Pakistan Case Law

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

Annotations already mark the tool as read-only and idempotent. The description adds significant behavioral detail beyond those annotations: what each section returns, the ~40,000-character cap on full text, and the body_truncated/body_chars_total flags. This gives an agent accurate expectations about cost and output limits.

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 compact and front-loaded, with each sentence earning its place. The main purpose comes first, followed by a structured explanation of the two sections and the truncation behavior. No filler or redundant restatement of the schema.

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 read-only single-record fetch tool with a strong schema and output schema, the description covers everything an agent needs: what each section contains, the default, the cost tradeoff, and the truncation signal. Nothing important is missing.

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 documents case_id and section, and the description enriches section semantics considerably: 'summary' includes metadata, AI headnote, laws/provisions, and keyword tags, while 'full' adds capped body text. The response_format parameter is not described, but its markdown/json enum is self-explanatory and does not create a meaningful gap.

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 opens with a specific verb and resource: 'Fetch one judgment by id.' It clearly distinguishes this tool from the sibling search/citation tools by targeting a single known case rather than querying or aggregating.

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 gives clear guidance within the tool: start with the cheap 'summary' section to judge relevance before requesting the full text. It does not explicitly name alternative tools for finding or searching cases, but the case_id source in the schema and the 'by id' phrasing make the intended usage 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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context beyond annotations by specifying that the graph walk is forward-only, restricted to in-corpus cases, and that results represent the precedents relied upon by the given judgment. This is meaningful additional 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 compact and front-loaded: the first sentence states exactly what the tool does, the second clarifies the relationship with a sibling, and the final sentence gives a practical usage heuristic. Every sentence earns its place, with no filler or repetition.

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 read-only listing tool with an output schema and strong annotations, the description covers the tool's purpose, direction, scope boundary (in-corpus), relationship to sibling tools, and a concrete use case. Pagination details are handled by the input schema defaults and constraints, so nothing essential is missing for correct 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 only 25%, with only case_id documented in the schema. The description provides no additional meaning for limit, offset, or response_format, and it does not compensate for the low schema coverage. The parameters are reasonably self-descriptive, but the description itself adds no semantic 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 states a specific verb and resource ('Walk the citation graph FORWARD: list the in-corpus cases that THIS judgment cites') and explicitly differentiates from caselaw_get_cited_by, which is the sibling that lists cases citing this judgment. The directionality is unambiguous, so an agent can select it correctly without guessing.

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 explicitly says to combine with caselaw_get_cited_by to traverse precedent backward and forward, and it gives a concrete usage scenario ('Most useful on RECENT judgments'). This is clear, actionable guidance that names the complementary alternative and explains when the tool provides the most value.

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 establish readOnly, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond those annotations: it explains the traversal direction, that results are returned newest first, and that large result sets are possible for leading cases. No contradiction with annotations exists.

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 compact, front-loaded with the core mechanism, and every sentence adds value: direction, citation meaning, ordering, use-case guidance, and a volume warning. There is no redundant filler.

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 read-only citation-listing tool, the description covers direction, ordering, use case, and scale. Annotations handle safety, and an output schema exists, so return-value details are not required here. Minor gaps such as explicit pagination advice or explicit sibling differentiation keep it from a perfect score.

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 only 25%, and the tool description does not compensate. It adds no meaning for limit, offset, or response_format beyond their schema titles and defaults, and it only indirectly refers to case_id as 'THIS judgment.' The low coverage means the description should have explained parameter behavior more explicitly.

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 states a precise verb and resource: 'Walk the citation graph BACKWARD: list later cases that cite THIS judgment.' It also clarifies the semantic content of the citations (followed, distinguished, relied upon), which clearly distinguishes this tool from sibling tools like caselaw_get_citations or caselaw_get_case.

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 gives clear context for when this tool is most useful: 'Most useful on OLDER or landmark judgments' and warns that 'A leading case can have hundreds of citing cases.' It does not explicitly name alternative tools or state when not to use it, but the backward-citation direction and use-case guidance make the intended usage 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, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral detail beyond those: exact-page matching, list-everything behavior when page is omitted, and the return shape being the same as caselaw_search.

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?

Three short sentences with no filler. The core purpose is front-loaded, followed by a concrete example and the key page-omission nuance. Every sentence earns its place.

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 lookup tool with strong annotations, an output schema, and well-named parameters, the description covers the essential behavior: exact citation lookup, page omission behavior, and output compatibility with caselaw_search. Remaining details like pagination and response format are sufficiently represented by the schema and defaults.

