Databento MCP

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation3/5
Most tools have distinct purposes, but there is some overlap that could cause confusion. For example, get_account_status and get_metrics both provide server performance information, and analyze_data_quality and quick_analysis both assess data quality. However, descriptions help differentiate them, and the majority of tools target unique operations.
Naming Consistency4/5
Tool names follow a consistent verb_noun pattern throughout, such as get_historical_data, list_datasets, and submit_batch_job. There are minor deviations like clear_cache (verb_adjective) and health_check (noun_noun), but the overall naming is predictable and readable.
Tool Count2/5
With 30 tools, the count is too high for the server's purpose of market data access and analysis. Many tools feel redundant or overly granular, such as separate tools for reading DBN and Parquet files or multiple batch job operations, which could overwhelm agents and indicate poor scoping.
Completeness4/5
The tool surface is largely complete for market data operations, covering data retrieval, analysis, batch processing, and metadata. Minor gaps exist, such as no direct tool for updating or deleting data, but agents can work around this given the domain's focus on read-heavy operations.
Average 3.2/5 across 30 of 30 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 only states the action 'search' without detailing expected behavior, such as search scope, result format, pagination, or error handling. This is inadequate for a tool with parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It is front-loaded and efficiently conveys the core action, making it highly concise and well-structured for its purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no annotations, and no output schema, the description is insufficient. It lacks details on behavior, result expectations, and usage context, failing to compensate for the missing structured information, which is critical for effective 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 100%, so the schema fully documents parameters like 'dataset', 'symbols', and 'start'. The description adds no additional meaning beyond the schema, such as explaining relationships between parameters or search semantics, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the verb 'search' and resource 'instruments in a dataset', which clarifies the basic purpose. However, it lacks specificity about what 'instruments' means (e.g., financial instruments, symbols) and does not distinguish this tool from sibling tools like 'resolve_symbols' or 'get_symbol_metadata', making it vague in context.
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 does not mention prerequisites, exclusions, or comparisons to siblings such as 'resolve_symbols' or 'get_historical_data', leaving the agent without context for 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool cancels jobs but doesn't mention important behavioral aspects: whether cancellation is reversible, what permissions are required, if there are rate limits, how long cancellation takes, or what happens to associated resources (e.g., files). The description adds basic return information but lacks critical operational context 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, parameters, returns, example) and uses bold formatting effectively. The core purpose is stated upfront in the first sentence. While the parameter and return sections are somewhat redundant given the schema coverage, they're organized efficiently. The example adds practical value without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 no output schema, the description is incomplete. It mentions return values but doesn't specify their format or structure. It doesn't cover error conditions, side effects, or dependencies. Given the tool's potential impact (canceling jobs) and lack of structured metadata, the description should provide more comprehensive operational context to be complete.
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 100%, so the schema already fully documents the single parameter 'job_id'. The description repeats the parameter documentation verbatim ('The batch job ID to cancel'), adding no additional meaning beyond what the schema provides. With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter 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 ('Cancel') and target resource ('a pending or processing batch job'), making the purpose immediately understandable. It distinguishes from siblings like 'submit_batch_job' and 'list_batch_jobs' by focusing on cancellation rather than creation or listing. However, it doesn't explicitly differentiate from potential alternatives like 'stop_batch_job' or 'abort_batch_job' if they existed.
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. While it mentions 'pending or processing batch job', it doesn't specify prerequisites (e.g., job must be in a cancellable state), exclusions (e.g., cannot cancel completed jobs), or compare it to related tools like 'get_batch_job_files' for checking job status first. Usage context 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the action ('Clear') which implies a destructive operation, but doesn't specify whether this requires special permissions, affects system performance, has rate limits, or what happens after execution (e.g., cache repopulation). For a potentially destructive tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that communicates the core purpose without any wasted words. It's appropriately sized for a simple tool and gets straight to the point with efficient language.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a potentially destructive cache-clearing operation with no annotations and no output schema, the description is insufficient. It doesn't explain what 'clearing' entails, potential side effects, or what confirmation/response to expect. Given the tool's nature and lack of structured metadata, more contextual information would be valuable.
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 doesn't mention any parameters, but the input schema has 100% description coverage for its single parameter 'expired_only'. The schema already documents this parameter well with a clear description and default value, so the description doesn't need to compensate. This meets the baseline expectation when schema coverage is high.
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 ('Clear') and the target resource ('API response cache'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its siblings, as none of the other tools appear to be cache-related operations, so this isn't a major deficiency.
