Clearance Adjudications Search Engine

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

Beyond annotations (readOnlyHint=true), the description adds that the tool is purely descriptive and never predictive, along with specific behavioral traits (e.g., covers favorable rate, judge findings, rebuttals). No contradictions with annotations.

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

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

The description is very concise, using two sentences to front-load the core purpose and then detail the specific metrics. Every sentence contributes meaning 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 no output schema, the description adequately explains the output (counts, denominators, rates) but could be more explicit about the format (e.g., table vs text). Still, it provides enough context for an agent to understand what to expect.

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?

There are zero parameters, so the baseline is 4. The description adds value by explaining what the tool computes, though it does not need to cover 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 tool's purpose: providing outcomes of cases with candor allegations, specific to falsification, omission, or lack of candor. It distinguishes from siblings like get_case (general case info) and get_statistics (broader stats), making its unique focus evident.

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 descriptive analysis of candor outcomes, noting it is never predictive. However, it does not explicitly state when to use this tool over alternatives like get_candor_outcomes (nonexistent) or when not to use it, leaving some ambiguity.

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?

Annotations already mark the tool as readOnly and non-destructive. The description adds value by detailing the exact data returned, which is beyond annotations. No contradictions.

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

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

Two sentences cover purpose and usage efficiently. The description is front-loaded and every sentence adds 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 the single parameter and no output schema, the description adequately explains the tool's function. However, it could mention error handling for invalid IDs, but overall it's sufficient.

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% for the sole required parameter. The description reinforces that case_id should come from search_cases, providing semantic context beyond the schema's basic description.

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 explicitly states the tool's purpose: fetching a full DOHA case with specific contents (allegations, findings, outcome, etc.). It distinguishes from siblings like search_cases and similar_cases by specifying the detailed output.

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 indicates the prerequisite ('Use case_id from search_cases'), guiding the agent to obtain the ID first. It lacks explicit exclusions or alternatives, but the context is clear enough 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. Description adds that the tool is descriptive, not predictive, and explains the reporting format, providing added behavioral context beyond annotations.

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

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

Two sentences, front-loaded with purpose, second sentence adds essential nuance. No redundant information.

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?

No output schema, but description adequately explains what the tool returns (median years, counts, split by favorable/unfavorable). Also specifies input scope.

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% for the single optional parameter, so baseline is 3. The description lists the concerns but does not add significant meaning beyond the schema's parameter description.

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?

Description clearly defines what the tool does: measures time between most recent conduct and decision for incident-type concerns, reporting median years and counts. It is distinct from siblings like get_candor_outcomes or get_statistics.

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?

Specifies scope to incident-type concerns and clarifies descriptive association, not prediction. No explicit when-not or alternatives, but context is clear.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's disclosure of behavioral traits is less critical. However, it adds context about always including sample sizes and grouping options, which is useful beyond the annotations. No contradictions.

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

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

The description is concise (two sentences) and front-loaded with the core purpose. Every sentence adds value. It is appropriately structured for quick agent comprehension.

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 4 parameters and no output schema. The description clarifies grouping and sample sizes but does not describe the output format (e.g., how results are structured). For a statistics tool, more detail on return values would improve completeness, but the description is adequate for basic use.

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 50% (by and guideline have descriptions; year_from and year_to do not). The description adds context for the by parameter (default year) and the guideline parameter (single letter A-M), but provides no additional meaning for year_from and year_to beyond what the schema's names suggest. Thus it adds value but does not fully compensate for the missing 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 it provides 'grant/denial statistics over decided hearing-level DOHA cases', which is a specific verb and resource. It mentions grouping options and references the site's Insights page for population rules. It distinguishes itself from sibling tools like get_case (individual) and search_cases (search) by focusing on aggregate statistics.

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 lacks explicit when-to-use or when-not-to-use guidance. It only says 'Same population rules as the site's Insights page', which provides a reference but no direct comparison to alternatives. No exclusions or prerequisites are mentioned.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description does not need to restate safety. It adds that timelines are derived from dates in the decisions themselves, which is useful context. However, it does not disclose potential limitations (e.g., incomplete dates, data freshness) that would increase transparency.

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, well-structured sentence that front-loads the core purpose ('Measured DOHA timelines') followed by specifics. Every word adds value with no redundancy, achieving maximum conciseness.

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 no output schema exists, the description should clarify the return format (e.g., median values, list of decisions). It mentions only median days but omits structure, edge cases (missing dates), or whether multiple years are aggregated. While focused, it leaves gaps for agent comprehension.

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 the year parameter already described. The description restates its optionality without adding new meaning beyond the schema. Baseline 3 applies since the schema already carries the semantic load.

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 identifies the tool as computing median timelines (days from Statement of Reasons to hearing and to decision) using decision dates, with optional year scoping. It specifies the resource (DOHA timelines) and action (measure medians), distinguishing it from other tools like search_cases or get_statistics.

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 mentions optional year scoping but provides no explicit guidance on when to use this tool versus alternatives like get_statistics or similar_cases. It lacks when-not-to-use conditions or prerequisites, leaving the agent to infer usage context.

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