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

Discloses behavioral traits: defines outlier and streak thresholds, defaults to last 30 days and core metrics. However, does not describe output format or whether it modifies data. Without annotations, this is relatively transparent but incomplete.

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

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

Two short, front-loaded sentences with no redundancy. Every word adds value, defining behavior and default scope efficiently.

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 moderate complexity with 4 parameters and no output schema, the description covers anomaly types and defaults but omits return format, pagination, or error conditions. Sufficient for basic understanding but not 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 coverage is 0%, so description must compensate. It explains z (threshold), start/end (implied by time range), metrics (core vs customized). But does not explicitly describe each parameter's usage or constraints, leaving some ambiguity.

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?

Clearly states the tool detects outlier days and sustained streaks, defining specific criteria (z standard deviations and 5+ consecutive days). Distinguishes itself from siblings like baselines or get_activity by focusing on anomaly detection.

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?

Implies default usage with 'last 30 days of core wellness metrics' but provides no explicit guidance on when to use this tool versus alternatives (e.g., query_metrics, correlate). No when-not or alternative recommendations.

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 provided, but description implies a read-only check. Could be improved by explicitly stating no side effects, but otherwise clear.

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

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

Two concise sentences with front-loaded purpose, no unnecessary words.

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 tool with no parameters and no output schema, the description fully covers its function and recommended usage scenarios.

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?

No parameters, so baseline applies. Description adds value by explaining the purpose of the tool, making it useful beyond 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?

Description clearly states it checks existence of Garmin Connect tokens, distinguishing it from sibling tools like sync and sync_status.

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 advises use before sync or after an auth error, providing clear context for when to invoke this tool.

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 bears full weight. It explains the computation (mean and sd) and default window/metrics, but omits details like return format, data freshness, or edge cases (e.g., insufficient data).

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: two sentences that front-load the core concept and purpose, with no redundant words.

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?

Although the tool is simple, the description lacks details on output format (e.g., structure of the band) and does not cover potential errors or data requirements. Without annotations or output schema, more completeness would be beneficial.

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

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

Schema coverage is 0% with no parameter descriptions. The description compensates by listing default values for both parameters and specifying the default metrics list, adding significant meaning 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?

The description clearly states it computes mean +/- sd band per metric over a trailing window and is used to judge if today's value is normal for the user. It specifies default window and metrics, making the purpose unambiguous and distinct from siblings like 'anomalies'.

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 explicitly says to use it for judging normality and references default parameters. However, it does not mention when not to use this tool or alternatives like 'query_metrics' or 'anomalies'.

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?

Discloses default time range and parameter behaviors (lag_days, scan_lags). However, it does not explain how start/end parameters interact with the default, nor the effect of missing data or the exact output format.

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

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

Two concise sentences: first gives purpose and default, second explains key parameters. No wasted words, front-loaded with essential info.

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 core parameters but omits start/end, no output description, and no guidance on correlation method selection. With 6 parameters and no output schema, more detail is needed for full completeness.

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?

Adds meaning for metric_a, metric_b, lag_days, and scan_lags (e.g., lag explanation, scan range). But start and end parameters are not described, and schema coverage is 0%, so description must do more.

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 it computes Pearson/Spearman correlation between two metrics with a default 30-day window. It distinguishes from sibling tools like 'anomalies' or 'baselines' by focusing on correlation analysis.

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 guidance on when to use this tool versus siblings. Does not provide context on when correlation is appropriate or what prerequisites exist.

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 must cover behavior. It mentions default date range and that it finds missing days and sync errors, but lacks detail on return format, side effects, or limits.

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

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

Two concise sentences: first defines purpose, second provides usage guidance. No wasted words.

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?

Despite conciseness, the description lacks critical details (e.g., return format, how missing days are listed) and offers no compensation for missing schema and 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% and the description provides no information about the two parameters (start, end), leaving them completely unexplained.

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 it returns missing days per table and unresolved sync errors, which is specific and distinguishes from sibling tools like sync_status or get_day.

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 gives usage guidance ('Use to find holes worth re-syncing before drawing conclusions'), but does not explicitly state when not to use or list alternatives.

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 provided, the description carries full burden. It discloses that only summary fields are returned, not GPS or sample streams, which is useful behavioral information. However, it does not cover other traits like idempotency or rate limits.

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 extremely concise with two short sentences, front-loading key information (what it does and what it excludes). No filler words, making it efficient for an AI agent.

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 the tool has one parameter, no output schema, and no annotations, the description provides basic purpose and a behavioral caveat (no GPS/samples), but lacks details on the return structure or any other context needed for complete understanding.

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 description does not add meaning beyond the schema for the single parameter activity_id. It only mentions 'by id', and with 0% schema description coverage, more detail (e.g., how to obtain the ID, expected range) would be beneficial.

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 retrieves a full stored summary row for one activity by ID, and distinguishes itself from siblings like list_activities by specifying 'by id' and noting it excludes GPS/sample streams. It is specific and informative.

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 activity ID comes from list_activities, giving usage context, but does not explicitly state when to use this tool versus alternatives or when not to use it. More explicit guidance on prerequisites or exclusions would improve clarity.

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?

Describes data content but does not disclose behavioral traits (e.g., read-only nature, permissions, side effects). With no annotations, more detail would be beneficial.

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

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

Two sentences, no wasted words, front-loaded with purpose and followed by usage guidance.

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 main purpose and data categories, but lacks details on output format or date range constraints. Sufficient for a simple 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?