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, prerequisites, or typical scenarios. It doesn't mention whether this should be used for performance troubleshooting, data freshness issues, or other contexts, leaving the agent with minimal 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 the full burden of behavioral disclosure. It states the conversion action but doesn't mention side effects (e.g., file creation, overwriting), performance characteristics (e.g., speed, memory usage), error handling, or output format details. For a file conversion 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's front-loaded and appropriately sized for a straightforward conversion tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of file format conversion, lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like error conditions, output file structure, or performance implications. For a tool that modifies data formats, more context is needed to use it effectively.
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 100%, so the schema fully documents all three parameters (input_path, output_path, compression) with descriptions, enums, and defaults. The description adds no additional parameter semantics beyond what's in the schema, such as file format details or compression trade-offs. Baseline 3 is appropriate when the schema does the heavy lifting.
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 ('Convert') and resource ('DBN file to Parquet format'), making the purpose immediately understandable. However, it doesn't distinguish this tool from the sibling 'export_to_parquet' tool, which might have overlapping functionality. The description is specific but lacks 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?
The description provides no guidance on when to use this tool versus alternatives like 'export_to_parquet' or 'read_dbn_file'. It doesn't mention prerequisites, such as needing an existing DBN file, or exclusions, like whether it works with streaming data. Usage is implied by the action 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'export' implies a write operation, the description doesn't mention important behavioral aspects like whether this is a synchronous or asynchronous operation, potential file size limitations, authentication requirements, rate limits, or what happens if the output path already exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core functionality without any wasted words. It's appropriately sized and front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 7 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (e.g., success/failure status, file metadata), doesn't mention performance characteristics or limitations, and provides no context about how this fits with sibling tools in the data processing workflow.
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 input schema has 100% description coverage, providing clear documentation for all 7 parameters. The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline of 3 for adequate but not exceptional parameter semantics.
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 ('Query historical data and export') and resource ('to Parquet format'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_historical_data' or 'convert_dbn_to_parquet' which might have overlapping functionality.
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 sibling tools like 'get_historical_data' (which likely retrieves data without export) and 'convert_dbn_to_parquet' (which converts existing files), there's clear potential for confusion about when this specific export tool is 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?
With no annotations provided, the description carries full burden but only states it retrieves 'download information' without detailing what that includes (e.g., file URLs, formats, sizes), authentication needs, rate limits, or error conditions. This leaves behavioral traits largely undisclosed for a tool that likely involves data access.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool that likely returns structured download data. It lacks details on return values (e.g., file lists, metadata), error handling, or prerequisites beyond 'completed', leaving significant gaps in context for effective agent 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 100%, with the single parameter 'job_id' documented in the schema. The description adds no additional parameter semantics beyond implying it's for a 'completed batch job', which aligns with but doesn't extend the schema's information. Baseline 3 is appropriate as the schema handles the heavy lifting.
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 ('Get download information') and target resource ('for a completed batch job'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'download_batch_files' or 'list_batch_jobs', 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 minimal guidance by specifying 'completed batch job', which implies a prerequisite state. However, it offers no explicit when-to-use advice, alternatives (e.g., vs. 'download_batch_files'), or exclusions, leaving usage context vague.
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 'Subscribe' (implying a streaming/ongoing operation) and 'limited duration', but fails to describe critical behaviors: whether this is read-only or has side effects, authentication requirements, rate limits, error handling, or what happens when the stream ends. For a real-time data tool, this leaves significant 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 a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for the tool's complexity and front-loads the core purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a real-time data subscription tool with no annotations and no output schema, the description is insufficient. It doesn't explain what format the data arrives in, whether it's a stream or batch return, how errors are handled, or what permissions are required. The combination of missing behavioral context and no output information creates significant gaps for an agent trying to use this tool effectively.
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 100%, so the schema already documents all parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters, provide examples beyond what's implied, or clarify edge cases. Baseline 3 is appropriate when schema does the heavy lifting.
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 ('Subscribe to real-time market data') and resource ('market data'), making the purpose understandable. However, it doesn't specifically differentiate from sibling tools like 'get_historical_data' beyond the 'real-time' qualifier, which is implied but not explicit.
