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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the safety profile is covered. The description adds only the 'without forcing a refresh' constraint, which is useful but modest given annotations carry the main behavioral burden.

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 one-line purpose followed by two tightly scoped use-case bullets; every line earns its place with zero filler or restatement of the title.

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 no explanation, and annotations fully cover the read-only/idempotent safety profile. For a zero-parameter status read, nothing an agent needs to invoke 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?

The tool takes zero parameters, so there is nothing to document and no ambiguity for an agent to resolve. Baseline of 4 applies for a no-argument tool.

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 (Return) and resource (dump cache status), and the qualifier 'without forcing a refresh' distinguishes it from the mutation-oriented sibling lobbywatch_refresh_dump. It stops short of naming that sibling explicitly, but the scope is unambiguous.

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?

Provides concrete use cases ('How fresh is the cached data?' / 'When was the dump last loaded?') and an implicit when-not via 'without forcing a refresh'. Clear usage context, though it never names lobbywatch_refresh_dump as the alternative for forcing a reload.

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

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

Annotations already indicate read-only and idempotent behavior. The description adds transparency by noting the live REST API and fresh data, plus the inclusion of connected entities. No contradictions, but more detail on rate limits or auth could further enhance.

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

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

The description is concise with a clear purpose statement followed by illustrative use cases. Every sentence adds value, and the structure is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description adequately covers what the tool does, including its return content (connected organisations and parliamentarians). With an output schema available, further detail on return format is unnecessary.

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 input schema has no parameter descriptions (0% coverage), but the description compensates by showing examples of using a name or ID, clarifying the parameter's purpose and format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool fetches a lobby group including connected organisations and parliamentarians. It is specific and distinguishes from sibling tools that focus on individual parliamentarians, rankings, or transparency quotes.

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 provides example use cases that illustrate common queries, giving clear context for when to use the tool. However, it lacks explicit guidance on when not to use it or alternatives among 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 cover the safety profile (readOnly, idempotent, closed-world), so the bar is lower, and the description adds non-obvious behavior: fuzzy miss returns near-miss candidates rather than an authoritative empty result. Useful and specific, though nothing is said about profile size, rate limits, or error 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?

Purpose and fuzzy-miss caveat are front-loaded, followed by clearly labeled Args and Use cases blocks. Slightly verbose with the use-case list and audit reference, but every section is scannable and none of it is 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?

A single-parameter lookup with an output schema present, so return-field detail is not required, and the description still explains the fuzzy-miss return behavior. Complete enough for correct invocation; only marginal gaps remain around input sanitization and failure modes.

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?

With schema description coverage at 0%, the description carries the burden and does so: it explains name_or_id accepts either a numeric Lobbywatch ID as a string or a name, and that name matching is fuzzy with partial last names supported. That is substantive semantics beyond the bare string type in the schema, though it omits edge cases like max length behavior.

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 (look up) and resource (Swiss federal parliamentarian) plus what it returns (full profile including interessenbindungen). Clearly distinct from siblings like lobbywatch_search_parlamentarier_nach_branche and lobbywatch_list_interessenbindungen.

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?

Provides three concrete use cases that make the intended invocation context clear, and explains the fuzzy-miss path. It does not, however, explicitly name alternatives or state when not to use it (e.g., vs. the search-by-branche sibling), so it stops 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.

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

Annotations already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so the safety profile is covered. The description adds that kriterium is restricted to two values and that filters can be combined, but says nothing about ordering direction, tie-breaking, or behavior of defaults 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?

The one-line purpose is front-loaded, followed by a compact Arg list and three terse examples. Every element earns its place, though the labeled sections add slight overhead relative to the amount of content.

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 present, return values need not be described, and all four parameters plus the two enum values receive coverage. Combined with annotations covering idempotency and safety, an agent has enough to invoke it correctly; only ordering/ranking semantics remain 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?

Schema description coverage is 0%, so the description must carry the load, and it largely does: it names each of the four parameters, gives examples for kommission ('WBK-N') and partei ('SP', 'SVP', 'Mitte'), and the use cases disambiguate the opaque enum values (e.g. 'night-most full-time mandates' for anzahl_hauptberuflich). It still does not explain the full semantics of anzahl_interessenbindungen beyond 'IB count'.

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 ('Rank parliamentarians by a criterion'), which is clearly distinct from sibling tools like get_parlamentarier or list_interessenbindungen. It does not explicitly name or contrast against those siblings, 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?

The 'Use cases' block gives three concrete scenarios that show when the tool applies (ranking by mandate count, filtering by party, filtering by commission), which is clear context. It does not state when NOT to use it or name an alternative sibling, so no exclusions.

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, and openWorldHint=false, so the safety profile is covered. The description adds real behavioral context beyond that: it states what underlying field is aggregated and that omitting the commission defaults to the whole parliament. No permission or rate-limit detail is given, but none is needed for a local-dump read.

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 core operation is front-loaded in the first sentence and the example questions are tucked below it. The bulleted use cases partly restate the 'Useful to answer' line, so there is slight redundancy, but nothing is bloated for a tool of this complexity.

