Sondaże.pro — Polish Polls

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

No annotations are provided, but the description's 'returns a list' phrasing makes clear this is a read-only lookup with no side effects. It does not disclose whether the list is static or configurable, nor any auth requirements, so the behavioral disclosure is adequate but thin.

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?

A single front-loaded sentence that identifies the resource and its contents with no filler. Nothing could be removed without losing 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 no output schema and no annotations, the description is the only contract, and it omits how the periods are represented (identifiers vs. localized labels) or their ordering — details an agent needs to actually consume the result and pass it to aggregation calls.

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 tool takes zero parameters, so there is nothing for the description to clarify; baseline 4 applies. The enumerations in the description describe return values rather than inputs.

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 ('Zwraca listę') and a precise resource ('dostępnych w systemie okresów agregacji sondaży'), and even enumerates the returned categories (miesięczne, tygodniowe, rolling). It is clearly distinguishable from siblings like get_polling_averages, though it does not name an alternative explicitly.

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?

No statement of when to call this versus the sibling polling tools, nor that the returned periods are meant to feed period parameters elsewhere. Usage is only implied by the tool's nature as an enumerator; there are no exclusions or prerequisites.

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 behavioral burden, yet it only states the output granularity and available filters. It omits that period_type and period_key are mandatory, says nothing about the simulated turnout/strategy inputs, error behavior, or defaults.

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?

A single tightly-written sentence with the resource and granularity front-loaded and no filler. Nothing extraneous, though the brevity comes partly at the cost of missing required-parameter context.

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 six-parameter tool with no annotations and no output schema, the description covers the return level and two filters but leaves the two required period parameters and the simulation inputs unexplained. Adequate but with clear gaps.

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 all six parameters are already documented in the schema; baseline is 3. The description echoes the voivodeship_code and district_number filters but adds no syntax or format 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 (Zwraca) and resource (lokalne projekcje poparcia partii) plus the exact granularity (380 powiatów). This granularity implicitly distinguishes it from siblings like get_district_projections, get_voivodeship_projections, and get_national_projection, though it never names them.

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 mention of filtering by voivodeship code or district number implies when the tool is useful, but there is no explicit when-to-use/when-not-to-use guidance and no reference to the sibling projection tools an agent should choose between.

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 are provided, so the description carries the full behavioral burden. It lists the returned data (projections, estimated turnout, vote distribution) but says nothing about read-only semantics, permissions, whether values are simulated/modeled, or any rate or caching behavior.

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?

A single front-loaded sentence that names the resource and its scope with no filler. It is efficient, though the lack of any follow-up sentence leaves behavioral and usage gaps unfilled.

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-style projection tool with fully documented parameters and no output schema, the description covers what the tool returns at a high level. It omits return structure detail and any behavioral context, which is a real gap given the absence of both annotations and an output 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 description coverage is 100%, with all four parameters documented in the schema (including enums for strategy and period_type), so the baseline is 3. The description's mention of "szacowaną frekwencję" loosely ties to the optional turnout parameter but adds no syntax or meaning beyond what the schema already supplies.

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 ("Zwraca") and resource ("projekcje poparcia partii") scoped to all 41 Sejm electoral districts, which distinguishes it by granularity from siblings like get_county_projections, get_voivodeship_projections and get_national_projection. It stops short of naming an alternative explicitly, so it is clear but not fully sibling-differentiating.

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 "we wszystkich 41 okręgach wyborczych" implies this is the district-level tool, giving implicit guidance on when to pick it over the county/voivodeship/national variants. There is no explicit when-to-use statement, no exclusion, and no reference to a named alternative.

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 behavioral burden. It discloses the methodology ('Multilevel Bottom-Up Model') and that the vote figure is an estimate, which helps interpretation, but says nothing about required inputs, turnout/strategy effects, or any operational constraints.

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?

A single well-formed sentence that front-loads the return value (national projection + valid-vote estimate) before the qualifier about the model and period. No wasted text.

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?