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 mentions 'for a limited duration' which provides some context about temporal constraints, but it doesn't explicitly state when to use this tool versus alternatives like 'get_historical_data' or 'get_dataset_range'. No guidance on prerequisites, error conditions, or specific use cases 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool identifies a trading session but doesn't explain what a 'trading session' entails (e.g., market hours, session types), how it's determined, or any constraints like rate limits or authentication needs. This leaves significant gaps for a tool that likely involves time-based logic.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded and efficiently communicates the core functionality, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'identify' means in terms of return values (e.g., session name, status, time ranges) or behavioral details like error handling. For a tool that likely outputs session-specific data, this leaves the agent with insufficient context to use it effectively.
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 input schema has 100% description coverage, with the 'timestamp' parameter well-documented as an optional ISO 8601 timestamp defaulting to current time. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline for high schema coverage without compensating value.
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: 'Identify the current trading session based on time'. It specifies the verb ('identify') and resource ('trading session'), making it understandable. However, it doesn't explicitly differentiate from siblings like 'get_historical_data' or 'get_live_data', which might also relate to time-based data retrieval, 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or compare it to sibling tools such as 'get_historical_data' or 'get_live_data', leaving the agent with no usage context beyond the basic purpose.
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 what the tool returns (metadata including symbology mappings and instrument definitions) but doesn't describe important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or response format. For a tool with 4 parameters and no 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 a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a metadata retrieval tool. However, it could be slightly more front-loaded by explicitly mentioning it's for retrieving symbol metadata rather than just 'Get metadata for symbols' which could be ambiguous.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what format the metadata returns in, whether there are pagination considerations, error handling, or performance characteristics. The description alone is insufficient for an agent to understand the full context of using this tool effectively, especially given the lack of structured behavioral information elsewhere.
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 100%, so all parameters are documented in the schema. The description adds no additional parameter information beyond what's already in the schema. It doesn't explain relationships between parameters or provide examples of valid symbol formats. With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding but doesn't need to compensate for schema 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 verb 'Get' and the resource 'metadata for symbols', specifying it includes 'symbology mappings and instrument definitions'. This distinguishes it from siblings like 'get_historical_data' or 'get_live_data' which focus on data retrieval rather than metadata. However, it doesn't explicitly differentiate from 'resolve_symbols' or 'search_instruments' which might have overlapping functionality.
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 'resolve_symbols' or 'search_instruments'. It doesn't mention prerequisites, use cases, or exclusions. The agent must infer usage from the tool name and parameters 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?
With no annotations provided, the description carries the full burden but only states the action without behavioral details. It does not disclose aspects like read-only nature, potential rate limits, authentication needs, or what the output looks like (e.g., list format, pagination).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is front-loaded and appropriately sized for a simple list operation, earning its place without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It does not explain what the tool returns (e.g., field names, types, or metadata) or address complexity like error handling, leaving gaps for a tool with two parameters and potential behavioral nuances.
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 100%, so the input schema fully documents both parameters ('schema' and 'encoding'). The description adds no additional meaning beyond implying the 'schema' parameter is required, which is already clear from the schema's required field.
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 ('fields available for a specific schema'), making the purpose unambiguous. However, it does not differentiate from sibling tools like 'list_schemas' or 'list_datasets' by specifying what type of fields or their 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?
The description provides no guidance on when to use this tool versus alternatives. It lacks context such as prerequisites (e.g., after listing schemas) or comparisons to siblings like 'list_schemas' for schema-level information.
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 the basic action without disclosing behavioral traits such as pagination, rate limits, authentication needs, or what 'details' include. This is inadequate for a tool with potential complexity in listing operations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste, front-loading the core purpose. It's appropriately sized for a simple listing tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a simple but potentially incomplete context (e.g., what 'details' entail), the description is insufficient. It doesn't compensate for the lack of structured data, leaving gaps in understanding the tool's full behavior and output.
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 100%, so the schema already documents the optional 'dataset' parameter. The description adds no additional meaning beyond implying filtering by dataset, matching the baseline for high schema coverage without extra param 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 verb 'List' and the resource 'data publishers with their details', providing a specific action and target. However, it doesn't differentiate from sibling tools like 'list_datasets', 'list_fields', or 'list_schemas' that also list different resources, 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. It doesn't mention prerequisites, context for filtering, or compare to other listing tools like 'list_datasets', 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 the tool 'returns the records as structured data', which hints at output behavior, but fails to address critical aspects like error handling (e.g., for invalid file paths), performance implications (e.g., for large files), or side effects (e.g., memory usage). For a read operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality ('Read and parse a DBN file') and adds value with the outcome ('returning the records as structured data'). There is no wasted verbiage, repetition, or unnecessary details, making it highly concise and well-structured for its purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations and no output schema, the description is incomplete for a data-reading tool with 3 parameters. It lacks details on output format (what 'structured data' means), error conditions, or performance considerations. While it states the basic purpose, it doesn't provide enough context for safe and effective use, especially compared to siblings like 'read_parquet_file' which might have different behaviors.
