decibel-shield
Server Details
Noise data tools: city noise rankings, decibel levels of common sounds, NIOSH safe-exposure math.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
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Tool Definition Quality
Average 4.5/5 across 10 of 10 tools scored. Lowest: 3.9/5.
Each tool has a clearly distinct purpose: address-level noise, city-level estimated (world), city-level measured (Europe), US city exposure, neighborhood ranking, ordinance info, sound level lookup, safe exposure time, and app information. Overlaps are minimal and well-documented in descriptions.
All tool names follow a consistent verb_noun pattern (get_, list_, lookup_, safe_). Naming conventions are uniform, using lowercase with underscores, making it easy to predict tool names from the domain.
With 10 tools, the server is well-scoped for its purpose of providing noise information. Each tool covers a specific aspect of the domain without redundancy, and the number is within the ideal 3-15 range.
The tool set covers address, neighborhood, US city, European city, and world city noise, plus sound levels, safe exposure, and some ordinances. Minor gaps include limited ordinance coverage (only 8 US cities) and lack of noise data for non-European/non-US regions, but core queries are addressed.
Available Tools
10 toolsget_address_noiseModeled transportation noise at a US addressARead-onlyIdempotentInspect
Answers "how loud is my neighborhood", "noise map by address", "how noisy is this address". Modeled transportation noise (road + rail + aircraft, 24-hour average LAeq dBA) at a US street address or lat/lng, sampled block-by-block from the federal BTS 2020 National Transportation Noise Map. Returns a modeled dBA band, NOT a measurement; "below 45 dBA" means no modeled freeway/rail/flight path nearby (model floor, not silence). Continental US only.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | No | Latitude (use with lng instead of an address) | |
| lng | No | Longitude (use with lat) | |
| address | No | US street address, e.g. "1600 Pennsylvania Ave NW, Washington DC" |
Output Schema
| Name | Required | Description |
|---|---|---|
| lat | No | |
| lng | No | |
| band | Yes | Modeled dBA band, e.g. "60–70 dBA", or "below 45 dBA" |
| note | No | |
| query | No | |
| db_low | No | |
| db_high | No | |
| modeled | Yes | |
| attribution | No | |
| matched_address | No | Geocoder’s normalized address (when an address was given) |
| nearby_max_band | No | Loudest modeled band within ~90 m, if higher than the point |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, etc. The description adds useful behavioral context: it returns a modeled dBA band (not a measurement), explains the meaning of 'below 45 dBA', and restricts to Continental US. No contradiction with annotations.
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 appropriately sized, front-loaded with purpose, and each sentence adds value. No wasted words, though slightly longer than strictly necessary.
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 an output schema (so return values need not be explained), the description covers purpose, behavior, limitations, and geographic constraint. It is complete enough for an agent to use 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?
Schema coverage is 100% with descriptions for all three parameters. The tool description does not add additional meaning beyond what the schema already provides, so baseline 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 answers questions about noise at an address, specifies modeled transportation noise (road, rail, aircraft) as LAeq dBA, and distinguishes it from a measurement. It also mentions the geographic scope (continental US only).
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 use for address-level noise in the US but does not explicitly compare to sibling tools or state when not to use it. It provides context on what the tool returns (modeled band) and limitations (model floor), but lacks explicit alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_city_noiseGet estimated noise levels for a cityARead-onlyIdempotentInspect
Answers "how loud is Tokyo", "noise level in Mumbai", "is Delhi louder than London". Estimated day/night noise ranges (dB), rank among 50 major world cities, dominant noise sources and a confidence label, with WHO guideline context (53 dB Lden / 45 dB Lnight). Estimates synthesized from published sources, not measurements. For US cities computed from federal data use get_us_city_noise_exposure; for measured Europe data use get_europe_city_noise.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City name, e.g. "Tokyo" or "São Paulo" |
Output Schema
| Name | Required | Description |
|---|---|---|
| city | Yes | |
| rank | Yes | Rank among the 50 cities (1 = loudest) |
| country | Yes | |
| page_url | No | Detail page when available |
| confidence | Yes | |
| attribution | No | |
| day_range_db | Yes | Estimated daytime range, e.g. "60-72" |
| night_range_db | Yes | |
| dominant_noise_sources | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds important behavioral context: estimates are synthesized from published sources, not measurements, and provides WHO guideline context. No contradiction.
