UETGPT

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds real behavioral value: the exact weighting formula applied and the guardrail that missing marks must be requested rather than guessed. Output schema handles return values, so no gap there.

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 sentences, zero waste, front-loaded with the trigger condition followed by the formula and the exclusion. 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 deterministic calculation tool with full schema coverage and an output schema, the description supplies everything an agent needs: trigger, formula, missing-data behavior, and scope boundary.

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 schema already documents every parameter including defaults and the Hafiz/NCC bonus semantics. The description only restates the formula weights; it adds little parameter-level meaning beyond what the schema provides, 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?

States a specific verb and resource (calculate the UET Taxila merit aggregate), names the exact inputs it consumes (SSC, HSSC Part-I, entry test), and explicitly scopes out other universities, so it is distinguishable from the sibling UET lookup/estimate 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?

Gives explicit triggering conditions ('when a user gives their SSC, HSSC Part-I and entry test marks and wants merit, eligibility, or program chances'), an explicit exclusion ('Do not use it for other universities' formulas'), and a behavioral instruction for missing data ('Ask for any missing marks; never guess them').

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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds valuable context beyond annotations by stating the data source ('official prospectus and FAQs with their source URLs'), which tells the agent the figures are authoritative and citable. It doesn't discuss caching, currency, or staleness, but those are minor gaps against an otherwise transparent read-only estimation tool.

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?

Single sentence structure, front-loaded with the primary use case, followed by coverage scope and exclusions. No filler; every clause 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?

Given an output schema exists and annotations cover safety, the description provides precisely the missing context: scope, source, and exclusions. Nothing else is needed for the agent to call this tool 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%, so the schema already documents both parameters, and the enum labels A/S are described. The description doesn't repeat parameter details, which is fine. Baseline for a 0-required-param tool with full coverage is around 3; the enum descriptions in the schema are thorough. Slight bump to 4 because the description's enumeration of cost components (hostel charges) aligns with the 'hostel' parameter, giving the agent intuition about what toggling it does.

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 verb (estimate) and resource (UET Taxila fees), then enumerates the exact cost components covered: tuition per semester, admission charges, first-semester total, hostel charges, application and entry test fees. This immediately distinguishes it from siblings like calculate_merit_aggregate or get_uet_program.

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?

Explicit when-to-use ('when a user asks what UET Taxila undergraduate admission costs') and explicit when-not-to-use ('Do not use it for other universities or for postgraduate fees'). This is exactly what an agent needs to route correctly.

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, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered elsewhere. The description adds the data scope and the 2022-2024 temporal range of the merit history, which is useful, but says nothing about caching, response shape, or failure behavior when the slug is absent.

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, zero filler. The positive scope and the returned fields come first, and the exclusion clause follows immediately after, making it easy to scan.

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 single-parameter, output-schema-backed, read-only lookup tool, the description supplies everything needed: what is fetched, which fields come back, the date range covered, and which requests belong to sibling tools. Nothing required for correct invocation 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 coverage is 100% and the single parameter is an enum with a slug example, so the schema carries the parameter documentation. The description only reinforces that exactly one program is fetched, adding no format or validation detail beyond what the schema provides — baseline 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?

States a specific verb ('get') and resource ('one UET Taxila undergraduate program') and enumerates the returned fields (credit hours, duration, accreditation body, minimum HSSC percentage, closing merit history). It explicitly distinguishes itself from list_uet_programs ('Do not use it for listing all programs'), so an agent can route correctly without opening schemas.

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 ('a user asks about one ... program') and when-not ('Do not use it for listing all programs or for programs at other universities'). The single-result scope constraint effectively selects this tool over list_uet_programs and the other 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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile needs no restatement. The description does add one genuine behavioral detail — that merit is the 'latest published Category A closing merit' with an accompanying year, implying data staleness semantics — but it mostly enumerates return fields, which an output schema already covers. No pagination, rate-limit or sorting-behavior 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?

Three compact sentences with zero redundancy: trigger first, return contents second, exclusions last. Every clause carries routing or scope 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?

An output schema exists, so return values need not be explained in depth, and annotations cover the safety profile. For a single-optional-parameter read-only list tool, the description supplies everything an agent needs: trigger, scope, sibling routing, and exclusion of out-of-scope requests.

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 is exactly one optional parameter (degreeType) with a full enum and 100% schema description coverage, so the schema already carries the semantics. The description never mentions the ability to filter by degree type, so it adds no meaning beyond the schema; baseline 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?

States a specific verb and resource ('list UET Taxila programs') and immediately scopes it to undergraduate offerings, with a concrete secondary use (comparing by closing merit / HSSC requirement). It explicitly distinguishes itself from get_uet_program and from other universities, so an agent can route correctly without opening the schema.

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?

Explicit when-to-use ('user asks which undergraduate programs... or wants to compare by closing merit or HSSC requirement') and explicit when-not ('Do not use it for one program's details (use get_uet_program) or for other universities'). Both the positive trigger and the alternative are named.

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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds genuinely useful behavior beyond that: it returns passages plus source URLs from UETGPT's index, and instructs the agent to answer from them and cite the URLs. It does not discuss result count, ranking, or truncation behavior, so it falls short of a 5.

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?

Front-loaded with the usage trigger, then behavior, then exclusions. Every sentence carries content, though the middle topic enumeration is long and slightly slows the read without adding routing precision.

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 one-parameter search tool with an output schema, the description covers trigger, scope, exclusions, alternatives, and how to use the returned passages. Nothing an agent needs to invoke or interpret it correctly 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 100% and the single parameter is fully documented in the schema (standalone English sentence, 3-500 chars). The description adds no syntax, format, or phrasing guidance beyond what the schema already provides, so the baseline 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?

States a specific verb and resource (search UET Taxila's indexed official pages) and enumerates the topical coverage, which cleanly separates it from the four calculation/lookup siblings. An agent can identify the tool from the description alone without opening the schema.

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 an explicit trigger ('when a user asks about UET Taxila and the other UETGPT tools don't cover it'), names the topical conditions, and states negative cases with alternatives ('do not use for merit aggregates, fee totals or program lists' and 'not for other universities'). This is the full when/when-not/alternative triad.

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