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 input schema has 100% description coverage, with clear documentation for 'file_path', 'limit', and 'offset'. The description adds no parameter-specific semantics beyond what's in the schema, such as explaining DBN file structure or record format. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 parse') and resource ('a DBN file'), specifying what the tool does. It distinguishes from siblings like 'convert_dbn_to_parquet' or 'write_dbn_file' by focusing on reading/parsing rather than conversion or writing. However, it doesn't explicitly differentiate from 'read_parquet_file' beyond file format, which is why 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?
The description provides no guidance on when to use this tool versus alternatives like 'read_parquet_file' for Parquet files or 'get_historical_data' for other data access methods. It lacks context about prerequisites (e.g., file availability) or exclusions, offering only a basic functional statement 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 reads and returns data, but doesn't cover critical aspects like error handling (e.g., what happens if the file doesn't exist), performance implications (e.g., memory usage for large files), or output format details. This leaves significant gaps for a data-reading tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action and outcome, making it easy to understand at a glance. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a data-reading tool with 3 parameters. It doesn't explain what 'return the data' means in practice (e.g., format, structure, or limitations), nor does it address behavioral aspects like error handling. This is inadequate for guiding an agent effectively.
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 100%, so the input schema fully documents all parameters (file_path, limit, columns). The description adds no additional meaning beyond what's in the schema—it doesn't explain parameter interactions or provide examples. Baseline 3 is appropriate as the schema does the heavy lifting.
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 resource ('a Parquet file'), and specifies the outcome ('return the data'). It's specific about what the tool does, though it doesn't explicitly differentiate from sibling tools like 'read_dbn_file' or 'export_to_parquet' beyond the file format.
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 this over 'read_dbn_file' for similar data reading tasks, or any prerequisites like file accessibility. Usage is implied by the name and purpose, but no explicit context or exclusions are given.
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 for behavioral disclosure. It mentions 'resolve' but doesn't clarify if this is a read-only lookup, a transformation with side effects, or requires specific permissions. It lacks details on rate limits, error handling, or what 'resolution' entails operationally. For a tool with 6 parameters and no annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without fluff. It's appropriately sized for the tool's complexity and front-loaded with the essential action. Every word earns its place, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what 'resolution' means in practice, what the output looks like, or any behavioral constraints. For a symbol transformation tool with multiple inputs, this leaves significant gaps for an agent to understand how to use it effectively.
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 100%, so the schema already documents all parameters thoroughly. The description doesn't add any meaning beyond what's in the schema—it doesn't explain parameter interactions, provide examples of symbol resolution, or clarify the relationship between 'stype_in' and 'stype_out'. Baseline 3 is appropriate when the schema does the heavy lifting.
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 ('resolve') and resource ('symbols'), specifying it's about converting between different symbology types. It distinguishes from siblings like 'get_symbol_metadata' or 'search_instruments' by focusing on transformation rather than retrieval or search. However, it doesn't explicitly differentiate from all siblings, leaving some ambiguity about when to use this versus similar tools.
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 resolution, or compare it to siblings like 'get_symbol_metadata' or 'search_instruments'. There's no explicit 'when' or 'when not' usage advice, leaving the agent to infer based on the 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'submit a batch job' which implies an asynchronous, potentially long-running operation, but doesn't clarify whether this requires special permissions, what happens after submission (e.g., job status tracking via 'list_batch_jobs'), rate limits, or how results are accessed (likely via 'download_batch_files'). The description is insufficient for a mutation 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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded with the core purpose, making it easy to scan and understand immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that submits batch jobs (a mutation operation) with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after submission, how to track job progress, where results are stored, or how to retrieve them. Given the sibling tools include 'list_batch_jobs', 'get_batch_job_files', and 'download_batch_files', the description should at least hint at this workflow.
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 100%, so the schema already documents all 8 parameters thoroughly with descriptions, enums, and defaults. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.
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 ('submit') and resource ('batch data download job for large historical datasets'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'download_batch_files' or 'get_historical_data', which appear to be related but distinct 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 like 'download_batch_files' (which might retrieve already-generated files) or 'get_historical_data' (which might be for smaller, immediate queries). There's no mention of prerequisites, typical use cases, or when this batch approach is preferable to other methods.
