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

With no annotations provided, the description carries the burden of behavioral disclosure. It transparently states key behaviors: parsing, grouping by error signature, deduplicating repeated errors, and categorizing into four specific types. It does not mention return format or side effects, but the core behavior is well covered.

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 three sentences with zero fluff. It front-loads the primary purpose, then adds behavioral detail, then a usage directive. Every sentence earns its place and the length is appropriate for the tool's complexity.

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

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

The tool has 7 parameters, no annotations, and no output schema, so the description needs to convey both input handling and expected results. While it explains the grouping/categorization logic, it omits details like the output structure and does not mention the extensibility via customInfraPatterns. The description is adequate but leaves gaps that an agent would need to infer.

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 100%, so the baseline is 3. The description adds no parameter-level detail beyond what the schema already provides, and it does not reference parameters like customInfraPatterns or runId. It neither enhances nor harms the semantic understanding.

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

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

The description uses a specific verb and resource ('Parse a CI test run and group failures by error signature') and clearly distinguishes the tool from siblings by detailing its deduplication and categorization behaviors (assertion, timeout, network, crash). This uniquely positions it as an aggregation/pre-processing tool.

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 a clear usage context with 'Use this as the first step before triage,' implying it precedes other analysis tools. However, it lacks explicit alternatives or when-not-to-use conditions, so it stops short of the top anchor.

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, the description carries the full burden. It describes the scanning, classification, and return fields (rollback_eligible, blocking_migrations, deployment_strategy), implying a read-only operation. It does not mention edge cases or permissions, but the core behavior is transparently disclosed.

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 compact sentence that front-loads the action and includes concrete file patterns, outputs, and usage context. Every phrase adds value with no wasted words.

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

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

With one parameter, no output schema, and no annotations, the description provides a solid picture of what the tool does and returns. It covers purpose, examples, outputs, and usage timing. It could elaborate on classification details or error cases, but the core is sufficiently complete for an agent to invoke it correctly.

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

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

Schema coverage is 100% (repo_path described as 'Absolute path to the repository root to scan for migration files'). The description adds no new semantics beyond the schema, so the baseline of 3 applies.

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 a specific action (scans a repository) and resource (versioned database migration files), and specifies the classification (additive vs destructive). It also names output fields, distinguishing it from sibling tools that focus on test failure analysis.

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?

Explicitly states when to use ('Use before recommending deployment') and the goal (determine rollback safety after go-live). It does not mention when not to use, but the clear context is sufficient given the unrelated sibling tools.

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, the description must disclose behavioral traits but only states the operation and return type. It does not mention side effects, limitations, or how the optional affectedTests parameter influences behavior, leaving the agent with limited insight into the tool's runtime characteristics.

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 two sentences with the core action front-loaded. Every phrase is informative, and there is no redundancy, making it highly efficient and easy 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?

The absence of an output schema shifts the burden to the description to explain the return value, but 'correlation mapping' is vague and lacks structural details. Edge cases and the behavior of the optional parameter are unaddressed, leaving the agent under-informed for a 3-parameter tool.

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 provides 100% description coverage for all parameters, so the baseline is 3. The description adds no extra parameter semantics beyond what is already in the schema, such as formatting rules or parameter interdependencies.

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 function with a specific verb ('Match') and delineates the exact inputs (changed files, failing tests) and objective (determine which failures are directly caused by changes). It distinguishes itself from siblings like detect_temporal_failure_patterns by focusing on code-change causation.

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 the tool: when analyzing a commit's changes against its failing tests. However, it does not explicitly mention alternatives or exclusion criteria, so it misses the full 'when/when-not' guidance needed for a 5.

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, the description carries the full burden for behavioral disclosure. It explains the scoring intent and the return value, but it does not disclose matching logic, edge cases, or any side effects. This is adequate but not rich.

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

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

Two sentences, front-loaded with the task, no filler. Every word adds value and the structure is easily scannable.

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 moderate-complexity tool with two well-specified parameters and no output schema, the description covers the core return value ('probability scores per test') and enough behavioral context to use the tool. It could be more explicit about the matching key, but overall it is complete.

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

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

Schema coverage is 100%, so the schema already fully documents both parameters and their fields. The description adds no meaningful parameter-level detail beyond what the schema provides, settling at the baseline of 3.

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

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

The description uses a specific verb ('score') and names the exact resource ('test failures' and 'flakiness history'), clearly distinguishing this tool from siblings like aggregate_suite_failures or correlate_code_changes. It also states the distinguishing outcome: separating flaky tests from real regressions.

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 clearly states the required inputs ('Given a list of test failures and a flakiness history'), providing clear context for when to use it. It does not explicitly mention when not to use it or name alternatives, but the prerequisites are obvious enough.

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 full responsibility. It conveys that the tool is read-only ('Analyzes') and clarifies the interpretive output: detected patterns imply time artifacts, not regressions. This adds useful context beyond a simple verb, though it does not detail the exact return format or any potential limitations.

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 two sentences, front-loaded with the core function and enriched with concrete examples and actionable guidance. Every sentence contributes value without 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 a single parameter and no output schema, the description covers the input semantics, the type of analysis, and the decision outcome. It is nearly complete, though it could explicitly state what the tool returns (e.g., a list of matched patterns) to fully close the loop for an agent.

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 schema already provides a description for the 'failures' parameter and its nested 'timestamp' property, achieving 100% schema coverage. The tool description adds little parameter-specific detail beyond stating 'test failures with timestamps', so it does not significantly augment 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's function: 'Analyzes a history of test failures with timestamps to detect chronometric patterns'. It distinguishes itself from siblings by focusing specifically on temporal patterns (hourly, monthly, weekly, DST), which is not covered by other tools like cross_reference_flakiness or correlate_code_changes.

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 explicit guidance: when a pattern is found, treat the failure as a time artifact and schedule a re-run rather than investigating source code. This directly tells the agent when to use this tool and what action to take, effectively distinguishing it from code-analysis siblings.

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 were provided, so the description bears the disclosure weight. It discloses the combination logic and output structure (verdict, confidence score, blockers vs safe-to-ignore breakdown), which is good transparency for a non-mutating decision tool. It does not describe edge cases or external effects, but for this type of tool the disclosure is adequate.

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

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

The description is a single sentence with strong front-loading ('The final step') and no redundant wording. It conveys the tool's purpose, inputs, and outputs efficiently.

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?

Without an output schema, the description explicitly lists the verdict types and breakdown categories, which is necessary for an agent to understand return values. It does not mention how affectedTests or customInfraPatterns factor in, but those are documented in the schema, so the completeness is adequate.

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

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

Schema coverage is 100% with all parameters described, so the baseline is 3. The description conceptually ties together failures, flakinessHistory, and changedFiles but adds no detail beyond the schema for format, affectedTests, or customInfraPatterns.

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

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

The description uses a specific verb ('combines') and resource ('failures, flakiness history, and code change correlation') to produce a distinct output (GO / NO_GO / INVESTIGATE verdict). This clearly distinguishes it from sibling tools that handle individual analyses, and the 'final step' phrasing positions it as the synthesis tool.

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?

Explicitly labels itself as 'the final step', implying it should be used after running the upstream analysis tools. However, it does not explicitly name alternatives or state when not to use it, which keeps it from a perfect score.

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