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 adds meaningful semantics for the core params: journal and year through the example, and especially page, whose omission changes the result set. However, schema description coverage is only 33%, and limit, offset, and response_format receive no descriptive help from either the schema or the 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?

States a specific action and resource: 'Find the judgment(s) at an exact law-report citation.' The example with journal='PLD', year=1995, page=34 makes the lookup concrete, and referencing caselaw_search helps distinguish this citation-lookup tool from search-based siblings.

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?

Gives clear invocation context: use it when you have an exact law-report citation, and explains the important behavioral alternative of omitting page to list everything in a journal+year. It does not explicitly name sibling alternatives or say when not to use them, so it falls short of 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context by specifying that results are ranked by citation count and that it returns landmark judgments, which goes beyond the annotations without contradicting them.

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 zero fluff. The main purpose is front-loaded, and the landmark/ranking context is provided in a compact, readable way. Every word earns its place.

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?

Combined with the rich annotations and presence of an output schema, the description is largely complete: it states what the tool returns, how results are ranked, and the legal domain. It does not mention pagination or output format behavior, but those are covered by the input schema and output schema, so the gap is minor.

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 for explaining parameters, but it does not mention limit, offset, or response_format at all. While the parameter names and defaults are reasonably self-explanatory, the description provides no additional semantics or usage hints, leaving the full burden on 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 a specific verb ('List') and resource ('most-cited (landmark) judgments in the corpus'), with a precise ranking criterion ('by how many other cases cite them'). It does not explicitly differentiate from sibling tools, but the core purpose is unambiguous and distinct from search or individual case retrieval.

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 phrase 'A good entry point for the leading authorities on Pakistani law' gives contextual guidance on when to use the tool, implying it is for discovering landmark cases. However, it does not explicitly mention alternatives or state when not to use this tool versus siblings like caselaw_search or caselaw_get_cited_by.

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 readOnly/idempotent/non-destructive, so the description's burden is lighter. It usefully adds ranking behavior (nearness to legal point, court/bench/recency/citations) and the returned fields, though it does not describe pagination/offset behavior beyond the schema.

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?

Three short paragraphs: scope first, then ranking/sort behavior, then return fields and sibling routing. No filler and each sentence adds useful 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?

For a search tool with five parameters and an output schema, the description is fully sufficient: it defines scope, ranking, sort semantics, return fields, and sibling alternatives. An agent can select and call this tool correctly with the information provided.

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 only 40%, so the description compensates by explaining what 'relevance' means through the ranking algorithm and when to use each sort value. Query semantics are implied by 'full-text search' and the schema's keyword examples. It does not add much about limit/offset/response_format, but those are conventional and partially encoded in 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?

Description states a specific action ('Full-text search') with a clear resource (197,000+ Pakistani judgments from Supreme Court & High Courts, 1970–2025). It distinguishes itself from sibling tools by naming caselaw_lookup_citation for exact citations and caselaw_get_case for reading a case.

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?

Gives explicit when-to-use guidance: sort='newest' for current law and sort='court' for authority, and names the sibling tools to use instead for citation lookup or reading a case. This is actionable routing without 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?

Annotation Contradiction: the description explicitly says coverage is partial and that an empty result 'does NOT mean no such judgment exists', while the annotations set openWorldHint to false, implying closed-world semantics where absence of a result is meaningful. This directly contradicts the annotation and could mislead an agent that trusts the structured metadata over the description.

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 longer than average but every section earns its place: purpose, source of the questions, coverage caveat, fallback, and usage caution. It is front-loaded with the core distinction, and the all-caps caveats make the most important warnings hard to miss.

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 read-only search tool with an output schema, the description covers everything an agent needs: what is searched, the coverage limitation, the alternative tool, the non-authoritative nature of the AI-written questions, and the next step via caselaw_get_case. Nothing essential is missing.

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 only 25%, but the description substantially compensates for the critical required query parameter by explaining what kind of legal question to enter and providing concrete examples. The remaining parameters—limit, offset, and response_format—are left to names, defaults, and schema-defined choices, which is adequate for standard controls.

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 opening sentence states the verb, resource, and distinctive mechanism: 'Find judgments by the QUESTION they settle, rather than by words they contain.' This clearly separates it from caselaw_search without needing to inspect sibling definitions.

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 explicitly tells the agent when to prefer this tool—when matching the shape of a legal problem matters—and names the fallback: 'Fall back to caselaw_search, which covers the whole corpus.' It also explains what an empty result means, preventing the agent from drawing the wrong conclusion.

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