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 but offers minimal information. It doesn't mention whether this is a read-only or write operation (though 'write' implies mutation), what permissions are required, whether it's idempotent, or how errors are handled. For a tool that writes files, this is a significant gap in behavioral 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 a single, efficient sentence that communicates the core functionality without unnecessary words. It's appropriately sized for the tool's complexity and gets straight to the point with zero wasted verbiage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 7 parameters that writes files (implying mutation and side effects), the description is inadequate. With no annotations, no output schema, and minimal behavioral context, the agent lacks crucial information about what this tool actually does, what it returns, and how it behaves in different scenarios.
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 doesn't add any parameter-specific information beyond what's already documented in the input schema (which has 100% coverage). It doesn't explain relationships between parameters, provide examples of valid combinations, or clarify edge cases. With complete schema coverage, the baseline of 3 is appropriate.
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 ('write') and resource ('historical data query results to a DBN file'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'export_to_parquet' or 'convert_dbn_to_parquet', which would require more specific comparison.
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 'export_to_parquet' or 'get_historical_data'. There's no mention of prerequisites, performance considerations, or typical use cases, leaving the agent with insufficient context 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. It states the action but lacks details on permissions, rate limits, error handling, or what the output looks like (e.g., date format, null handling). 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, making it easy for an agent to parse 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 the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It explains what the tool does but lacks behavioral and output details, which are necessary for full contextual understanding despite the simple schema.
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 input schema has 100% description coverage, with the parameter 'dataset' clearly documented as 'Dataset name'. The description adds no additional semantic context beyond this, such as examples or constraints, so it meets the baseline for high schema coverage without compensating value.
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 ('Get') and resource ('available date range for a dataset'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_historical_data' or 'list_datasets', which might also involve date-related operations, 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. For example, it doesn't specify if this should be used before fetching data with 'get_historical_data' or as a standalone check, 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'List all batch jobs' implies a read-only operation, it doesn't specify whether this requires authentication, what permissions are needed, how results are returned (e.g., pagination, ordering), rate limits, or error conditions. For a tool with no annotation coverage, this leaves significant behavioral aspects undocumented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that clearly communicates the tool's purpose without unnecessary words. It's appropriately sized for a listing tool and front-loads the essential information. Every word earns its place, making it easy for an agent to quickly understand what the tool does.
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 (listing with filtering), no annotations, and no output schema, the description is minimally adequate but has clear gaps. It explains what the tool does but doesn't cover behavioral aspects, usage context, or return format. For a tool with three parameters and no structured output documentation, more context would be helpful, but the description meets basic requirements.
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 input schema has 100% description coverage, providing clear documentation for all three parameters (states, since, limit) including defaults and formats. The description adds no parameter-specific information beyond what's in the schema, so it doesn't enhance parameter understanding. However, with complete schema coverage, the baseline score of 3 is appropriate as the schema adequately documents 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 action ('List') and resource ('batch jobs') with additional context about what information is included ('with their current status'). It distinguishes itself from siblings like 'get_batch_job_files' or 'submit_batch_job' by focusing on listing jobs rather than retrieving files or creating jobs. However, it doesn't explicitly differentiate from all siblings like 'analyze_data_quality' or 'health_check' which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, when this tool is appropriate versus other batch-related tools like 'cancel_batch_job' or 'get_batch_job_files', or any specific use cases. The agent must infer usage from the tool name and description 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 mentions the return content (list of schemas with descriptions) but lacks details on format, pagination, rate limits, authentication needs, or error handling, which are critical for a list operation.
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 a clear returns section and an example. It's efficient with minimal waste, though the example could be integrated more seamlessly for a perfect score.
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 (0 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers what is returned but misses behavioral aspects like response format or limitations, leaving gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description correctly avoids discussing parameters, earning a baseline score of 4 for not adding unnecessary 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 ('List') and resource ('all available data schemas from Databento'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_datasets' or 'list_fields', 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 'list_datasets' or 'list_fields'. It includes an example call but no context about prerequisites, timing, or exclusions, leaving usage unclear.
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 only states the action ('List all available datasets') without detailing traits like whether it's read-only, paginated, rate-limited, or what the output format might be. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy for an agent to parse 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 the tool's simplicity (0 parameters, no output schema), the description is minimally complete. However, it lacks behavioral context that would be helpful for an agent, such as output details or usage scenarios. With no annotations and no output schema, the description should do more to compensate, but it's adequate for a basic list 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?