There is no output schema, so the description rightly sketches the return (projection plus estimated valid votes). However, with no annotations and no usage guidance, it leaves the agent without routing context against 12 sibling tools.

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%, with all four parameters documented including enums for strategy and period_type, so the schema does the heavy lifting. The description adds nothing about parameters, which is the expected baseline here.

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: returns a national (ogólnopolską) projection of party support and an estimated valid-vote count. The explicit 'ogólnopolska' scope implicitly distinguishes it from the county/district/voivodeship projection siblings, though those are never named.

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?

No when-to-use guidance, no prerequisites, and no reference to the regional siblings (get_county_projections, get_voivodeship_projections) that an agent must choose between. Usage is only inferable from the tool name.

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 are provided, so the description carries the full burden. It does disclose the shape of the returned data (thresholds, coalition status, colors, slugs), which is useful, but says nothing about whether the data is static or live, its scope (current parliamentary parties only?), or any caching/auth behavior.

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?

A single front-loaded sentence that states the resource and its contents with zero filler. Every listed item (thresholds, coalition status, colors, slugs) corresponds to actual returned fields.

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 zero-parameter reference lookup with no output schema, the description usefully enumerates the returned fields, which is what an agent needs to know. It is nearly complete; only the data's scope/currency is unstated.

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 tool takes zero parameters, so the baseline is 4. The description correctly implies no filtering arguments are needed — the whole dictionary is returned — and requires no further parameter explanation.

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 ('Zwraca słownik partii politycznych w Polsce') and enumerates the payload (electoral thresholds, coalition status, colors, slugs). This clearly separates it from siblings like get_pollsters, get_surveys and get_political_events, which cover different resources.

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 lookup nature is implied by the description — it is a static reference of parties — but there is no explicit statement of when to call it versus alternatives such as get_polling_averages or get_seat_projections (e.g. as a slug/color lookup before rendering results). Usage is inferable rather than 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?

With no annotations, the description carries the full behavioral burden. It does not disclose ordering, pagination behavior, what happens when the date range is omitted, or the practical meaning/limits of 'bombshells'. Calling it a read-only list is inferable from 'Zwraca listę', but nothing beyond that is explained.

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?

A single, front-loaded sentence that states the resource first and the optional filtering afterward. No filler, though the parenthetical costs a few words for modest clarification value.

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 simple read-only list tool with 3 fully documented parameters and no output schema, the description is minimally sufficient. It still leaves return structure, ordering, and default date-range behavior unstated, which is a modest gap rather than a critical one.

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 limit, date_from, and date_to with formats and bounds. The description only echoes the date-range filtering and omits the limit parameter entirely, adding 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?

The description names a specific verb+resource ('Zwraca listę kluczowych wydarzeń politycznych') and even clarifies the domain concept via the parenthetical '(tzw. bombshells) wpływających na dynamikę poparcia społecznego'. It is clearly distinguishable from siblings like get_polling_averages or get_parties, though it does not explicitly contrast with them.

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 only usage signal is 'z opcjonalnym filtrowaniem po zakresie dat', which merely restates the optional parameters. There is no statement of when to prefer this tool, what question it answers, or any exclusion relative to the sibling projection/polling 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 carries the full behavioral burden. It usefully discloses the normalized-vs-raw (RAW) distinction and the rolling-window options, but says nothing about permissions, rate limits, data freshness, or what the response contains beyond the averages.

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?

A single dense sentence with the core operation front-loaded and no filler. It is appropriately sized for the tool, though the parenthetical enumeration of period formats is somewhat list-like rather than structured.

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 five parameters, no annotations, and no output schema, the description is only minimally adequate. It covers the period dimension but leaves the strategy weighting, survey_id filtering, and the shape of returned data entirely to the schema, which the agent must open to call it confidently.

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 five parameters including strategy, survey_id, period_key, and strategy_version. The description only reinforces the period-type portion already covered by the schema, adding no syntax or semantics beyond it. 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?

