Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-pt Which fuel station has the cheapest diesel in Lisbon?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
[!IMPORTANT] This is an unofficial community project. It is not affiliated with the data providers (DGEG, IPMA, INE, GeoAPI.pt, AMA/dados.gov.pt). Respect each provider's terms — in particular, DGEG fuel data is for non-commercial use.
Ask your AI assistant things like:
"What's the cheapest diesel station in the Lisbon district?"
"Will it rain tomorrow in Porto? Are there active weather warnings?"
"Which locality has the postal code 1990-096?"
"How many inhabitants did Portugal have in the 2021 Census?"
"Search dados.gov.pt for datasets about the environment."
…and it will call the right tool, hit the public API, and answer with real data.
Related MCP server: IPMA MCP Server
Run it with npx (no install needed) — just point your MCP host at it.
Add to claude_desktop_config.json (~/Library/Application Support/Claude/ on macOS, %APPDATA%\Claude\ on Windows):
{
"mcpServers": {
"pt": {
"command": "npx",
"args": ["-y", "mcp-pt"]
}
}
}Add to ~/.cursor/mcp.json (or the project's .cursor/mcp.json):
{
"mcpServers": {
"pt": { "command": "npx", "args": ["-y", "mcp-pt"] }
}
}The server speaks MCP over stdio. Launch npx -y mcp-pt (or node dist/index.js from a clone) as the server command.
Tool | What it does | Source |
| Cheapest fuel stations in a district (petrol / diesel / LPG) | DGEG |
| 5-day forecast for a location | IPMA |
| Active meteorological warnings | IPMA |
| Look up a postal code | GeoAPI.pt |
| Coordinate → district / municipality / parish | GeoAPI.pt |
| Municipality details | GeoAPI.pt |
| Fetch an INE statistics indicator by | INE |
| Search datasets on dados.gov.pt | dados.gov.pt |
All tools call free, public APIs and need no API keys.
# List the tools over stdio
printf '%s\n' \
'{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"x","version":"0"}}}' \
'{"jsonrpc":"2.0","method":"notifications/initialized"}' \
'{"jsonrpc":"2.0","id":2,"method":"tools/list"}' | npx -y mcp-ptmcp-pt is the AI-facing layer over a family of standalone, multi-language SDKs
for the same data — precos-combustiveis-pt,
ipma-api,
geoapi-pt-sdk,
ine-pt and
dados-gov-pt. The server is
self-contained (it calls the APIs directly), so it has no dependency on those
packages.
npm install
npm test # vitest
npm run build # tsup → dist/index.js (executable)
node dist/index.jsNo. Every tool uses a free, public Portuguese API.
No — it's an independent community project. Please respect each provider's terms (DGEG fuel data is non-commercial).
Anything that speaks MCP over stdio — Claude Desktop, Cursor, Continue, and custom clients using the MCP SDK.
MIT © Marcelo Domingues
Find the cheapest fuel stations in a Portuguese district (data: DGEG). Use for questions about gasolina/gasóleo/GPL prices near a place.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many stations to return (default 10). | |
| district | Yes | District name, e.g. 'Lisboa', 'Porto', 'Faro' | |
| fuelType | No | One of: gasolina 95, gasolina 98, gasoleo, gpl. Default gasolina 95. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It does not mention read-only vs. write behavior, data freshness (DGEG data likely has update schedules), rate limits, or pagination (though no output schema is given). The description is too brief to adequately cover behavioral traits for a tool interacting with an external data source.
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, concise sentence that front-loads purpose and context (data source, use case). Every word adds value; there is no fluff. However, it could slightly improve by mentioning the optional parameters limit and fuelType, but overall it is 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 moderate complexity (3 parameters, no output schema), the description covers the basic purpose and data source. However, it misses completeness regarding return format (list of stations with prices? just names?) and any limits on results other than what is in the schema. Without output schema, the description should hint at what the agent will receive. It is adequate 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?
The input schema has full coverage (100%) with descriptions for all three parameters, including defaults and the list of fuel types. The description adds context by connecting the 'fuelType' parameter to Portuguese terms (gasolina, gasóleo) mentioned in the use case, which semantically clarifies the parameter. The description and schema together provide strong parameter semantics.
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: finding the cheapest fuel stations in a Portuguese district. It specifies the data source (DGEG) and the use case (questions about gasolina, gasóleo, GPL prices near a place). However, it could more directly differentiate from siblings like reverse_geocode or municipality, though the domain is distinct enough.
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 fuel price queries in Portugal, but does not explicitly state when not to use it or mention alternatives among siblings. The mention of 'data: DGEG' provides context, but there is no guidance on when other tools like open_data_search might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch an INE (Instituto Nacional de Estatística) statistics indicator by its 7-digit code (varcd). Pass dimension selectors (Dim1 is the period; use 'T' for a dimension's total). Example: varcd '0011609', dims { Dim1: 'S7A2021', Dim2: 'PT', Dim3: 'T', Dim4: 'T' }.
| Name | Required | Description | Default |
|---|---|---|---|
| dims | No | Dimension selectors, e.g. { Dim1: 'S7A2021', Dim2: 'PT' } | |
| lang | No | Response language (default PT) | |
| varcd | Yes | 7-digit INE indicator code |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavioral traits, but it only says 'fetch' (suggesting read-only) and explains the 'T' placeholder for totals. It does not cover error handling, response format, required permissions, rate limits, or any side effects, leaving significant gaps for an agent.