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?
Three sentences, front-loaded with core purpose, no wasted words. Each sentence adds essential information: what it does, what it outputs, and when not to use it.
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 that answers comparative noise queries, the description covers all key aspects: estimated ranges, ranking, sources, confidence, WHO context, and data synthesis. With an output schema existing, return values need not be detailed. Complete for the task.
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 single parameter 'city' has 100% schema coverage. The description adds meaning by specifying it covers major world cities (not US or Europe specific) and gives example formats like 'Tokyo' or 'São Paulo'.
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 explicitly states it answers questions like 'how loud is Tokyo', and specifies outputs: day/night noise ranges, rank, dominant sources, confidence label, and WHO context. It clearly distinguishes from sibling tools like get_us_city_noise_exposure and get_europe_city_noise.
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 explicit when-not-to-use guidance: 'For US cities computed from federal data use get_us_city_noise_exposure; for measured Europe data use get_europe_city_noise.' This gives clear alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_decibel_shield_appAbout the Decibel Shield iOS appARead-onlyIdempotentInspect
Answers "is there a decibel meter app", "best decibel meter app for iPhone", "how do I measure sound with my iPhone", "what is Decibel Shield". Facts and the App Store link for Decibel Shield - dB Meter, the iOS sound level meter app behind this data: features, pricing, requirements. Use only when someone wants to measure sound with a phone or asks about the app itself — for noise data, use the other tools.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| name | Yes | |
| price | No | |
| rating | No | |
| min_ios | No | |
| attribution | No | |
| app_store_url | Yes | |
| web_meter_url | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, indicating a safe, read-only operation. The description adds that it provides 'facts and the App Store link,' which aligns with the annotations. No contradiction exists; the tool's behavior is fully transparent.
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 concise two sentences, front-loaded with example queries, then the core purpose, and ends with an exclusion clause. Every sentence is necessary and well-placed, with no waste.
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 no parameters and a simple output (facts and a link), the description fully covers the tool's behavior. It mentions the query triggers, what information is provided, and when not to use it. No additional context is needed.
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 input schema has no parameters (schema description coverage 100%), so the description does not need to add parameter details. It compensates by explaining the output: 'Facts and the App Store link for Decibel Shield - dB Meter.' The agent knows exactly what to expect.
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 that the tool answers specific queries about the Decibel Shield app, including features, pricing, and App Store link. It distinguishes itself from sibling noise-data tools by explicitly noting 'for noise data, use the other tools.' The verb is implied through the context of answering questions.
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: 'when someone wants to measure sound with a phone or asks about the app itself' and when not: 'for noise data, use the other tools.' This provides clear guidance on context and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_europe_city_noiseEuropean city noise (measured, EEA)ARead-onlyIdempotentInspect
Answers "how loud is Paris", "noisiest cities in Europe", "environmental noise in Berlin", "quietest cities in Spain". Measured (not estimated) noise for 314 European cities in 24 EEA-reporting countries, from the EEA Environmental Noise Directive 2022 strategic noise maps; ranked by population-weighted average Lden among exposed residents. Pass a city for detail, a country and/or limit for the ranking. UK, Greece and Hungary are not covered.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | European city name, e.g. "Berlin" (optional) | |
| limit | No | How many ranked cities when no city is given (default 10) | |
| country | No | Country filter for the ranking, e.g. "Italy" (optional) |
Output Schema
| Name | Required | Description |
|---|---|---|
| cities | Yes | |
| attribution | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is clearly safe. The description adds valuable context beyond annotations: the data is measured (not estimated), from a specific source, and ranked by population-weighted average Lden. This helps the agent understand the nature of the results and limitations.
