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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds real behavioral substance: prefecture returns both obtained and un-obtained, jcc/gridsquare return only a worked list plus count, qsl_rcvd narrows judgement to QSL-received contacts, and include_worked_detail attaches counts and first/last dates.

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 the core effect in one line, then progressively layers kind-specific caveats and the argument list. Slightly long and the Args block partly restates schema fields, 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?

No output schema exists, and the description compensates by explaining what each kind returns (obtained/un-obtained vs list+count) and what include_worked_detail adds. Adequate for a 7-parameter read tool; only the concrete response shape per kind is left implicit.

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 carries the burden and largely meets it: it documents all seven parameters, including the kind enum values (prefecture/jcc/gridsquare), band/mode filters, date range format (YYYY-MM-DD), and the effect of qsl_rcvd and include_worked_detail. Minor gaps remain (e.g. accepted band/mode value forms).

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 (アワードの進捗を出す) and immediately scopes the return to obtained + un-obtained items. It further distinguishes its own behavior per kind (prefecture vs jcc/gridsquare), which lets an agent tell it apart from siblings like qso_stats or search_qso.

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 concrete usage conditions: prefecture kind reveals the missing 47 so it suits 'where to aim next', while jcc/gridsquare cannot be judged against a defined population and only return a worked list and count. Clear context but no explicit naming of sibling alternatives or when-not-to-use statements.

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

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

Annotations already declare readOnly/idempotent/non-destructive, so safety is covered; the description adds genuinely non-obvious behavior: it works without the DLL or HDB file present, and it will delegate to a 32-bit Python interpreter when one is supplied. It stops short of describing what the diagnostic reports or exit/failure signaling, so it is not fully complete.

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 the purpose, then the mechanics, then the routing rule. No filler, though the 'runs under 32-bit Python' detail is slightly redundant with the parameter 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 read-only diagnostic with no output schema, the description covers purpose, preconditions, the 32-bit path option, and the escalation trigger from import_from_hdb. The one gap is that an agent cannot tell what a successful vs failed diagnostic returns, which matters for a troubleshooting 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 0%, so the burden is on the description. It does explain the behavioural effect of supplying 32-bit Python (execution switches to that interpreter to exercise the whole path), but it never states the expected value format (a path to a Python executable) or what happens with a bad value.

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: a self-diagnostic of the HDB read path, with the concrete checks it performs (struct size validation, synthetic-record read test). It also names the failure scenario that selects it, which separates it from siblings like import_from_hdb and db_info.

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?

Explicitly routes the agent: 'when import_from_hdb fails, call this first to isolate the problem.' That is a concrete when-to-use condition tied to a named alternative, plus the precondition that it runs even without DLL or HDB present.

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?

The annotations already declare readOnlyHint, idempotentHint, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond the annotations by naming exactly what information will be returned and positioning the tool as an initial overview call. It does not mention permissions, rate limits, or response format, but given the annotation coverage this is a minor gap.

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 short and front-loaded: the first sentence states the action, the middle enumerates return contents, and the final sentence provides usage guidance. Every line earns its place with no redundancy or filler.

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

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

For a no-parameter, read-only information tool with rich annotations but no output schema, the description is complete enough: it lists the returned fields so an agent knows what data to expect and explains that calling it first gives an overview. No essential invocation detail 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 the baseline is 4. The empty schema means there is nothing for the description to compensate for, and the description does not need to explain parameter syntax or formats.

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: it returns the state of the log cache, and it enumerates the exact contents (QSO count, unique callsigns, date range, bands/modes, import source files and timestamps). This clearly distinguishes it as a metadata/overview tool rather than a search or import tool. However, it does not explicitly contrast itself with the sibling qso_stats, which may also provide counts and statistics, so sibling differentiation is implicit at best.

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 final sentence explicitly says to call this tool first to grasp the overall picture, which is clear context for when to use it. It does not, however, state when not to use it or name alternative tools for more detailed queries, so it stops short of full when/when-not 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 readOnly/idempotent/non-destructive, but the description adds genuinely useful environment context: HAMLOG itself is never modified, and the server must run as the same user at the same privilege level as HAMLOG. It does not describe what happens when HAMLOG is not running or how stale the window data might be.