The description states a specific verb and resource ('Pobiera ... średnie sondażowe poparcia partii politycznych w Polsce'), and its mention of 'znormalizowane lub surowe (RAW)' plus the period vocabulary hints at how it differs from the sibling get_polling_averages_series. It never names that sibling or any other alternative, so it falls short of a 5.

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 gives implicit usage context by enumerating supported period types (month, ISO week, rolling 14/21/30 days), which tells an agent what input shapes are valid. However, it offers no explicit when-to-use/when-not-to-use guidance and does not point to get_polling_averages_series for time-series needs.

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 behavioral burden. 'Pobiera' makes clear this is a read-only retrieval, and it discloses the scope (monthly series, 12 months or a year), which is useful. It says nothing about empty-month handling, result size, or the raw-vs-processed averaging behavior, though those are partly covered by parameters.

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?

A single front-loaded sentence that names the resource, its scope, and the intended use without padding. Nothing is wasted, though it is brief enough that some useful context is omitted rather than compressed.

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 retrieval tool with six fully documented parameters and no output schema, the description covers purpose, scope, and use case adequately. The main gap is that it does not clarify the raw vs. weighted/arithmetic strategy distinction or how it relates to get_polling_averages.

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 restates the range options already documented in the schema and adds no meaning for strategy, pollster_id, strategy_version, or include_empty_months, so it does not go beyond structured data.

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: retrieving a time series of monthly polling averages for Polish parties, scoped to last 12 months or a chosen year. It is distinguishable from the sibling get_polling_averages via the explicit 'szereg czasowy' (time series) framing, but it never names that sibling, so the differentiation is implicit rather than stated.

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 'do analizy trendów historycznych' (for historical trend analysis) implies the intended use case, and the parenthetical mentions the two range modes. However, there is no explicit when-to-use vs when-not guidance and no reference to the overlapping sibling get_polling_averages, leaving the agent to infer which tool fits.

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 behavioral burden, but it does disclose the return shape ('słownik' = dictionary) and concrete example values, which matters since no output schema exists. It says nothing about read-only safety, ordering, or error behavior, so gaps remain.

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?

A single, front-loaded sentence that identifies the resource and illustrates its contents with real examples. No filler or redundant restatement of the tool name.

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 zero-parameter reference lookup with no annotations or output schema, the description covers what the data is but omits the one thing an agent most needs here: how this reference list differs from siblings that return surveys or parties, and whether the returned keys are the identifiers those tools 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate. Baseline 4 applies; the description correctly refrains from inventing filtering options that do not exist.

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 concrete verb+resource: it returns a dictionary of polling/research institutions conducting political surveys in Poland, with named examples (CBOS, IBRiS, IPSOS, etc.). This clearly separates it from siblings like get_parties or get_surveys, though it never explicitly names what it is not.

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?

There is no when-to-use guidance, no mention of the sibling tools that return actual survey data (get_surveys, get_polling_averages), and no indication of when this reference lookup is needed versus those. The agent must infer usage entirely.

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 and does disclose meaningful behavior: the d'Hondt method, the multi-level bottom-up aggregation, and the restriction to normalized strategies. It omits, however, the return structure, potential error behavior, or computational cost for a heavy simulation. The methodological disclosure is real but incomplete for a complex 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?

A single dense sentence that front-loads the core action (seat division) before the method and constraint. It is efficient and wastes little, though the parenthetical model chain adds weight.

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 5-parameter, complex simulation tool with no output schema and no annotations, the description explains the computation but says nothing about the returned data structure or how period/survey selectors affect results. The well-documented schema covers inputs, but the agent lacks visibility into output shape.

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 five parameters including period formats and the enum for strategy. The description adds a reinforcing constraint about normalized-only strategies but no syntax or format detail beyond the schema. Baseline 3 applies when the schema does the heavy lifting.

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 — simulating the division of 460 Sejm seats via the d'Hondt method — and specifies the data basis (averaged polls from a chosen period) and model (bottom-up 380 counties -> 41 districts -> national). This clearly distinguishes it from siblings like get_national_projection or get_district_projections, which produce vote projections rather than seat allocations.