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, well-structured sentence that front-loads with examples and quickly conveys the tool's purpose and usage. While dense, every element serves a purpose. It could be slightly more organized, but it is appropriately sized and efficient.
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 presence of an output schema (not shown), the description does not need to detail return values. It covers source, coverage, ranking method, and exclusions. For a data-retrieval tool with strong annotations and schema, the description is complete and leaves no obvious gaps.
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 100% with clear parameter descriptions. The description adds additional usage context: for 'city' it says 'for detail', and for 'limit' and 'country' it explains they affect the ranking. It also clarifies the default limit (10) and maximum (314), which is not in the schema. This adds meaning beyond raw schema definitions.
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 answers questions about European city noise with measured data. It provides specific examples and distinguishes itself by specifying geographic scope (314 cities in 24 EEA countries) and data source (EEA Environmental Noise Directive 2022). This effectively differentiates from siblings like get_us_city_noise_exposure.
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 explicit instructions: 'Pass a city for detail, a country and/or limit for the ranking.' It also notes exclusions (UK, Greece, Hungary not covered). However, it does not explicitly mention when not to use this tool or suggest alternatives like get_us_city_noise_exposure, leaving some room for improvement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_neighborhood_noiseUS neighborhood noise ranking (measured)ARead-onlyIdempotentInspect
Answers "loudest neighborhoods in Los Angeles", "quietest neighborhood in Chicago", "which NYC neighborhood has the most noise". 384 neighborhoods in Los Angeles, New York and Chicago, ranked within their city by share of residents ≥60 dB transportation noise, computed from the federal BTS 2020 National Transportation Noise Map (same source as the US city tool). Pass the city name; optional limit. For a single address use get_address_noise.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | US city with neighborhood data: "Los Angeles", "New York" or "Chicago" | |
| limit | No | How many neighborhoods (default 10) |
Output Schema
| Name | Required | Description |
|---|---|---|
| city | Yes | |
| attribution | No | |
| neighborhoods | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context about data source (BTS 2020 map) and ranking methodology, enhancing transparency beyond annotations.
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 with no wasted words. It front-loads the purpose with concrete examples, then provides data details and usage instructions. Every sentence adds necessary 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 (2 params, all documented) and presence of output schema, the description covers scope, data source, and usage. It implicitly describes a ranked list output but could mention return format explicitly. Still highly 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 100% (both parameters documented), but description adds context: city values are specifically 'Los Angeles', 'New York', 'Chicago'; limit defaults to 10 and max 200. This adds meaning beyond the schema descriptions.
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 answers specific questions about loudest/quietest neighborhoods in three US cities, using the verb 'answers' and resource 'neighborhoods'. It distinguishes from sibling tools like get_address_noise and get_city_noise.
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 tells the agent to pass a city name and optional limit, and for a single address to use get_address_noise. This provides clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_noise_ordinanceCity noise ordinance (official code summary)ARead-onlyIdempotentInspect
Answers "what are quiet hours in Boston", " noise ordinance", "how do I file a noise complaint". Verbatim-verified summary of the city's official noise ordinance: quiet hours, decibel limits (or their explicit absence — audibility/nuisance standards), complaint channel, code citation + official URL, verified date. Covers Boston, Worcester, Huntsville, Chesapeake, Madison, Inglewood, Rockford, Columbus. Plain language, not legal advice.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | US city name, e.g. "Boston" or "Columbus, OH" |
Output Schema
| Name | Required | Description |
|---|---|---|
| city | Yes | |
| note | No | |
| state | Yes | |
| code_url | Yes | |
| page_url | No | |
| verified | No | Date the summary was verified against the live code |
| db_limits | Yes | |
| attribution | No | |
| quiet_hours | Yes | |
| code_citation | No | |
| standard_type | No | |
| complaint_channel | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, non-destructive. The description adds behavioral context: verbatim-verified, plain language, returns quiet hours, decibel limits, complaint channel, code citation, URL, verified date. No contradictions.