The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details, but since there are no parameters, this is acceptable. A baseline of 4 is appropriate as the description doesn't need to compensate for missing param 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 verb ('List') and resource ('all available datasets from Databento'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'list_batch_jobs', 'list_fields', 'list_publishers', etc., which follow the same 'list' pattern for different resources, so it misses 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, such as whether it's for initial discovery or if other tools like 'get_dataset_range' might be more specific. This leaves the agent with minimal 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 the full burden. It discloses that the tool returns 'List of unit prices showing cost per GB or per record,' which adds some behavioral context. However, it doesn't cover critical aspects like whether this is a read-only operation, rate limits, authentication needs, or error handling. For a tool with zero annotation coverage, 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 (purpose, parameters, returns, example) and front-loaded key information. It's concise at four sentences, but the 'Parameters' section repeats schema details unnecessarily, 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 low complexity (1 optional parameter, no output schema, no annotations), the description is moderately complete. It covers purpose, parameters, and returns, but lacks behavioral details like safety or performance. Without annotations or output schema, it should do more to compensate, making it adequate but not fully comprehensive.
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 100%, so the schema already documents the optional 'dataset' parameter. The description repeats this information in the 'Parameters' section without adding meaning beyond what the schema provides (e.g., no examples of valid datasets beyond 'GLBX.MDP3'). Baseline 3 is appropriate when the schema does the heavy lifting.
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: 'Get current pricing information per dataset/schema combination.' It specifies the verb ('Get') and resource ('pricing information'), and distinguishes it from siblings like 'get_cost' by focusing on unit prices per dataset/schema rather than overall costs. However, it doesn't explicitly differentiate from all 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context with 'Helps understand pricing before querying,' suggesting it's for pre-query planning. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_cost' or 'list_datasets,' and doesn't specify exclusions or prerequisites. This makes it adequate but with gaps.
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 of behavioral disclosure. It adds value by detailing what information is returned (e.g., server uptime, cache statistics, API usage metrics), which helps the agent understand the tool's output. However, it lacks critical behavioral details such as whether this is a read-only operation, potential rate limits, authentication requirements, or error conditions, leaving gaps 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement, a bulleted list of returns, and an example. It's appropriately sized and front-loaded, with the key information presented first. Every sentence earns its place, though the example could be slightly more informative (e.g., noting it takes no arguments).
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 (retrieving multiple status metrics) and the absence of both annotations and an output schema, the description is moderately complete. It details what information is returned, which is helpful, but lacks specifics on output format, error handling, or behavioral constraints. This leaves the agent with incomplete context for reliable tool 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 input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description compensates by implying no parameters are needed through the example 'get_account_status()' and by not mentioning any inputs. This effectively adds semantic clarity beyond the schema, though it could be more explicit about the lack of 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's purpose as retrieving 'comprehensive server status and account information' with specific components listed. It uses the verb 'Get' with the resource 'server status and account information', making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'health_check' or 'get_metrics', which likely serve related but distinct 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 mentions using it 'for a full server overview' but doesn't clarify how this differs from other status-related tools in the sibling list, such as 'health_check' or 'get_metrics'. There are no explicit when-to-use or when-not-to-use instructions, leaving the agent to infer 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 mentions the tool 'Get only the metadata' which implies a read-only operation, but fails to detail aspects like error handling (e.g., invalid file paths), performance characteristics, or what metadata is included (e.g., file size, schema info). This leaves significant gaps for a tool with potential complexity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get only the metadata from a DBN file') and adds a key constraint ('without reading all records'). There is no wasted wording, making it highly concise and well-structured for quick 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?
For a tool with one parameter and no output schema, the description is minimally adequate. It covers the basic purpose and scope but lacks details on return values (e.g., metadata structure), error cases, or integration with siblings like 'read_dbn_file'. Given the simplicity, it's complete enough to be functional but could be more informative.
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 input schema has 100% description coverage, with 'file_path' clearly documented. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Given the high schema coverage, a baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
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 ('Get') and resource ('metadata from a DBN file'), with the specific scope 'without reading all records' that distinguishes it from the sibling 'read_dbn_file'. However, it doesn't explicitly contrast with other metadata-related tools like 'get_symbol_metadata' or 'list_schemas', keeping it from 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying 'without reading all records', suggesting this tool is for lightweight metadata retrieval compared to full file reading. However, it lacks explicit guidance on when to use this over alternatives like 'get_symbol_metadata' or prerequisites such as file accessibility, leaving room for improvement.