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 the description needn't explain the return shape, and it correctly focuses on intent and scope. Combined with the annotations it gives an agent enough to invoke the tool correctly; only the commission-identifier format is left to inference.

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 one parameter with 0% schema description coverage, so the description has to carry the load. It implies 'kommission' accepts a commission identifier and the examples (FK-N, WBK-N) hint at the accepted format, which is genuinely useful. However, it never states the format or matching rules explicitly and doesn't mention the maxLength constraint, so compensation is only partial.

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 gives a specific verb and resource: it aggregates verguetungstransparenz_beurteilung values and returns the distribution, with scope stated as 'all parliamentarians (or a commission subset)'. That is far more informative than the opaque German tool name. It does not, however, explicitly differentiate itself from siblings such as lobbywatch_get_ranking, so it stops 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?

Usage context is clear and concrete: 'Useful to answer: How transparent is the education commission...' plus three example questions covering whole-parliament, single-commission, and comparison scenarios. There is no explicit when-not guidance or named alternative (e.g. get_ranking), so it does not reach 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 and non-open-world semantics, so the safety profile is covered. The description adds genuine behavioral context beyond that: fuzzy-name misses return near-miss candidates in a 'suggestions' field (audit ARCH-003), and nur_aktiv drops mandates with an end date set. It does not mention rate limits or result ordering, but for a read-only list tool this is solid.

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?

Purpose and the fuzzy-miss caveat are front-loaded, then args, then use cases. Every section earns its place, though the use-case block repeats filter semantics already stated in Args, adding mild redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained, and the description covers the mutation-free behavior, the three parameters and the fuzzy-match edge case. Missing only the default of nur_aktiv and any note on result scope/ordering for a complete picture.

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 carry the parameter meaning, and it does: name_or_id is 'ID or name (fuzzy)', nur_hauptberuflich means only main-occupation mandates, nur_aktiv drops mandates with an end date set. The one gap is not noting that nur_aktiv defaults to true in the schema, which could surprise an agent expecting all mandates.

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 ('Return the list of interessenbindungen'), glosses the German term as conflicts of interest, and scopes it to 'one parliamentarian' with optional filters. This is clearly distinguishable from siblings like get_parlamentarier or get_lobbygruppe 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?

Three concrete use cases ('Which active mandates does Jositsch hold today?', 'full-time mandates only', 'every IB ever declared by #1') make the intended usage unambiguous and show how the filter flags combine. It stops short of naming an alternative tool or stating when not to use it, so it misses the top tier.

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=false, destructive=false, idempotent=true, and openWorld=true, so the safety profile is covered. The description adds genuinely useful context beyond that: progress reporting via MCP Context, the ~17 MB compressed payload size, and that it is long-running – helping an agent anticipate a slow call.

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 core action and its return are front-loaded in the first two sentences, with operational detail and use cases following. Nothing is padded; the quoted examples are compact and aid routing.

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 present, the description need not explain return values, and it correctly defers to it while noting 'returns the new cache status'. It covers behavior, size, and progress feedback; only the expected wall-clock duration or blocking behavior is left 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 there is nothing for the description to disambiguate; the empty schema is self-explanatory. Baseline 4 applies for a parameterless tool.

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 ('Force re-download of the weekly Lobbywatch dump') and adds the scoping detail that it is weekly and a full re-download. It does not explicitly differentiate from the sibling lobbywatch_dump_status, so an agent must infer that this mutates the cache while the sibling only reports it.

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 two quoted use cases ('data looks stale', 'new declaration — refresh and re-check') give clear triggering conditions. However, it names no alternatives (e.g. check lobbywatch_dump_status first to see if a refresh is even needed) and no when-not-to-use guidance.

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 and openWorldHint=false, yet the description still adds real behavioral context: the match is a case-insensitive substring over organisation names and the branche field where present, and the current 0.1 implementation cannot resolve branche ids via interessengruppe. That limitation disclosure materially affects result interpretation.

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 Args and Use cases sections are well front-loaded and earn their place, but the paragraph about version 0.1 vs. a planned 0.2 adds roadmap narrative that is tangential to calling the tool today. The definition is longer than it needs to be for three parameters.

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 described, and the description still notes the pair shape ({parlamentarier, ib}). Combined with the disclosed substring/version limitation and full parameter coverage, the agent has what it needs; the undefined 'ib' abbreviation and unstated default are minor 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?

With 0% schema description coverage the description carries the burden and largely succeeds: branche_query is documented with its matching fields and examples ('Verlag', 'Pharma'), kommission with a concrete abbreviation ('WBK-N'), and limit as the max number of pairs returned. It omits the limit default (25) and maxLength constraints, so it stops short of a 5.

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 gives a specific verb+resource: find parliamentarians whose interessenbindungen match a term across an organisation's name/alias/branche. It is clearly a search over mandate links rather than a plain parliamentarian lookup, but it never names a sibling (e.g., lobbywatch_list_interessenbindungen or lobbywatch_get_parlamentarier) to route the agent between 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?

Three concrete use cases establish the intended context (e.g., 'Which WBK-N members hold mandates in the publishing industry?'), and the kommission/branche combination is explained. However, there is no explicit when-not or alternative-tool guidance, so the agent must infer boundaries with the sibling list/search tools.

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