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 plus an example, with no redundant text. It is front-loaded with the core action and code format. The example could be slightly cleaner (nested braces), but overall it is 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 lack of an output schema and the tool's complexity (nested dims object, multiple parameters), the description is incomplete. It does not explain what the response contains (e.g., a single value, a series, metadata) or how errors are indicated. This forces the agent to guess the return structure.
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%, so baseline is 3. The description adds meaningful context beyond the schema: it clarifies that varcd is exactly 7 digits, explains that Dim1 is the period, and introduces the 'T' convention for dimension totals. This helps the agent correctly construct the dims object.
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 an INE statistics indicator using a 7-digit code (varcd) with dimension selectors. It specifies the exact resource and action, and the sibling tools (e.g., fuel_prices, weather_forecast) are in different domains, so no confusion arises.
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 an example of how to call the tool but does not explicitly state when to use it versus alternatives or when not to use it. The usage is implied by the domain, but there is no direct guidance on context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Details of a Portuguese municipality (concelho): NIF, area, contacts, postal code (data: GeoAPI.pt).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Municipality name, e.g. 'Lisboa', 'Sintra' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavioral traits. It mentions the data source (GeoAPI.pt) but omits side effects, authentication needs, error behavior, or whether the operation is read-only. With such minimal disclosure, the agent lacks critical context for safe invocation.
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 efficient sentence that conveys purpose and data source without extraneous words. It could be slightly more structured (e.g., listing fields explicitly), but overall it is concise and 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?
Without an output schema, the description should provide sufficient context about return values and behavior. It lists a few fields but is vague ('details') and does not cover error cases, completeness of fields, or format. The sibling context helps but does not compensate for the lack of explicit 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?
The input schema already provides a description for the sole parameter 'name' with examples. The description adds no additional meaning about format, constraints, or behavior, so it meets the baseline of 3 given 100% schema 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 identifies the resource (Portuguese municipality details) and lists specific data fields (NIF, area, contacts, postal code). While it doesn't explicitly differentiate from siblings, the resource and data points are distinct enough among the listed siblings to imply its purpose.
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 is provided on when to use this tool versus alternatives like postal_code or open_data_search. The description only states what it does, leaving the agent to infer usage context without any criteria for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the Portuguese open-data portal dados.gov.pt for datasets by keyword (data: dados.gov.pt / uData).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search terms, e.g. 'população', 'covid', 'ambiente' | |
| pageSize | No | How many datasets to return (default 5). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It discloses the tool searches a specific portal and mentions the keyword parameter, but does not detail return format, pagination, or rate limits. However, the clarity on resource and action is sufficient for a simple search 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?
The description is a single, concise sentence that packs significant information—resource, action, location, and keyword examples. It is front-loaded and earns its place with no filler. A slight improvement could include a brief note on return format.
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 low complexity (2 simple parameters, no output schema or nested objects), the description is largely adequate. It covers the main search behavior and parameter usage. It lacks details on expected output structure or pagination, but for a simple keyword search, this is acceptable.
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 100%, so baseline is 3. The description adds value by giving example query terms ('população', 'covid', 'ambiente') and explaining pageSize's purpose and default, going beyond the schema's basic description. This additional guidance helps the agent craft effective queries.
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 searches a specific open-data portal (dados.gov.pt) for datasets by keyword, with the key resource and action explicit. It distinguishes itself from sibling tools (e.g., fuel_prices, weather_forecast) which target different data sources and domains.
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 a clear search context and example keywords, implying usage for dataset discovery on the Portuguese portal. It does not explicitly state when not to use it or name alternatives, but the sibling tools (e.g., fuel_prices, ine_indicator) serve distinct purposes, making the context clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up a Portuguese postal code (código postal) — locality, municipality, district and streets (data: GeoAPI.pt).
| Name | Required | Description | Default |
|---|---|---|---|
| postalCode | Yes | Postal code, e.g. '1990-096' or '1990' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It only states the lookup nature and data source. It does not disclose whether the lookup is read-only, what happens if the postal code is invalid, rate limits, authentication needs, or the structure of the return value. The description is too minimal for a tool with no annotation safety net.
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 sentence that is front-loaded with the action and resource. Every word adds value, and there is no unnecessary repetition 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?
The tool has low complexity (one parameter, no nested objects) and no output schema. The description mentions the fields returned (locality, municipality, district, streets) but does not specify the output format, structure, or how to interpret the result. This is adequate but incomplete for an agent to reliably use the tool without further assumptions.
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 100% coverage with a description for the parameter. The tool description does not add any new semantic information about the parameter beyond what the schema already provides (e.g., example format). Following the rubric, 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 action ('Look up'), the resource ('Portuguese postal code'), and the specific data returned ('locality, municipality, district and streets'). It also identifies the data source (GeoAPI.pt). This clearly distinguishes it from sibling tools like fuel_prices or weather_forecast.
