data format specifics.
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 well-structured with a brief purpose statement followed by organized parameter and return sections. It's appropriately sized, though the 'Returns' section could be slightly more detailed given the output schema exists.
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?
For a read operation with an output schema, the description covers parameters thoroughly but lacks behavioral context (e.g., permissions, errors). The presence of an output schema reduces the need to detail return values, but without annotations, more operational guidance would be beneficial.
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?
Given 0% schema description coverage, the description fully compensates by explaining all 6 parameters with clear semantics, including examples (e.g., 'Strategy Equity' for 'name'), defaults ('count: 100'), and optionality. It adds significant value beyond the bare 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 specific action ('Read chart data') and resource ('from a backtest'), distinguishing it from sibling tools like 'read_backtest' or 'read_live_chart'. It precisely identifies what the tool does without being vague or tautological.
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?
The description provides no guidance on when to use this tool versus alternatives like 'read_backtest' or 'read_live_chart'. It lacks context about prerequisites, such as whether the backtest must be completed, or any exclusions for its use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool reads compilation results but doesn't describe important behavioral aspects like whether this is a read-only operation, if it requires authentication, potential rate limits, or what happens if the compilation is still in progress. The description adds minimal context beyond the basic 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?
The description is efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence adds value: the first establishes context, the parameter explanations are necessary, and the return statement provides output expectations. No wasted words or redundancy.
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 an output schema (though not shown here), the description doesn't need to detail return values. It covers the essential purpose and parameters adequately. However, for a tool with no annotations and 2 required parameters, it could benefit from more behavioral context about authentication requirements or error conditions.
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?
With 0% schema description coverage, the description compensates well by explaining both parameters: 'project_id: The ID of the project that was compiled' and 'compile_id: The compile ID returned from compile_project'. This adds crucial semantic meaning that the schema alone doesn't provide, though it doesn't cover format details like integer 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?
The description clearly states the action ('Read the result of a compilation job') and resource ('in QuantConnect'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from other 'read_' siblings like read_backtest or read_optimization, which would require a 5.
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 by referencing 'compile_id returned from compile_project', suggesting this tool should be used after compilation. However, it doesn't provide explicit guidance on when to use this versus alternatives like checking project status directly, nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool reads files, implying a read-only operation, but doesn't disclose behavioral traits like permissions needed, error handling details, rate limits, or whether it returns raw content or metadata. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a structured 'Args' and 'Returns' section. Every sentence adds value without redundancy, 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, no nested objects) and the presence of an output schema (which handles return values), the description is mostly complete. It covers the dual behavior and parameter meanings. However, it lacks context on errors or operational limits, which could be useful despite the output schema.
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 adds meaningful semantics: 'project_id' is explained as 'ID of the project to read files from', and 'name' as 'Optional name of specific file to read. If not provided, reads all files.' This clarifies purpose and default behavior, though it doesn't detail formats (e.g., string constraints). Given the coverage gap, it does well but not perfectly.
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?
The description clearly states the tool's purpose: 'Read a specific file from a project or all files if no name provided.' It specifies the verb ('Read') and resource ('file from a project'), and distinguishes its dual behavior (specific vs. all files). However, it doesn't explicitly differentiate from siblings like 'read_project' or 'read_backtest', which lowers it from a 5.
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 through the parameter explanation: use with 'name' for a specific file or without for all files. However, it lacks explicit guidance on when to choose this tool over alternatives (e.g., 'read_project' for project metadata) or any prerequisites. This makes it adequate but with gaps in sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'authorize' which implies a write/mutation operation, but does not disclose critical traits such as required permissions, whether this is a one-time or recurring authorization, potential rate limits, error handling, or what happens if authorization fails. The description lacks behavioral details beyond the basic 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?
The description is efficiently structured with a clear purpose statement followed by separate sections for Args and Returns. Each sentence earns its place by providing essential information without redundancy. The formatting enhances readability and information retrieval.
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 complexity of an authorization tool with no annotations, 2 parameters (0% schema coverage), and an output schema, the description is moderately complete. It covers purpose and parameters adequately, and the output schema handles return values, but it lacks behavioral context (e.g., security implications, error cases) which is important for a mutation 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?
Schema description coverage is 0%, so the description must compensate. It provides meaningful semantics for both parameters: 'brokerage_id' is explained as a 'Brokerage identifier' with an example, and 'credentials' as a 'Dictionary containing brokerage-specific credentials'. This adds significant value beyond the bare schema, though it could elaborate on credential formats or requirements.
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 specific action ('Authorize an external connection') and the resource ('with a live brokerage or data provider'), distinguishing it from siblings like 'configure_quantconnect_auth' or 'get_auth_status' which handle different aspects of authentication. It uses precise terminology that indicates a live authorization process.
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 for authorizing external connections, but does not explicitly state when to use this tool versus alternatives like 'configure_quantconnect_auth' (which might set up auth parameters) or 'get_auth_status' (which checks status). No exclusions or prerequisites are mentioned, leaving usage context somewhat implied rather than clearly defined.
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?
No annotations are provided, so the description carries the full burden. It describes the read/list behavior and conditional logic based on the ID parameter, which is useful. However, it lacks details on permissions, rate limits, pagination for the list view, or error handling, leaving behavioral gaps for a tool with no annotation coverage.
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 well-structured and front-loaded with the core functionality in the first sentence. The 'Args' and 'Returns' sections are clear and efficient, with no wasted words. Every sentence adds value, making it easy to parse quickly.
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 no annotations, 0% schema coverage, but an output schema exists, the description is reasonably complete. It covers purpose, usage, parameter semantics, and return values. However, it could improve by addressing permissions or list pagination, though the output schema mitigates some gaps in return format details.
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 the 'project_id' parameter's semantics: optional, used to get specific details, and if omitted, returns a list. This adds crucial meaning beyond the schema's type/format, though it doesn't specify ID format constraints or list behavior 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 tool's purpose with specific verbs ('Read project details' and 'list all projects') and distinguishes it from siblings like 'create_project' or 'update_project'. It explicitly mentions the resource ('project') and scope ('by ID' or 'all projects'), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'by ID or list all projects if no ID provided'. It distinguishes usage based on parameter presence, though it doesn't name alternatives (e.g., 'list_backtests' for other resources), which is acceptable given the clear conditional logic.
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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