Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions analyzing failures but doesn't disclose behavioral traits such as whether this is a read-only operation, if it requires specific permissions, what happens if no test run exists, or how results are structured. The description is minimal and lacks critical 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a brief parameter explanation. There's minimal waste, though it could be slightly more structured (e.g., separating usage notes).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (analyzing test runs), no annotations, and an output schema (which reduces need to explain return values), the description is partially complete. It covers the basic purpose and parameter but lacks context on when to use it, behavioral details, and differentiation from siblings, making it adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds meaning for the single parameter 'summary_only' by explaining it controls whether to 'return only a summary of the test results,' which clarifies its purpose beyond the schema's title. However, it doesn't cover other potential aspects like default behavior or interactions, leaving some gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Analyze the most recent test run and provide detailed information about failures.' It specifies the verb ('analyze'), resource ('most recent test run'), and output focus ('detailed information about failures'). However, it doesn't explicitly differentiate from siblings like 'run_tests_*' tools, which might perform tests rather than analyze results.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., whether a test run must exist), exclusions, or comparisons to sibling tools like 'create_coverage_report' or 'run_tests_*'. Usage is implied but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool 'runs a script' and 'generates reports,' implying a potentially resource-intensive or time-consuming operation, but doesn't disclose critical traits like execution time, permissions needed, side effects (e.g., file creation), error handling, or rate limits. 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and concise, with a clear purpose statement followed by separate 'Args' and 'Returns' sections. Every sentence adds value: the first explains the action, and the subsequent sections detail inputs and outputs. It's front-loaded with the main functionality, making it efficient to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (involves running scripts and generating files), no annotations, and an output schema (which handles return values), the description is partially complete. It covers the basic purpose and parameters but lacks behavioral details (e.g., execution context, file paths, errors). With an output schema, it doesn't need to explain return values, but other gaps remain, making it adequate but not fully informative.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description includes an 'Args' section that explains the 'force_rebuild' parameter, adding meaning beyond the input schema (which has 0% description coverage). However, with only 1 parameter and the schema providing basic type info, the description compensates adequately but not richly—it doesn't detail default behavior or implications of the parameter. Given the low schema coverage, this is a minimal improvement.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Run the coverage report script and generate HTML and XML reports.' It specifies the action (run script, generate reports) and outputs (HTML and XML reports), making it easy to understand what the tool does. However, it doesn't differentiate from sibling tools like 'analyze_tests' or 'run_tests_*', which might also relate to testing/reporting, so it doesn't fully distinguish from alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context (e.g., after running tests), or comparisons to sibling tools like 'analyze_tests' or 'run_tests_*', leaving the agent to guess based on tool names alone. This lack of explicit when/when-not instructions reduces its helpfulness.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 clearly describes what the tool returns (sys.path and sys.executable), which is helpful. However, it doesn't mention potential side effects, performance characteristics, authentication requirements, or error conditions. The description provides basic behavioral information but lacks depth 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that immediately states the tool's purpose without any unnecessary words. It's front-loaded with the core functionality and contains zero redundant information. Every word earns its place in this compact description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that the tool has zero parameters, 100% schema coverage, and an output schema exists, the description provides adequate context. It clearly states what information will be returned (sys.path and sys.executable). The existence of an output schema means the description doesn't need to detail return values. However, with no annotations, it could benefit from mentioning that this is a read-only, non-destructive operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, and schema description coverage is 100% (though there are no parameters to cover). The description appropriately doesn't discuss parameters since none exist. It focuses instead on what the tool returns, which is the correct emphasis for a parameterless tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Returns') and the exact resources being returned ('sys.path and sys.executable from the running MCP server'). It precisely distinguishes this tool from sibling tools that perform testing, logging, or coverage operations by focusing exclusively on server environment details.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context (when you need to inspect the server's Python environment) but doesn't explicitly state when to use this tool versus alternatives. No exclusions or specific prerequisites are mentioned, leaving the agent to infer appropriate usage scenarios from the tool's purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It clearly indicates a read-only check operation ('Check if... is alive') which implies non-destructive behavior, but doesn't specify what 'alive' means (e.g., network reachability, service responsiveness), response format, or timeout behavior. It meets minimum requirements but lacks operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, perfectly focused sentence with zero wasted words. It immediately communicates the core function without any unnecessary elaboration, making it optimally concise for such a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with an output schema (which handles return value documentation), the description provides adequate context about what the tool does. The main gap is lack of differentiation from sibling tools that might serve similar diagnostic purposes, but overall it's reasonably complete for this complexity level.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0 parameters and 100% schema description coverage, the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema already fully documents the empty input structure.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Check') and target ('MCP server'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_server_env_details' which might also provide server status information, preventing a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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_server_env_details' that might offer more detailed server information, there's no indication whether 'ping' should be used for simple liveness checks or as a preliminary diagnostic step.