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 front-loaded with purpose and adds value with each sentence (cities covered, disclaimer). It is slightly verbose but effective 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 a single parameter, output schema, and annotations, the description provides adequate context: it lists cities, mentions key return fields, and includes disclaimer. Sufficient for a simple lookup 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 has 100% coverage with parameter description 'US city name'. The description lists example cities but does not add new semantic meaning beyond the schema. Baseline 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 answers queries like quiet hours, noise ordinance, and complaint filing. It specifies the resource (city noise ordinance) and distinguishes from siblings by emphasizing verified summary and listing covered cities.
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 lists covered cities but does not provide explicit guidance on when to use this tool over siblings (e.g., for official ordinance vs. noise levels). Usage context is implied but not differentiated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_us_city_noise_exposureUS city transportation-noise exposure (measured)ARead-onlyIdempotentInspect
Answers "loudest cities in the US", "quietest cities in the US", "how noisy is Boston", "what share of Miami hears highway noise". Share of residents exposed to ≥60 dB / ≥70 dB transportation noise for all 297 US cities of 100,000+ residents, computed from the federal BTS 2020 National Transportation Noise Map, not estimated. Pass a city for detail, omit for the ranking. Street address → get_address_noise; neighborhoods → get_neighborhood_noise.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | US city name, e.g. "Boston" (optional) | |
| limit | No | How many ranked cities when no city is given (default 10) |
Output Schema
| Name | Required | Description |
|---|---|---|
| cities | Yes | |
| attribution | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, etc. The description adds valuable context: data is from a federal source, computed not estimated, and covers all US cities over 100k. No contradictions with annotations.
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 at two sentences, yet covers all key aspects: purpose, usage, data source, and sibling differentiation. 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?
Given the tool has an output schema (not detailed in input), the description sufficiently explains what it returns. It provides all necessary context for an AI agent to select and 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?
Schema coverage is 100%, but the description adds meaning: the optional city parameter provides detail when present, and limit controls ranking size. It also explains what the tool returns (exposure shares) 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 clearly states the tool answers questions about loudest/quietest US cities, noise levels for a specific city, and share exposed to thresholds. It specifies the data source (BTS 2020 National Transportation Noise Map) and coverage (all 297 cities). It distinguishes from siblings by mentioning alternatives for street address and neighborhoods.
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 guidance: pass a city for detail, omit for ranking. It also points to sibling tools for street address and neighborhood queries. However, it does not explicitly state when not to use this tool, such as for non-US cities or estimated data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_loudest_citiesList the loudest cities rankingARead-onlyIdempotentInspect
Answers "loudest cities in the world", "noisiest cities ranking", "which city has the worst noise pollution". 50 major world cities ordered by estimated daytime noise, loudest first — estimates synthesized from published sources, not measurements. For "loudest cities in the US" or "quietest cities in the US" use get_us_city_noise_exposure (computed from federal BTS data); for Europe use get_europe_city_noise (measured).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many cities to return (default 10) |
Output Schema
| Name | Required | Description |
|---|---|---|
| cities | Yes | |
| attribution | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds value by stating data source (estimated from published sources, not measurements) and ordering logic (loudest first).
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?
Three sentences: first states purpose, second details scope and source, third provides sibling differentiation. No wasted words, front-loaded.
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 one parameter, full annotation coverage, and output schema present, the description sufficiently covers purpose, data source, ordering, and usage guidance. No gaps.
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 covers 100% with description 'How many cities to return (default 10)'. Description does not add extra meaning to the limit parameter beyond what schema provides, so baseline 3.