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 is read and the read-only guarantee, then the use case, then prerequisites — a sensible ordering. Slightly more lines than strictly necessary, but every sentence carries distinct 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 what is returned at a conceptual level (callsign, report, frequency) and all prerequisites, which is adequate given there is no output schema and no parameters. The absence of any failure-mode or staleness note is the only 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a no-param tool 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 — reads the contents of the Turbo HAMLOG input window — and explicitly scopes it as read-only (読み取りのみ). The 'running input window' focus distinguishes it from data-oriented siblings like search_qso and qso_stats, though it never names an alternative.

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 a concrete use case: as a starting point during a QSO to look up when you last worked a station. It also states clear preconditions (Windows, HAMLOG 5.27c+, same user/permission level as the server). It stops short of naming the sibling that performs the follow-up lookup.

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?

Beyond the annotations, the description discloses the 32-bit subprocess isolation architecture, that write APIs are unbound (source data is never mutated), env-var fallbacks for all path args, and that use_copy is a guard against HamlogOpen generating index files in the original folder. That is substantial behavioral context; only the return/shape of the cache update is left unexplained.

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 is front-loaded in the first sentence, followed by value proposition and architectural rationale. It is a little long, but the subprocess and use_copy paragraphs explain non-obvious behavior that an agent could not get elsewhere, so most sentences earn their 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 an import tool with no output schema and five loosely-typed parameters, the description is nearly complete on inputs and runtime behavior. The remaining gap is what the cache update returns or how success/failure is surfaced, which matters for an import operation.

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 carries full documentation burden and does: it explains each of the five parameters, their env-var defaults (HAMLOG_HDB/DLL/PY32), the default and rationale for use_copy, the test-only nature of limit, and when py32_path is unnecessary.

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

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

The description states a precise verb+resource: reading HAMLOG.HDB directly through HAMLOG50.DLL and updating the cache, with the ADIF-free benefit made explicit. It is easy to tell apart from layout/config siblings like check_hdb_layout or get_hamlog_input, but it never names the closest sibling alternative (import_log) to disambiguate the import paths.

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 is implied rather than stated: 'without the hassle of ADIF export' hints at when this beats an export-and-import flow, and notes it is safe while HAMLOG is running. There is no explicit when-to-use / when-not-to-use guidance and no routing to import_log or other siblings, so the agent must infer the choice.

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?

The description states that replace=True (the schema default) discards the existing cache and re-inserts it, i.e. a non-additive update that removes data. That directly contradicts the annotation destructiveHint=false, which asserts only additive updates are performed. The rest of the disclosure (source read-only, writes confined to the local SQLite, best-effort CSV parsing) is genuinely useful, but the replace semantics conflict with the declared destructive profile.

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 purpose and the read-only/write-scope caveat are front-loaded in two short sentences, followed by a compact Args block covering all three parameters. Every line carries information; the only mild redundancy is restating the write scope already implied by the first sentence.

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 local file-import tool with no output schema, the description covers source format support, the safety boundary (HAMLOG untouched), and every parameter. It omits what an agent would still want to know about results and failure modes — whether duplicate QSOs are merged or appended, what happens on a malformed file, and what the import reports back — which keeps it short of fully complete.

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 carries the full burden and does: path (file path, defaulting to the HAMLOG_EXPORT environment variable), replace (True = discard and rebuild the cache, False = append), and csv_columns (explicit column-name list when the CSV column order differs from the default). Each parameter's meaning and default behaviour is stated, adding real value beyond the bare schema.

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

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

The description names a specific verb and resource (read a HAMLOG-exported log file and update the local cache) and immediately scopes what is and is not touched: HAMLOG data is read-only, only the tool's own SQLite is written. That scope statement also separates it from siblings such as import_from_hdb or check_hdb_layout, which deal with HDB sources rather than HAMLOG exports.