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 implies a read-only operation ('Get') and describes return values, but lacks details on permissions, rate limits, or side effects (e.g., the reset parameter's impact). The example hints at reset behavior, but this is covered in the schema description.
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 appropriately sized. It front-loads the core purpose, uses bullet points for returns efficiently, and includes a concise example. Every sentence adds value without redundancy, making it easy to scan and understand.
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 and no output schema, the description partially compensates by listing return values, but it lacks details on behavioral traits (e.g., side effects, permissions). For a tool with one parameter and moderate complexity, it's adequate but has clear gaps in usage 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?
The schema description coverage is 100%, with the reset parameter fully documented in the schema. The description adds no additional parameter semantics beyond what's in the schema (e.g., no extra context on reset implications). The example references reset but doesn't enhance understanding. Baseline 3 is appropriate given high 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 tool's purpose with specific verbs ('Get server performance metrics and usage statistics'), identifying the resource (server metrics/statistics). It distinguishes itself from most siblings (e.g., get_account_status, health_check) by focusing on performance data, though it doesn't explicitly differentiate from health_check, which might overlap in monitoring functions.
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 get_metrics over health_check (a sibling tool) or other monitoring-related tools, nor does it specify prerequisites or exclusions. The example shows usage but doesn't contextualize it.
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 describes what the tool detects and returns (e.g., quality score, issues list), which adds useful context beyond basic functionality. However, it lacks details on performance aspects like rate limits, error handling, or computational intensity, which are important for a data analysis 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 bullet points and an example, making it easy to scan. It is appropriately sized, with each sentence adding value, such as listing detections and returns. However, the example could be more concise, and some information might be slightly redundant given the schema coverage.
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 a data quality analysis tool with no annotations and no output schema, the description provides a good overview of what it does and returns. However, it lacks details on output structure (e.g., format of the 'list of issues'), error cases, or dependencies, which would enhance completeness for an agent's understanding.
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 100%, so the input schema already documents all parameters thoroughly. The description adds minimal parameter semantics beyond the schema, only implying in the example that parameters like 'dataset', 'symbols', 'start', and 'end' are used for analysis. No additional syntax or format details are provided, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('analyze', 'detect') and resources ('data quality', 'market data'), and distinguishes it from siblings by focusing on quality analysis rather than data retrieval or job management. It explicitly lists what it detects: time gaps, price outliers, null values, and duplicate records.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage through an example that references a sibling tool ('First retrieve data with get_historical_data'), implying this tool should be used after data retrieval. However, it lacks explicit guidance on when not to use it or alternatives, such as comparing it to 'quick_analysis' or other analysis tools.
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 estimates costs, implying it's a read-only, non-destructive operation, but lacks details on rate limits, authentication needs, or what the estimation output includes (e.g., currency, granularity). This leaves 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 a single, front-loaded sentence that efficiently conveys the tool's purpose without unnecessary words. Every part of the sentence earns its place by specifying the action ('estimate'), target ('cost'), and context ('before executing it').
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 and no output schema, the description is minimally complete for a read-only estimation tool. It clarifies the tool's role but lacks details on behavioral aspects (e.g., response format, error handling) and output expectations, which are important for a cost estimation function in a data query context.
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 100%, so the schema fully documents all 5 parameters. The description does not add any parameter-specific information beyond what the schema provides (e.g., it doesn't explain relationships between parameters like 'dataset' and 'schema'). Baseline 3 is appropriate when the schema handles parameter documentation.
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 a specific verb ('estimate') and resource ('cost of a historical data query'), distinguishing it from siblings like 'get_historical_data' (which executes queries) and 'list_unit_prices' (which lists pricing). It precisely indicates this is a pre-execution estimation tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('before executing it'), implying this tool should be used to check costs prior to running actual queries. However, it does not explicitly state when not to use it or name specific alternatives (e.g., 'get_historical_data' for execution), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context beyond the input schema: it mentions caching behavior ('bypass cache'), preview capabilities ('preview query without executing'), and practical advice ('start with small date ranges'). However, it doesn't cover aspects like rate limits, error handling, or authentication needs, which are relevant for a data retrieval tool.
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 examples and tips that earn their place by illustrating usage and providing practical advice. Each sentence adds value, and there is no redundant information, making it efficient and easy to scan.
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 complexity of 8 parameters, 100% schema coverage, and no output schema, the description is mostly complete. It covers purpose, examples, and behavioral tips, but lacks details on return values (e.g., data format, pagination) and error scenarios, which would be helpful for an agent invoking this 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?