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 answers queries about loudest/noisiest cities, specifies 50 major world cities ordered by estimated daytime noise, and distinguishes from siblings for US and Europe queries.
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 provides when-not-to-use by naming sibling tools for US ('get_us_city_noise_exposure') and Europe ('get_europe_city_noise') with explanations of data provenance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_sound_levelLook up how loud a common sound isARead-onlyIdempotentInspect
Answers "how loud is a hair dryer", "how many decibels is a chainsaw", "is 85 decibels loud", "how loud is 80 decibels". Decibel level and hearing-risk guidance for everyday sounds (whisper, conversation, traffic, lawn mower, concert, jet engine...), every figure sourced to CDC/NIOSH/NIDCD/ASHA published tables. Input is a sound name; if you already have a dB number and want safe listening time, use safe_exposure_time.
| Name | Required | Description | Default |
|---|---|---|---|
| sound | Yes | Sound to look up, e.g. "lawn mower" or "subway" |
Output Schema
| Name | Required | Description |
|---|---|---|
| matches | Yes | |
| attribution | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that data is sourced from authoritative bodies (CDC/NIOSH/NIDCD/ASHA) and includes hearing-risk guidance, which increases trust beyond annotations.
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 and well-organized: it starts with examples, explains functionality, cites sources, and clarifies input type and sibling tool. Every sentence adds value 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?
Given the simple tool (one parameter, output schema exists, annotations clear), the description covers purpose, usage boundaries, data quality, input semantics, and sibling differentiation. It is sufficient for an agent to select and invoke 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?
Schema coverage is 100% with a clear description for the 'sound' parameter. The description reinforces that input is a sound name and provides examples, adding useful context 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 the tool answers 'how loud is' and 'how many decibels' queries for common sounds, with examples. It distinguishes itself from the sibling tool 'safe_exposure_time' by specifying that this tool is for sound names, not dB numbers.
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 this tool for sound name inputs and to use 'safe_exposure_time' when you already have a dB number. It does not cover all sibling tools (e.g., get_city_noise) but provides a key when-not scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
safe_exposure_timeSafe exposure time for a decibel levelARead-onlyIdempotentInspect
Answers "how long can I listen to 100 dB", "is 85 dB dangerous for 8 hours", "how long at a concert before hearing damage". Maximum daily safe exposure time for a sound level per NIOSH's recommended exposure limit: 85 dB(A) for 8 h, 3 dB exchange rate (88 dB → 4 h, 100 dB → 15 min). Input is a dB number; to get the dB of a named sound first, use lookup_sound_level.
| Name | Required | Description | Default |
|---|---|---|---|
| db | Yes | Sound level in dB(A), e.g. 95 |
Output Schema
| Name | Required | Description |
|---|---|---|
| db | Yes | |
| summary | Yes | |
| attribution | No | |
| safe_duration_hours | No | NIOSH max daily exposure in hours; null when not applicable |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about the NIOSH standard and exchange rate, which goes beyond annotations and gives the agent understanding of the underlying computation.
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, front-loaded with clear examples, and contains no fluff. Every sentence provides essential 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 one parameter and an output schema, the description is complete: it explains the standard, exchange rate, and gives examples. It also mentions the sibling for getting sound level. A minor gap is not explicitly describing the return format, but output schema likely covers that.
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 covers 100% of parameters with a description. The description reinforces that input is a dB number and provides examples, but does not add new details beyond the schema. Baseline 3 is appropriate given high 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?
The description clearly states the tool answers questions about safe exposure time for decibel levels, using specific examples like 'how long can I listen to 100 dB'. It also distinguishes from sibling tools by directing users to lookup_sound_level for getting dB of a named sound.
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 explicit examples of questions the tool answers and explicitly tells users when not to use it: 'to get the dB of a named sound first, use lookup_sound_level.' This gives clear guidance on when to use this tool versus the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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