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 the context (seat allocation from aggregated polls) and discloses a key constraint — only normalized strategies are allowed. However, it never states when to prefer this tool over get_national_projection or the other projection siblings, nor any prerequisites. Usage is inferable but not explicit.

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. 'Pobiera' implies a read operation and it discloses the shape of the returned data, but it says nothing about pagination behaviour despite page/limit/offset parameters, ordering, result caps, or anything destructive/irreversible.

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?

A single front-loaded sentence with the verb first and the payload enumerated compactly; no filler or repetition. It is a bit dense but 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?

Because no output schema exists, the description usefully enumerates the returned fields (results, metadata, N, dates, party structure), which covers the return-value burden. However, it omits routing guidance against the many sibling endpoints and any pagination semantics for an 8-parameter, unannotated 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?

Schema description coverage is 100%, so all eight parameters (including date range, period_key, period_type, pollster_id) are already documented in the schema with defaults and formats. The description adds no parameter-level meaning beyond the schema, which matches the baseline of 3 when the schema does the heavy lifting.

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 verb 'Pobiera' plus the resource 'kolekcję pojedynczych sondaży poparcia partii politycznych' names exactly what is returned and even enumerates the payload (results, pollster metadata, sample N, dates, party structure). It is clearly distinct from aggregation/projection siblings, though it never explicitly contrasts itself with the closest sibling get_surveys_ids.

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?

There is no when-to-use guidance, no exclusions, and no mention of alternatives such as get_surveys_ids (IDs only) or get_polling_averages (aggregated). The agent must infer the routing decision entirely from the sibling names.

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 are provided, so the description carries the full burden. It does disclose the shape of the result (a list of numeric IDs, i.e. not full records), which is useful behavioral context, but says nothing about read-only nature, ordering, pagination, or behavior for an empty/invalid period.

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?

A single front-loaded sentence that identifies the resource and its scope with no filler. It is arguably too terse rather than too long, but as pure conciseness it is efficient.

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 lookup with no output schema and no annotations, the description is minimally adequate: the agent knows what it retrieves and the schema covers all inputs. It omits any guidance on what to do with the returned IDs or how they relate to get_surveys, leaving a modest gap.

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 both parameters plus the enum are fully documented in the schema, so the baseline is 3. The description only adds the framing concept of an 'aggregation period', which loosely links the two parameters but adds little syntax or format 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 and resource ('Zwraca listę numerycznych identyfikatorów sondaży') scoped to a given aggregation period, so the agent knows it returns IDs rather than full survey objects. It does not explicitly contrast itself with the sibling get_surveys, so it stops short of full sibling differentiation.

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?

There is no explicit statement of when to use this tool versus get_surveys or the projection tools. The agent can only infer from 'returns IDs' that this is the lightweight lookup, but no condition or alternative is 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?

No annotations are provided, so the description carries the full behavioral burden. It discloses the output scope ('pełny okres agregacji' and 16 regions) but says nothing about safety profile, permissions, response size for 16 regions, or behavior when data is missing for a region.

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?

A single front-loaded sentence with the verb and scope stated immediately and no filler. It is efficient, though terse enough that it leaves obvious questions unanswered.

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 no output schema and no annotations, the description must convey enough to call correctly; it names the returned quantity and its geographic granularity, which is the minimum viable. It omits return shape hints (per-party/per-region structure) and any prerequisite for building a valid period_key.

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%, with all four parameters documented including two enums and example period_key formats, so the baseline is 3. The description adds no parameter-level detail (e.g. how strategy or turnout interact with the returned projections).

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 ('Zwraca') and a precisely scoped resource: party support projections broken down into the 16 voivodeships for the full aggregation period. This granularity implicitly separates it from get_county_projections, get_district_projections and get_national_projection, though no sibling is named explicitly.

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?

No when-to-use guidance and no alternatives named. An agent cannot tell from the text when voivodeship granularity is preferable to county, district or national, nor whether it should call get_aggregation_periods first to obtain a valid period_key.

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