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the output verbosity level, which is useful, but lacks details on what 'run all tests' entails (e.g., test types, execution environment, success/failure handling, or any side effects like timeouts or resource usage). This leaves gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the key information ('Run all tests') and adds necessary detail ('with minimal output (verbosity level 0)'). There is no wasted text, 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.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 0 parameters, an output schema, and no annotations, the description is adequate but minimal. It covers the purpose and verbosity level, which is sufficient for basic use, but lacks details on test scope, execution behavior, or output format, which could be important for an agent to use it correctly in complex scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by explaining the verbosity level, which is not captured in the schema, making it helpful for understanding the tool's configuration despite the lack of parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Run all tests') and specifies the execution mode ('with minimal output (verbosity level 0)'). It distinguishes from siblings like 'run_tests_verbose' and 'run_tests_very_verbose' by indicating the verbosity level, though it doesn't explicitly mention what 'tests' refers to or the scope beyond 'all'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by specifying 'minimal output (verbosity level 0)', which suggests this tool is for quick, concise test runs compared to verbose alternatives. However, it doesn't explicitly state when to use this versus other test-running tools or provide exclusions, such as not using it for debugging detailed failures.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 verbosity level but fails to describe what 'verbose output' entails, whether it affects performance, if it's read-only or destructive, or any other behavioral traits like error handling or output format. This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words or fluff. It is front-loaded and wastes no space, 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.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description's job is simplified. It covers the basic action and verbosity but lacks details on behavioral aspects like what 'verbose' means or how it differs from siblings. This makes it minimally viable but with clear gaps in context, especially with no annotations to supplement.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately adds no parameter details, focusing solely on the tool's action and verbosity level, which aligns with the schema's completeness. A baseline of 4 is applied as it compensates adequately for the lack of parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Run all tests') and specifies the verbosity level ('verbose output (verbosity level 1)'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'run_tests_no_verbosity' or 'run_tests_very_verbose' beyond the verbosity level, 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.

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, such as 'run_tests_no_verbosity' or 'run_tests_very_verbose', nor does it mention any prerequisites or context for usage. This lack of comparative or contextual information limits its helpfulness for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions the output verbosity level but does not disclose other behavioral traits such as execution time, side effects, error handling, or what 'run all tests' entails (e.g., test types, scope). This leaves significant gaps for a tool with potential operational impacts.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action ('Run all tests') and adds necessary detail ('with very verbose output (verbosity level 2)'). There is no wasted text, 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.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters and an output schema exists, the description's focus on verbosity is adequate. However, with no annotations and potential complexity in test execution, it could benefit from more context on what 'tests' include or behavioral expectations, leaving some gaps in completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's behavior, which aligns with the schema's completeness.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Run all tests') and specifies the output characteristic ('with very verbose output (verbosity level 2)'). It distinguishes from siblings like 'run_tests_no_verbosity' and 'run_tests_verbose' by indicating a specific verbosity level, though it doesn't explicitly contrast with all alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context through 'very verbose output,' suggesting it's for detailed debugging or logging scenarios. However, it lacks explicit guidance on when to choose this over alternatives like 'run_tests_verbose' or 'run_tests_no_verbosity,' and does not mention prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It explains what the tool does (runs tests matching agent patterns) and mentions performance characteristics (reduced execution time), but doesn't disclose other behavioral traits like whether it's read-only vs. destructive, authentication requirements, rate limits, error handling, or what happens if tests fail. The description adds some context but leaves gaps in behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the core purpose. Every sentence adds value: first states what it does, second explains scope and benefit, third gives usage guidance, and the Args/Returns sections efficiently document parameters and output. There's no wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 2 parameters with 0% schema coverage, no annotations, but an output schema exists, the description does a good job explaining parameters and purpose. However, for a test execution tool, it could better address what happens during test failures, timeouts, or other edge cases. The existence of an output schema means it doesn't need to explain return values in detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It provides clear semantics for both parameters: 'agent' is explained as 'The agent to run tests for' with examples, and 'verbosity' is defined with specific levels and default. This adds substantial value beyond the bare schema, though it doesn't explain parameter constraints or validation rules.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('run tests for a specific agent only') and resource ('agent's patterns including both main agent tests and healthcheck tests'). It explicitly distinguishes this tool from running all tests, which helps differentiate it from potential sibling tools like 'run_tests_no_verbosity' that might run broader test suites.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('when you need to focus on testing a specific agent component') and mentions the benefit ('significantly reducing test execution time compared to running all tests'). However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools (like 'run_tests_verbose' or 'analyze_tests').