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Clara — Personal Clean Air Planner

contextual_advice

Personalised air quality advice for a UK location and a specific user context.

Use this tool whenever the user asks an air-quality question that depends on who they are or what they're about to do: e.g. asthma, pregnancy, school-age child, gas cooker at home, tube commute, outdoor exercise. It composes location-specific pollution with the user's personal context to produce evidence-based advice — far more targeted than a generic "high pollution day" handout.

Composable with Hermes: pass pm25/no2 from Hermes get_current_aq for advice based on live readings rather than annual average estimates.

Returns structured advice with a plain-English summary, health context, and local intervention information. Present the 'summary' to users first.

Args: postcode: UK postcode (e.g. "SE17 1RL"). Provides coords + LAEI pollution. latitude: Latitude for coordinate-based lookup. longitude: Longitude for coordinate-based lookup. pm25: PM2.5 concentration in ug/m3. Overrides location-based estimate. no2: NO2 concentration in ug/m3. Overrides location-based estimate. setting: Context — residential, school, workplace, outdoor_exercise, commute. has_gas_cooker: Whether the person has a gas cooker (affects indoor advice). commute_mode: If setting is commute — walk, cycle, bus, car, train, tube. has_indoor_sources: Indoor pollution sources (smoking, woodstove). audience: Target audience — general, children, elderly, respiratory, pregnant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
no2No
pm25No
settingNoresidential
audienceNogeneral
latitudeNo
postcodeNo
longitudeNo
commute_modeNo
has_gas_cookerNo
has_indoor_sourcesNo

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses output format ('returns structured advice with a plain-English summary, health context, and local intervention information') and provides a presentation directive ('Present the summary to users first'). It also mentions composability with Hermes. It does not detail edge cases or failure modes, but covers the main behavioral aspects.

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

Conciseness5/5

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

The description is well-structured and front-loaded. The opening sentence states the core purpose, followed by usage guidance, composition notes, and return-value info. The parameter list is thorough but each line is concise and informative. No wasted words.

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

Completeness4/5

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

Given the 10-parameter complexity and no output schema, the description covers the essential context: purpose, usage triggers, parameter meanings, and return format. It lacks explicit guidance on parameter combinations (e.g., postcode vs. lat/long, required at least one) but otherwise provides a complete picture.

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

Parameters5/5

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

The schema has 0% description coverage, but the description compensates richly by explaining every parameter: postcode, latitude/longitude, pm25/no2 overrides, setting, has_gas_cooker, commute_mode, has_indoor_sources, and audience. It also adds crucial context like 'provides coords + LAEI pollution' and 'affects indoor advice.'

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

Purpose5/5

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

The description clearly states the tool's purpose: 'Personalised air quality advice for a UK location and a specific user context.' It uses a specific verb and resource, and elaborates on composing location-specific pollution with user context. While it does not explicitly name sibling exposure_assessment, it differentiates by focusing on personalized, evidence-based advice rather than generic assessment.

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

Usage Guidelines4/5

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

The description provides explicit when-to-use guidance: 'Use this tool whenever the user asks an air-quality question that depends on *who they are* or *what they're about to do*' with clear examples. It does not offer explicit when-not-to-use or alternatives, but the context is so specific that exclusion is implied.

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

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TDQS

A4.1/5.0
Disambiguation4/5

The two tools have distinct purposes: one gives contextual advice, the other estimates daily exposure. Though they share some inputs, their outputs and use cases are clearly different. No real ambiguity, but they are related enough that an agent might need to think twice.

Naming Consistency4/5

Both tool names are descriptive noun phrases (adjective_noun) and follow a consistent pattern. They don't use a verb_noun structure, but the style is uniform and readable. Minor deviation from typical action-oriented naming, but internally consistent.

Tool Count3/5

With only 2 tools, the server feels thin for a 'Personal Clean Air Planner.' The scope is narrow but may be intentional. However, most planners would include at least a basic air quality lookup tool, making the count borderline low.

Completeness2/5

The tools cover advice and exposure estimation but lack a direct way to fetch current or forecast air quality data (contextual_advice references another server's get_current_aq). No tool for simple 'what's the air quality now?' or general health tips. Gaps will cause agent failures for common requests.

Resources