Cycle Tracker

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

No annotations are present, so the description carries the full burden. It discloses live availability, sorting default, and that distance_km appears when coordinates are provided. This is valuable behavioral context, though it doesn't cover pagination 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, no redundant information.

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

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

For a simple tool with no output schema, this covers key behaviors: live availability, filtering, sorting, and conditional output. The limit parameter is documented in schema, so the description is adequate.

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 80%, so baseline is 3. The description adds nuance around lat/lon (including distance_km) and default sorting, but doesn't explain limit 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?

The description begins with 'Find bike stations in a city with live availability', stating a specific verb, resource, and scope. It further clarifies filtering by name and sorting by distance, which distinguishes it from sibling tools like get_popular_stations.

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 clearly states the tool finds stations and explains conditional behavior for lat/lon vs no coordinates. It doesn't explicitly name alternatives or when not to use, but gives enough context on usage.

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 full transparency burden. It discloses key behaviors: 10-minute snapshot intervals, newest-first ordering, and up to 90 days of history. However, it does not explicitly confirm the operation is read-only, define 'active stations,' or describe response format beyond 'time series.'

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 concise sentences, front-loading the primary purpose and then adding key behavioral details. Every word contributes without 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?

For a simple read-only tool with two parameters and no output schema, the description provides a solid shape of the response (time series of two metrics) and key constraints (resolution, ordering, retention). It lacks explicit return-field names and error scenarios, but for this complexity it is nearly 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 only 50%, but the description adds context by clarifying the city scope and the maximum history window (90 days), which helps interpret the hours parameter. It does not explicitly explain hours semantics beyond the schema's default/min/max, so the description partially compensates.

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 specifies the tool's function: it returns a time series of bike availability and active stations for a city, with a specific temporal resolution. This distinguishes it from sibling tools like find_stations (station search) and get_network_status (current 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?

The description implies usage for historical trend analysis but does not explicitly state when to use this tool over alternatives or provide exclusion criteria. It mentions the time-series nature and 90-day history, which signals suitability for trend queries, but lacks direct comparison with 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?

No annotations are provided, so the description must disclose behavioral traits. It adds the refresh interval ('Data refreshes every 10 minutes') which is a useful behavior. However, it does not explicitly state read-only semantics, error handling, or the exact return format. The listed fields provide some transparency, but the burden is only partially met.

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 a single, front-loaded sentence with no filler. It conveys the core purpose and key details (data fields and refresh rate) efficiently. Every word contributes value, making it highly concise and well-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 the tool's simplicity (one parameter, no output schema), the description covers the essential information: purpose, data fields returned, and refresh behavior. It lacks an explicit return format or error conditions, but the field list provides sufficient context for an agent to invoke the tool correctly.

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

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

The schema covers 100% of the parameter descriptions, with 'city' described as 'City id: one of manchester'. The tool description adds no additional parameter context, but since the schema provides complete documentation, the baseline of 3 is appropriate.

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's purpose: 'Live network totals for a city' and enumerates specific data fields (bikes available, e-bikes, active stations, bikes docked). This specific verb+resource combination distinguishes it from sibling tools like find_stations or get_availability_trend, which focus on station-level or trend data.

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 clear context for when to use the tool: when you need city-level network totals. It implies usage by listing the data fields, but does not explicitly mention alternatives or exclusion scenarios. This meets the 'clear context, no exclusions' level, though it stops short of naming sibling tools as 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?

No annotations are provided, so the description carries the transparency burden. It discloses the ranking metric (average bikes available) and the time window (last 24 hours), which is useful behavioral context. However, it does not explain what fields are returned, how stations with missing data are handled, or any pagination behavior, leaving some 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?

The description is a single, front-loaded sentence that directly states the tool's purpose. Every word contributes meaning, and there is 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?

There is no output schema, so the description is responsible for indicating return values. It does not specify whether the result is a list of station objects, includes additional metrics, or how they are ordered beyond 'ranked by average bikes.' This leaves the agent guessing about the response shape, which is a significant gap for a tool with no 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 only 50%; the 'city' parameter is described, but 'limit' has no text description. The tool description does not add meaning beyond the schema, nor does it clarify the 'limit' semantics beyond what its name and constraints (default 10, max 50) suggest. Since coverage is low, the description should compensate, but it fails to do so.

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 identifies the tool's purpose: listing the busiest stations in a city, ranked by average bike availability over the last 24 hours. It distinguishes itself from sibling tools by focusing on popularity ranking rather than generic station search or status. However, it is phrased as a noun phrase rather than an explicit verb+resource command, slightly reducing clarity.

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 the tool (when you need busiest station rankings) but does not explicitly state when to prefer this over siblings like find_stations or get_availability_trend. No alternatives or exclusions are mentioned, leaving the usage context only implied.

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 the full burden for behavioral disclosure. It clearly communicates this is a read/list operation and what the output contains (ids and coordinates). For a simple zero-parameter listing tool, this is adequate transparency, though it does not elaborate on edge cases or response structure.

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 purpose, scope, and key output fields without redundancy. Every word contributes useful 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?

Given the tool's simplicity (zero parameters, no output schema), the description is fully complete: it names the resource, what is returned (ids and coordinates), and why it is relevant to other tools. There are no significant gaps for an agent to invoke it correctly.

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

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

There are zero parameters, so the schema provides no semantic burden. The description adds valuable relationship context by explaining that the ids are used by every other tool, which is especially useful given the empty input 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 uses a specific verb ('List') and clearly identifies the resource: bike-share cities covered by Cycle Tracker, including their ids and map coordinates. It distinguishes itself from sibling tools (find_stations, get_network_status, etc.) by focusing on the city-level listing.

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 notes that the ids are 'used by every other tool', implying this should be called first to obtain necessary ids. This gives clear usage context, though it does not explicitly state when not to use it or name alternatives.

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