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 'with context' which hints at the context_before/after parameters, but doesn't explain what 'context' means, how results are returned, whether there are rate limits, authentication needs, or what 'search' entails (e.g., regex, exact match). This is a significant gap for a search tool with 5 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose. However, it could be more structured by separating key features (e.g., 'Searches all log records. Optional filters: scope, content patterns. Includes context lines.'), but it avoids unnecessary verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters with 0% schema coverage, no annotations, but an output schema exists, the description is incomplete. It hints at some parameters but doesn't fully explain their semantics or usage. The output schema mitigates the need to describe return values, but the lack of behavioral and parameter details leaves gaps for effective tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It mentions filtering by 'scope and content patterns' and 'with context', which loosely maps to some parameters (scope, log_content_patterns_override, context_before/after), but doesn't explain what 'scope' means, what 'log_dirs_override' does, or how patterns work. This adds minimal value beyond the schema's property names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('search') and resource ('all log records'), and mentions optional filtering by scope and content patterns. It distinguishes from some siblings like 'search_log_first_n_records' by specifying 'all' records, but doesn't fully differentiate from other search siblings like 'search_log_time_based' in terms of when to use each.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'search_log_first_n_records', 'search_log_last_n_records', or 'search_log_time_based'. It mentions optional filtering but doesn't specify use cases or exclusions, leaving the agent to guess based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions 'with context' but doesn't explain what context means, how filtering works, or behavioral aspects like pagination, rate limits, or error handling. This is inadequate for a tool with 6 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose. However, it could be more structured by explicitly mentioning the resource (logs) and separating filtering from context aspects.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters with 0% schema coverage, no annotations, and an output schema (which reduces need for return value explanation), the description is incomplete. It lacks details on parameter meanings, behavioral traits, and usage distinctions from siblings, making it insufficient for effective tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It mentions 'filtering' and 'context' but doesn't explain any of the 6 parameters (e.g., what 'scope' or 'log_dirs_override' mean). The description adds minimal value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches for records with filtering and context, specifying 'first N (oldest)' which distinguishes it from siblings like search_log_last_n_records. However, it doesn't explicitly mention logs as the resource, though this is implied by the name and sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving oldest records with optional filtering and context, but doesn't explicitly state when to use this vs. alternatives like search_log_last_n_records or search_log_time_based. No guidance on prerequisites or exclusions is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 'with context' and 'optionally filtering', but doesn't explain what 'context' means (e.g., surrounding log entries), how filtering works, whether this is a read-only operation, potential performance impacts, or output format. For a search tool with 6 parameters, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—a single sentence that front-loads the core purpose. Every word earns its place, with no redundancy or fluff. It efficiently communicates the tool's essence without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (6 parameters, no annotations, but with an output schema), the description is incomplete. It lacks details on filtering mechanics, context behavior, and how parameters interact. The output schema may cover return values, but the description doesn't provide enough operational context for effective use, especially compared to siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It only vaguely references 'filtering' and 'context', without explaining how parameters like 'scope', 'log_dirs_override', or 'log_content_patterns_override' relate to these concepts. The description adds minimal meaning beyond the schema's parameter names and types, failing to clarify usage or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search for the last N (newest) records, optionally filtering, with context.' It specifies the verb ('search'), resource ('records'), and scope ('last N/newest'), which distinguishes it from siblings like 'search_log_first_n_records' (first N) and 'search_log_time_based' (time-based). However, it doesn't explicitly differentiate from 'search_log_all_records' in terms of filtering capabilities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions 'optionally filtering' but doesn't specify what filtering entails or when to choose this over siblings like 'search_log_first_n_records' or 'search_log_time_based'. There's no mention of prerequisites, exclusions, or typical use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions 'optionally filtering' and 'with context,' it doesn't explain what 'context' means in practice, what format results are returned in, whether this is a read-only operation, or any performance characteristics. For an 8-parameter tool with 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place: 'Search logs' (action), 'within a time window' (primary constraint), 'optionally filtering' (additional capability), 'with context' (extra feature). No wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (8 parameters, no annotations, but has output schema), the description is minimally adequate. The output schema existence means return values don't need explanation in the description. However, for a search tool with multiple sibling alternatives and no behavioral annotations, the description should provide more guidance on usage scenarios and result interpretation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage for all 8 parameters, the description provides minimal help. It mentions 'time window,' 'filtering,' and 'context,' which loosely map to some parameters (minutes/hours/days for time, log_content_patterns_override for filtering, context_before/after for context), but doesn't explain parameter relationships, defaults, or how overrides work. The description doesn't adequately compensate for the complete lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search logs within a time window, optionally filtering, with context.' This specifies the verb (search), resource (logs), and key features (time-based, filtering, context). However, it doesn't explicitly differentiate from siblings like search_log_all_records or search_log_first_n_records beyond the time-based aspect.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With siblings like search_log_all_records and search_log_first_n_records available, there's no indication of when time-based searching is preferable over other log search methods. The description only states what the tool does, not when to choose it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.