The description specifies the date format YYYY-MM-DD, which the schema lacks, adding value. Could be more explicit about required 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 provides a merged view of a single day with wellness, sleep, HRV, activities, and data-quality flags, distinguishing it from siblings like get_activity and list_activities.

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 explicit usage context ('Use for 'how was <date>' questions') but does not mention alternatives or when not to use.

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?

Without annotations, the description carries full behavioral burden. It discloses that existing API-sourced rows are never overwritten, which is a key safety trait. However, it does not mention error handling, validation, or what happens on success.

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

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

Two sentences, front-loaded with the primary action, and each sentence adds value. No redundancy or fluff.

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 one parameter, no output schema, and no annotations, the description covers core purpose and a key behavior. However, it lacks details on error handling, file size limits, and confirmation of success. It's functional but not exhaustive.

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

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

Schema coverage is 0%, so description must compensate. It adds meaning by specifying the folder contains .fit files and is a Garmin FIT bundle. However, it does not clarify path format or requirements, but for a single parameter this is adequate.

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 verb 'Import' and the resource 'manually exported Garmin wellness FIT bundle (folder of .fit files)'. It distinguishes from siblings like 'sync' by specifying manual export and zero-auth offline ingest.

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 when to use: when you have a manually exported Garmin FIT bundle. It doesn't explicitly exclude alternatives, but the context of manual ingest and zero-auth sets it apart from sibling tools like 'sync' and 'sync_status'.

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, description provides behavioral context: ordering (newest-first) and truncation signal ('truncated=true'). This adds value beyond the input schema, though it omits details like authentication or rate limits.

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

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

Two sentences, front-loaded with main action, no filler. Every sentence adds value: the first states purpose and filters, the second explains the truncated flag.

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 tool with 5 parameters and no output schema, description covers core behavior and filtering but lacks return format details (e.g., columns in the compact table). Still, it provides sufficient context for an AI agent to understand the tool's role.

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

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

Schema coverage is 0%, so description must explain parameters. It mentions filterable by type (with example 'running'), date range (implied start/end), and minimum distance, but does not explain the 'limit' parameter (default 20) nor date format. Partial coverage.

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?

Description clearly states 'List recent activities newest-first as a compact table', specifying the verb, resource, order, and format. This distinguishes it from siblings like get_activity (single activity) or query_metrics (metrics).

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?

Description implies usage for listing activities with filters but does not explicitly state when not to use it or mention alternatives. No guidance on when to use get_activity for a single activity or query_metrics for aggregated metrics.

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 burden. It discloses that stats adds summary statistics and hints at aggregation behavior, but lacks details on output format, pagination, or performance implications.

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

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

Two sentences, front-loaded with the core function, and each sentence adds value. No fluff.

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?

Without an output schema, the description partially explains returns ('columnar time series' and stats details). It could be more explicit about the structure, but given the tool's simplicity and sibling differentiation, it is reasonably 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 coverage is 0%, so the description adds meaning by explaining metrics (with examples), the role of start/end, the aggregate hint, and the stats flag. It does not cover all parameters fully, but provides significant context.

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 it is for retrieving columnar time series for specified metrics between dates, with examples. This differentiates it from siblings like get_activity or get_day, which are more specific.

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 a usage guideline: prefer weekly/monthly aggregate for ranges over ~60 days. However, it does not explicitly state when not to use or mention alternatives, but the context is clear enough.

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 must carry behavioral disclosure. It reveals the tool fetches data (presumably writes to local store), sets a 60-day limit, and defaults to last 30 days. However, it does not disclose write behavior (overwrite/merge), authentication requirements, or rate limits, leaving gaps.

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

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

Two sentences, no fluff. Action verb upfront, default behavior stated, and usage guidance included. Every sentence adds distinct 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?

The description lacks explanation of return format, error handling, and parameter details (especially 'endpoints'). For a tool with 3 undocumented parameters and no output schema, this is incomplete.

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 explain parameters. It only implies start/end via default behavior, and 'endpoints' is mentioned but not described. This is insufficient for the 3 parameters.

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 up to 60 days from Garmin Connect into the local store, with a default of last 30 days. It distinguishes from multi-month backfills via CLI, but sibling tools like list_activities or get_activity are not explicitly differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly guides use for catch-ups and advises against multi-month backfills by directing to the CLI. This provides clear when and when-not context, though alternatives among sibling tools are not 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 full burden. It describes the tool as read-only, showing status information. However, it does not disclose whether authentication is needed, or if the data is purely local or requires network access. The description is adequate but lacks detail on potential side effects or dependencies.

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 two sentences with no wasted words. It front-loads the key purpose and provides a concrete usage hint. Every sentence earns its place.

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

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

Given zero parameters and no output schema, the description covers the essential information: what is shown (coverage, time, errors) and when to use it. It could be more complete by specifying what tables are included or whether the output includes error details, but it is sufficient for basic understanding.

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 are no parameters, and schema coverage is 100% (trivially). The description does not need to add parameter info, and it appropriately focuses on the tool's behavior. Baseline 4 is justified.

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 shows local data coverage, last sync time, and pending sync errors. It also provides a usage hint about queryable date ranges. This distinguishes it from sibling tools like 'sync' (triggers sync) and 'gaps' (shows missing data), but could be more explicit about the tables covered.

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 explicitly says 'Use to see what date ranges are queryable,' which provides a specific use case. While it doesn't list when not to use it, the context of sibling tools (e.g., sync for triggering, get_activity for specific data) makes the usage relatively clear.

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