The input schema has 100% description coverage, providing detailed parameter information. The description adds minimal semantic value beyond the schema, as it primarily restates parameter usage in examples (e.g., 'dataset="GLBX.MDP3"') without explaining nuances. The baseline score of 3 is appropriate since the schema does the heavy lifting.
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 a specific verb ('Retrieve') and resource ('historical market data for symbols from Databento'), and it distinguishes itself from siblings like 'get_live_data' by specifying historical data. The examples reinforce this purpose with concrete use cases.
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 examples (e.g., for ES futures trades, AAPL OHLCV bars) and tips (e.g., start with small date ranges), but it lacks explicit guidance on when to use this tool versus alternatives like 'get_live_data' or 'get_dataset_range'. No clear exclusions or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses what the tool returns (5 specific components) which is valuable behavioral context. However, it doesn't mention performance characteristics, rate limits, authentication needs, or potential side effects. The description adds meaningful value but doesn't fully compensate for the lack of annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured: a clear purpose statement, bulleted combination list, concrete examples, and bulleted return values. Every sentence earns its place with zero waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity.
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 output schema, no annotations), the description does well by explaining what the tool returns in detail. However, it could better address when to use this versus individual component tools. The lack of output schema is partially compensated by the return value description.
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 100%, so the schema already documents all parameters thoroughly. The description provides examples showing parameter usage but doesn't add semantic meaning beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting.
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 performs a 'comprehensive analysis of a symbol' and specifies exactly what it combines: metadata, cost estimate, sample data, trading session info, and data quality check. It distinguishes itself from siblings like get_cost or get_symbol_metadata by bundling multiple functions into one call.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (for a quick, all-in-one analysis) but doesn't explicitly state when NOT to use it or name specific alternatives. It suggests this is for comprehensive analysis rather than individual components, but lacks explicit comparison to siblings like get_cost or get_historical_data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: it's a download operation (implies data transfer), specifies that files are saved locally, mentions overwrite behavior, and describes return values. It doesn't cover aspects like error handling, rate limits, or authentication needs, but provides solid 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 well-structured and front-loaded with the core purpose in the first sentence. Subsequent sections for parameters, returns, and example are clearly labeled and concise, with no wasted words. Every sentence serves a specific informative role.
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 (3 parameters, no output schema, no annotations), the description is fairly complete. It covers purpose, parameters, returns, and includes an example. However, it lacks details on prerequisites (e.g., job must be completed), error conditions, or file formats, which could be useful for full contextual 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?
Schema description coverage is 100%, so the baseline is 3. The description adds value by organizing parameters in a clear list with required/optional labels and default values, and provides a concrete example that illustrates usage. This enhances understanding beyond the schema's basic descriptions, though it doesn't introduce new semantic 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 verb ('Download') and resource ('completed batch job files'), and distinguishes it from siblings like 'get_batch_job_files' (which likely lists files) and 'submit_batch_job' (which initiates jobs). The phrase 'to a local directory' adds useful context about the destination.
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 mentioning 'completed batch job files', suggesting this tool should be used after a batch job finishes. However, it doesn't explicitly state when to use it versus alternatives like 'get_batch_job_files' (which might retrieve metadata) or provide clear exclusions. The context is implied but not fully explicit.
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 indicates this is a diagnostic/read-only operation for checking connectivity, which is helpful, but doesn't mention potential side effects, authentication requirements, rate limits, or what constitutes a 'healthy' response. The description adds basic context but lacks comprehensive behavioral details.
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 in two sentences: the first states the core purpose, the second provides usage guidance. Every word earns its place with no redundancy or unnecessary elaboration, 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple diagnostic tool with one optional parameter and no output schema, the description provides adequate context about purpose and usage. However, without annotations or output schema, it could benefit from more detail about what the health check actually returns (e.g., status codes, metrics) or any prerequisites. The description is reasonably complete but has minor 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?
With 100% schema description coverage for the single parameter, the schema already documents the 'verbose' parameter thoroughly. The description doesn't add any parameter-specific information beyond what the schema provides, but with only one parameter and high schema coverage, the baseline is appropriate. No additional parameter semantics are needed.
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 ('Check the health and connectivity') and target resource ('Databento API'), distinguishing it from sibling tools focused on data operations, batch jobs, or account management. It provides a concrete purpose beyond just restating the tool name.
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 explicitly states when to use this tool ('to diagnose connection issues or verify the server is working properly'), providing clear context for its application. It distinguishes itself from siblings by focusing on system health rather than data processing or account operations.
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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