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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., valid postal code format), when not to use it, or how it compares to related tools like reverse_geocode or municipality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Reverse geocode a coordinate in Portugal → district, municipality and parish (freguesia) (data: GeoAPI.pt).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude | |
| lon | Yes | Longitude |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavioral traits. It reveals the data source (GeoAPI.pt) and output structure, but omits operational details like rate limits, cost, error handling (e.g., for coordinates outside Portugal), or idempotency. The core behavior is communicated, but safety and performance cues are absent.
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 focused sentence of 16 words. It packs the action, input scope, output types, and data source with no redundancy. Every word earns its place, meeting the highest standard of conciseness.
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 two-parameter tool with no output schema, the description adequately explains what the tool does and returns. It references the data source and geographic scope. It could mention coordinate system (WGS84), but given the tool's simplicity and the sibling context of Portugal-specific tools, it is sufficiently complete for selection.
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 basic descriptions ('Latitude', 'Longitude'), so baseline is 3. The description adds value by clarifying that the coordinate must be within Portugal and that the result yields specific administrative areas (district, municipality, parish), which is not evident from the schema alone.
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 action ('reverse geocode'), the input (coordinate), and the output (district, municipality, parish) with geographic scoping ('in Portugal'). This is specific and distinguishes it from most siblings (e.g., weather tools), though it does not explicitly contrast with the 'municipality' sibling which could retrieve similar area names by different means.
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 is given on when to use this tool versus alternatives like 'municipality' or 'postal_code'. The description implies use when you have lat/lon and need administrative divisions, but it does not state prerequisites, coordinate system assumptions, or when not to use it (e.g., for addresses or full postal codes).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
5-day weather forecast for a Portuguese location (data: IPMA). Give a city/locality name.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | Location name, e.g. 'Lisboa', 'Porto', 'Faro', 'Funchal' |
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 the time range (5-day) and data source (IPMA), but omits critical behavioral traits such as whether the forecast is read-only, what happens on invalid locations, data freshness, rate limits, or response format. The description is too minimal to ensure safe invocation.
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 of 16 words. It conveys the essential information without superfluous text. Every word 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 the tool's simplicity (one parameter, no output schema, no annotations), the description is too sparse. It does not explain the return value structure, error handling, or any constraints. An agent would lack enough context to interpret the forecast output or handle edge cases adequately.
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% for the single parameter 'location', so the baseline is 3. The description adds the phrase 'city/locality name', which slightly reinforces the schema's examples ('Lisboa', 'Porto'). This adds minimal extra meaning beyond the schema's existing description.
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 '5-day weather forecast for a Portuguese location' and specifies the data source (IPMA). It is a specific verb+resource combination. However, it does not explicitly differentiate from the sibling tool 'weather_warnings', leaving the agent to infer the distinction from the tool names alone.
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 a basic instruction ('Give a city/locality name') but provides no guidance on when to use this tool versus alternatives (e.g., weather_warnings for alerts, fuel_prices for unrelated data). It lacks explicit context about when to use or not use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Active meteorological warnings for Portugal (data: IPMA). Returns only non-green (active) warnings.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It reveals the data source (IPMA) and filtering (only non-green warnings), but omits behavioral traits like update frequency, rate limits, or whether the operation is read-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 a single sentence with no unnecessary words. It is concise and front-loaded with the core purpose.
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, no output schema, and no annotations, the description is fairly complete but lacks details on the return format (e.g., fields, structure). It hints at filtering but leaves the response shape ambiguous.
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 tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and it adds value by clarifying the output focus (active warnings).
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 returns active meteorological warnings for Portugal, sourced from IPMA. It distinguishes itself from the sibling 'weather_forecast' by focusing on warnings, not forecasts.
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 tool is for current active warnings (non-green) but does not explicitly state when to use it over alternatives like 'weather_forecast' or when not to use it. No mention of context or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0fuel_pricesine_indicatormunicipalityopen_data_searchpostal_codereverse_geocodeweather_forecastweather_warningsEach tool addresses a distinct domain (fuel prices, weather, postal codes, geocoding, municipalities, statistics, open data search) with no overlapping purposes, making it easy for an agent to select the correct tool.
All tool names follow a consistent snake_case pattern with descriptive noun_phrase or verb_noun structures (e.g., postal_code, reverse_geocode, open_data_search), ensuring predictability.
With 8 tools, the server provides a focused yet comprehensive coverage of Portuguese public data services, avoiding bloat or insufficiency for its intended scope.
The tool set covers major data categories for Portugal (energy, weather, geography, government, statistics, open data) with no critical gaps; common queries are well-served.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
IBGE: geography, census, economy and health from the official APIs, with provenance. 23 tools.
Real-world data for agents: air quality, geocoding, quakes, holidays, web search
15 media & data tools for AI agents: search, transcribe, subtitles, voiceover, translate & more.
Real-time data API for AI Agents: stocks, weather, forex, logistics, search, scrape, news, IP.
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