Country Stats Facts

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

Annotations already cover safety (readOnly, idempotent, non-destructive), so the description's added value is the behavioral detail it gives: ranked output with value and year, reporting of countries lacking data, and the source plus last-updated date. That is real context beyond annotations, though error/failure behavior is not addressed.

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?

Four sentences, front-loaded with the trigger condition followed by parameters, return shape and the group-expansion constraint. The return-value sentence is a little long but every clause carries usable 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?

With an output schema covering return structure and annotations covering safety, the description supplies the remaining essentials: trigger conditions, cardinality limits, group rejection, and pointer to the source/date metadata. An agent could call this correctly without further context.

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 listing country count, year and order largely restates the schema. The one genuine addition is the semantic rule that group names such as Nordic or G7 are rejected and must be expanded into member countries, which the schema does not convey.

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 names a specific verb (compare/rank) and resource (countries on one statistic) and gives two concrete example phrasings, so the agent immediately knows this is a multi-country ranking tool rather than a single-country lookup like get_country_indicator.

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?

It states clearly when to use it ('when the user asks to compare or rank countries on one statistic') and adds a hard exclusion — groups like Nordic or G7 must be expanded first. It does not explicitly name the sibling get_country_indicator as the single-country alternative, which keeps it just below 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 readOnly/idempotent/openWorld, so safety is covered; the description adds genuinely new behavior — the default-year fallback ('latest year with data'), the data vintage ('date it was last updated'), and the static-data caveat. It stops short of describing error/ambiguity handling for unrecognized country names, which is the one remaining gap for an open-world lookup.

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 trigger condition and examples before logistics, and every sentence carries information. The inline re-listing of all fourteen indicators is somewhat redundant with the enum, costing a little density.

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 three-parameter read tool with a full schema, complete annotations and an output schema, the description covers trigger, inputs, default behavior, return shape and scope limits — everything needed to invoke it correctly without opening the schema.

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 baseline is 3; the description adds value by restating the indicator vocabulary as a discovery aid and clarifying that year is optional and defaults to the latest available year. The country param's name-or-ISO flexibility is only echoed from the schema rather than extended, keeping it below top marks.

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?

Names a specific verb+resource ('one official statistic for one country') and anchors it with two concrete user-question examples. It also draws a clear boundary against siblings by declaring it reads annual World Bank statistics only — no forecasts (get_imf_forecast) or regions.

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 opening 'Use this when the user asks for one official statistic for one country' plus the Vietnam/Spain examples give an explicit trigger condition, and the closing 'no live data, forecasts or regions' tells the agent when to route elsewhere. Both the positive and negative cases are 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?

Annotations already declare readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context beyond that: the tool is pollable (pending until a reply arrives, then answered) and carries an explicit trust boundary warning that reply content is data, not instructions — a meaningful prompt-injection 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 tight sentences: purpose first, then polling behavior, then the trust caveat. Every sentence carries distinct information and nothing is padded.

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 read tool with an output schema and full annotation coverage, nothing an agent needs is missing. The polling semantics and the injection warning go beyond what structured fields provide, closing the remaining gaps an agent would otherwise have to discover by trial.

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?

Only one parameter exists and the schema already documents it fully (coverage 100%, including the ^fb_[0-9a-f]{32}$ pattern and the note that submit_feedback returned it), so the baseline of 3 applies. The description repeats the ticket-id linkage but adds no format or validation detail 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?

States a specific verb (Read) and resource (maintainers' reply to feedback), and ties it causally to the sibling tool submit_feedback via the ticket id. An agent can distinguish it from submit_feedback, screen_token, and wallet_identity without opening any 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?

Clearly establishes the precondition: this is used after submit_feedback, with the ticket id that call returned. It does not state any exclusions or alternative ways to retrieve feedback status, but the usage context is unambiguous.

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/open-world/non-destructive, and the description adds genuinely useful context beyond that: the return window (current year plus two), what metadata accompanies the numbers (unit, WEO edition, publication date), and the important caveat that these are IMF projections only, not the tool's own forecasts. No auth, rate-limit, or failure-mode information is given, which keeps it 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?

The trigger condition is front-loaded in the first sentence and the return shape follows logically. Slightly verbose with the embedded user-question example and a closing caveat that partly repeats 'the IMF's figures', but every sentence carries 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 two-parameter, no-side-effect lookup with an output schema present, the description covers the trigger, the inputs, the projection horizon, and the interpretation caveat — enough to call it correctly. The only gap is disambiguation against get_country_indicator, which an agent must resolve from the sibling names alone.

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 indicator enum already spells out growth/inflation/debt with definitions, so the description's mention of 'the indicator (growth, inflation or debt)' is largely a restatement. The country format guidance (name or ISO code) likewise lives in 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 ('the IMF's figures' / projections) and embeds a concrete example question, so the agent immediately knows the class of request it serves. It stops short of naming how it differs from the sibling get_country_indicator, which covers an adjacent data need, so it's clear but not sibling-differentiated.

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?

'Use this when the user asks what the IMF projects for a country' plus a sample utterance gives a clear trigger condition. There are no exclusions or named alternatives (e.g., 'for non-IMF indicators use get_country_indicator'), so the agent must infer the boundary.

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 read-only, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuine behavior beyond that: results are ranked best-match-first and the empty-input case enumerates every tool. It says nothing about result limits or pagination, which keeps it off 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?

Four short sentences, front-loaded with what the tool does and what it returns before the usage hint. Nothing is redundant given the description is the only place ranking and empty-input behavior are stated in prose.

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 discovery tool with an output schema and full annotation coverage, this covers purpose, inputs, ranking, return fields and the no-argument case. An agent has everything needed to decide to call it and to interpret the results.

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 schema already explains that task, query and keyword are searched together and that omitting all lists everything. The description's 'task or keyword' phrasing actually omits 'query', so it adds no meaning beyond the schema and is marginally less complete than it.

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 the tools on this server') and adds the matching mode ('by task or keyword in plain words'). It also names what comes back (name, title, one-line summary, input fields), so an agent knows exactly what this tool produces versus the sibling action 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?

'Use it when you are not sure which tool here fits a task' gives a clear triggering condition, and 'With no words it lists every tool' covers the degenerate case. It does not state when *not* to use it or name an alternative, but no sibling here offers overlapping discovery functionality, so the gap is minor.

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?

Adds substantial context beyond the annotations: it returns a ticket id, feedback is explicitly not a chat and nothing in it is executed, and links/emails/phone numbers are stripped with nothing stored about the caller. That privacy and no-execution disclosure is genuinely useful given openWorldHint=true and readOnlyHint=false.

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-loads what the tool sends, then what to pass, then what it returns, in three dense sentences with no filler. Every clause carries actionable 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?

Covers purpose, the enumerated kinds, the optional tool/error inputs, the 1000-character bound, the return value (ticket id), and privacy handling. With an output schema present, nothing further is required for an agent to call 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 description coverage is 100%, so the schema already documents all four parameters including the kind enum. The description restates the message length limit and the optionality of tool/error ('if you know them') but adds no syntax or format detail beyond the schema, making the baseline 3 correct.

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 ('Send feedback, a bug report or a tool request to the maintainers of the tools on this server') and enumerates the concrete situations (missing tool, stale data, broken/wrong answer). It is clearly distinguishable from the read-side sibling get_feedback_reply.

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 triggering conditions for each kind (need_tool, need_data, bug, other) and tells the agent to include the tool name and error text when known. It does not explicitly name the alternative get_feedback_reply or state when NOT to use this tool, so it falls short of full routing guidance.

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