Clara — Personal Clean Air Planner
Server Details
UK personal air quality advice and daily exposure assessment. Pairs with Hermes for live data.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.2/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: contextual_advice provides personalized air quality advice based on context, while exposure_assessment estimates daily pollution exposure using time-weighted modelling. No overlap in functionality.
Both tool names follow a consistent noun_noun pattern with underscores: contextual_advice and exposure_assessment. The naming is predictable and clear.
With only two tools, the server feels somewhat sparse for a 'Personal Clean Air Planner'. While the tools are well-scoped, additional tools (e.g., for historical data or indoor air quality tips) could enhance utility without bloating.
The tool set covers personalized advice and exposure assessment, which are core to the server's purpose. However, it lacks a tool for fetching raw air quality data independently (relying on another server), and could benefit from a tool to manage user preferences or settings.
Available Tools
2 toolscontextual_adviceAInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| no2 | No | ||
| pm25 | No | ||
| setting | No | residential | |
| audience | No | general | |
| latitude | No | ||
| postcode | No | ||
| longitude | No | ||
| commute_mode | No | ||
| has_gas_cooker | No | ||
| has_indoor_sources | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns structured advice with a summary, health context, and intervention info. However, it omits behavioral details such as error handling (e.g., invalid postcode), dependencies (requires either postcode or lat/lng), and any potential side effects or limitations (e.g., UK-only).
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 well-structured with clear paragraphs: purpose, usage guidelines, composability, return value, and parameter list. It is front-loaded with the main purpose. While it is somewhat lengthy, each sentence adds value, and the structure aids readability.
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 absence of annotations and output schema, the description is fairly complete: it explains the tool's purpose, when to use, all parameters, and the structure of the response. It also mentions composability with another tool. Minor gaps include missing error behavior and explicit differentiation from the sibling 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?
With 0% schema description coverage, the description compensates by explaining each parameter: e.g., 'pm25: PM2.5 concentration in ug/m3. Overrides location-based estimate.' It also lists allowed values for 'setting', 'audience', and 'commute_mode'. This adds significant meaning beyond the raw schema, though it does not explain defaults or constraints like mutual exclusivity of location 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 identifies the tool's function: providing personalized air quality advice for a UK location and user context. It specifies numerous concrete scenarios (asthma, pregnancy, school-age child, etc.) and contrasts with generic handouts, effectively distinguishing it from the sibling tool 'exposure_assessment'.
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 states when to use this tool (when the question depends on who the user is or what they are doing) and gives examples. It also mentions composability with Hermes for live readings, providing integration context. However, it does not explicitly state when not to use it or how it differs from 'exposure_assessment' beyond the purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
exposure_assessmentAInspect
Estimate personal daily air pollution exposure across home, work, and commute.
Uses time-weighted modelling across environments: home (with indoor source adjustments), work, and commute (with route-based pollution and transport mode factors). Based on annual average pollution estimates, not live readings.
Args: home_postcode: Home location UK postcode (e.g. "SE17 1RL"). Required. work_postcode: Work/school location postcode. Omit if not commuting. transport_mode: Commute mode — walk, cycle, bus, car, train, tube. work_frequency: How often you commute — most_days, some_days, less_often, never. commute_hour: Hour of commute (0-23) for time-of-day pollution adjustment. cooker_type: Home cooker type — gas, electric, induction, unknown. smoking_at_home: Whether anyone smokes indoors (major PM2.5 source). tube_line: London Underground line for tube commuters (e.g. "victoria").
| Name | Required | Description | Default |
|---|---|---|---|
| tube_line | No | ||
| cooker_type | No | unknown | |
| commute_hour | No | ||
| home_postcode | Yes | ||
| work_postcode | No | ||
| transport_mode | No | walk | |
| work_frequency | No | most_days | |
| smoking_at_home | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the data source (annual averages, not live) and mentions smoking as a major PM2.5 source. It does not detail output format or authentication needs, but provides adequate transparency for a read-only estimation tool.
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?
Description is concise: a clear overview sentence followed by a bullet list of parameters with explanations. Every sentence is informative, no 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?
Despite no output schema, the description adequately explains the model and inputs. It does not describe the return format (e.g., numeric value, unit) or error conditions, but overall is complete for an estimation 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%, and the description provides detailed explanations for all 8 parameters, including example values, defaults, and context (e.g., home postcode required, smoking as major PM2.5 source). This adds significant value 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?
Description clearly states the tool estimates personal daily air pollution exposure across home, work, and commute using time-weighted modelling. The verb 'estimate' and resource 'personal daily air pollution exposure' are specific and distinct from the sibling tool 'contextual_advice'.
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 explains the tool is based on annual average pollution estimates, not live readings, implying it is for long-term exposure assessment. However, it does not explicitly state when to use this tool vs alternatives or provide exclusions.
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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