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 input-format guidance (ADIF preferred, HAMLOG CSV best-effort) and the fallback behaviour of the path argument, which implies when the tool is applicable. However, it never states when to prefer this tool over the sibling import_from_hdb / get_hamlog_input / check_hdb_layout, nor any precondition (e.g. that an export must already exist). Usage is implied rather than routed.

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

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds a genuine behavioral caveat: the 'hour' axis depends on whether stored log times are JST or UTC, which the agent could not infer elsewhere. It does not describe the return shape, but no output schema exists and the axis list partially compensates.

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 the purpose in one short sentence, then a compact bulleted list of group_by meanings. Minimal waste, though the parenthetical about award_progress and the JST/UTC aside slightly interrupt the list flow.

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 an 11-parameter tool with no schema descriptions and no output schema, the description covers group_by well and delegates filters to search_qso, but never explains the top limit, default behavior, or output format. Adequate but not fully sufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 11 parameters, so the description must carry the load. It fully explains group_by values and delegates the filter parameters ('same as search_qso'), which is useful but indirect. Core parameters like top (result limit), date_from/date_to and qsl_rcvd receive no direct explanation, leaving real gaps.

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 ('集計する' on 交信数) and enumerates the aggregation axes, so the agent knows exactly what the tool produces. It also differentiates from siblings by naming search_qso (shared filters) and award_progress (better for reading prefecture names).

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 implies the aggregation use case and explicitly routes one scenario (prefecture names) to award_progress. However, it never states when NOT to use this versus search_qso, only that they share filter semantics, so the boundary is left partly implicit.

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 a safe (readOnly/idempotent/non-destructive) read, so the safety burden is offloaded. The description adds genuine behavioral context: default limit of 50, total-count reporting via a 'total' field, and offset-based paging. Return-field structure beyond 'total' and the query cost remain unspecified.

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 is front-loaded in one line, then a clean Args list where every entry adds non-redundant meaning. No filler; each line 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 12-parameter tool with no output schema and 0% schema coverage, the description documents every input with examples and defaults, plus pagination and the 'total' return hint. It is essentially complete for correct invocation; only the shape of returned records is left implicit, which is acceptable absent 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 0%, so the description bears the full documentation burden and does so well: wildcard syntax (`JA1*`, * ?), date format and inclusivity (YYYY-MM-DD, inclusive), case-insensitive mode, JCC/JCG prefix example ('09' -> Nagano), qsl_rcvd boolean meaning, partial-match semantics for name/QTH, and limit/offset/order 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?

The description states a specific verb and resource: searching QSO (contact) records with filters. This clearly distinguishes a record-search tool from aggregation siblings like qso_stats and award_progress. It does not explicitly name those siblings as alternatives, 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 is implied by the search semantics, and the description usefully points to db_info for discovering valid band values. However it gives no explicit when-to-use/when-not guidance or direct comparison to the sibling aggregation tools. Cross-referencing db_info is a partial, not complete, routing signal.

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

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

Annotations already establish the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds useful behavior beyond that: the exact-match semantics of the lookup and the full shape of the returned data, including that per-contact detail is capped by limit.

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 output contents and use case are front-loaded before the Args block, and every sentence carries information. It is slightly verbose in listing returned fields, but nothing is wasted.

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, the description compensates by enumerating the returned data; with annotations covering safety, the remaining needs are met. A brief note on ordering or how limit interacts with the summary fields would make it fully complete.

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 parameters, and it does: callsign is described as an exact-match lookup (a meaningful constraint), and limit as the cap on returned detail rows. Both params get semantics the bare schema does not provide.

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 ('returns the contact history with a specific station') and enumerates the concrete fields returned (first/last contact date, bands, modes, name, QTH, per-contact detail), so an agent knows exactly what this produces. It does not name sibling tools like search_qso or qso_stats to differentiate scope, which keeps it 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 a clear use scenario ('intended for confirming the other party's history during a QSO'), which tells an agent when to reach for it. It stops short of naming the alternative tools or stating when NOT to use it, so it is clear context rather than explicit routing.

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