Pawgo MCP Server
Provides real-time pet travel policy data for Air France, including cabin/cargo rules, breed restrictions, carrier dimensions, and fees.
Provides real-time pet travel policy data for Delta Air Lines, including cabin/cargo rules, breed restrictions, carrier dimensions, and fees.
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., "@Pawgo MCP ServerCan I fly with my dog on Delta?"
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.
Pawgo MCP Server
Real-time pet travel data for AI assistants. Connect Claude, ChatGPT, or any MCP-compatible AI to Pawgo's database of airline pet policies and country import requirements.
Pawgo tracks 130+ airlines and 100+ countries with 200+ requirement fields per entity — cabin/cargo rules, breed restrictions, carrier dimensions, vaccination requirements, quarantine rules, health certificates, fees, and more.
Why this exists
Pet travel information is scattered across airline PDFs, government websites, and outdated blog posts. Rules change constantly. Pet owners get conflicting answers and sometimes get turned away at the airport.
Pawgo's AI agent crawls official sources daily and structures everything into a single verified database. This MCP server makes that data available to any AI assistant, so when someone asks "Can I fly my French Bulldog on Delta?" — the AI gives an accurate, up-to-date answer.
Related MCP server: FlightTicketMCP
Quick start
Install
pip install pawgo-mcpClaude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"pawgo": {
"command": "pawgo-mcp",
"args": []
}
}
}Restart Claude Desktop. Ask: "What's Air France's pet policy?"
With API key (full data access)
{
"mcpServers": {
"pawgo": {
"command": "pawgo-mcp",
"args": [],
"env": {
"PAWGO_API_KEY": "your-key-here"
}
}
}
}Request a free API key at info.pawgo.fr/developers.
Tools
Tool | Description |
| Pet policy for a specific airline (cabin, cargo, breeds, fees) |
| Import/export requirements for a country (vaccines, quarantine, permits) |
| Compare airlines by criteria: cabin, cargo, brachycephalic, cheapest |
| Search countries: no_quarantine, easiest, titer_test |
| How many airlines and countries Pawgo tracks |
Examples
Once connected, ask your AI assistant:
"Can I fly with my dog on Delta?"
"What do I need to bring my cat to Japan?"
"Which airlines allow French Bulldogs?"
"What are the easiest countries for pet import?"
"Does Australia require a quarantine for dogs?"
How it works
This MCP server calls the Pawgo public API — no database access or credentials required. The API provides real-time data from Pawgo's continuously updated database.
For personalized trip computation — combining your specific pet, airline, origin country, destination country, and travel dates into a complete compliance checklist — visit www.pawgo.fr.
Environment variables
Variable | Default | Description |
|
| API base URL |
| (none) | API key for full data access |
|
| Transport: |
|
| Port for SSE transport |
API
This server uses the Pawgo public REST API. You can also call it directly:
# Get Air France pet policy
curl https://info.pawgo.fr/api/v1/airlines/AF
# Get Japan import requirements
curl https://info.pawgo.fr/api/v1/countries/JP
# See all endpoints
curl https://info.pawgo.fr/api/v1/openapi.jsonFull API docs: info.pawgo.fr/developers
Data coverage
130+ airlines — cabin, checked, cargo policies for dogs and cats
100+ countries — import/export requirements (microchip, rabies, titer test, health certificate, quarantine, import permit, breed restrictions)
Updated daily by Pawgo's AI crawling agent
Completeness scored — every record has a quality score (0-100%)
ChatGPT Custom GPT
Want to create a Pawgo GPT for ChatGPT? We've included the config:
Instructions:
chatgpt-gpt-instructions.mdOpenAPI Action schema:
openapi-action.yaml
Go to chat.openai.com/gpts/editor, paste the instructions and schema, add the Pawgo logo, and publish. (Requires ChatGPT Plus)
Poe Bot
Create a free Pawgo bot on Poe using the same instructions and OpenAPI schema above. No subscription required.
Links
Pawgo (trip planner): www.pawgo.fr
API docs: info.pawgo.fr/developers
Airline policies: info.pawgo.fr/airlines
Country requirements: info.pawgo.fr/countries
Compare: info.pawgo.fr/compare
License
MIT
Available Tools
5 toolscheck_airline_policyA
Check pet travel policy for a specific airline by IATA code (e.g., AA, DL, LH, AF).
Returns cabin and cargo policies, breed restrictions, carrier limits, and fees.
| Name | Required | Description | Default |
|---|---|---|---|
| airline_code | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It implicitly indicates a read-only query ("check") and lists return data, but does not explicitly state it is non-destructive or mention auth/rate limits. This is sufficient for a simple lookup 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?
Two concise sentences with no extraneous information. The first sentence states the action and parameter format; the second lists return values. Front-loaded 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 tool's simplicity (one parameter, output schema exists), the description fully covers its functionality and return values. No gaps remain for the intended use case.
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%, but the description compensates by explaining the parameter: an IATA code with examples (AA, DL, LH, AF). This adds meaning beyond the bare schema definition, though it could specify case sensitivity or format.
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 checks pet travel policies for a specific airline by IATA code, specifying return fields (cabin/cargo policies, breed restrictions, etc.). This distinguishes it from sibling tools like check_country_requirements or compare_airlines, which cover different 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 provides clear context: use when you need airline-specific pet policies. It does not explicitly discuss when not to use or mention alternatives, but the purpose is straightforward and the sibling tool names imply distinct functions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_country_requirementsA
Check pet import/export requirements for a country by ISO code (e.g., US, FR, JP, AU).
Returns vaccination, microchip, health certificate, quarantine, and permit requirements.
| Name | Required | Description | Default |
|---|---|---|---|
| country_code | Yes | ||
| direction | No | import |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden of behavioral disclosure. It states what the tool returns but does not mention side effects, authentication requirements, rate limits, or whether it modifies any state. The fact that it is a 'check' suggests a read-only operation, but this is not explicit.
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 long, with the first sentence stating the core purpose and example input, and the second enumerating the output types. Every word contributes clear information with no redundancy 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?
Given that an output schema exists, the description need not detail return values. It covers the main inputs and outputs well. However, it could be more complete by mentioning the optional direction parameter and its meaning, especially since the schema has no descriptions.
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 0% coverage (no parameter descriptions), so the description must add meaning. It provides examples for country_code (US, FR, JP, AU), which adds value. However, it does not explain the direction parameter (e.g., import vs. export) or its default value, leaving a gap in understanding.
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 ('Check pet import/export requirements'), specifies the input method ('by ISO code'), provides examples (US, FR, JP, AU), and lists the output types (vaccination, microchip, health certificate, quarantine, permit requirements). This distinguishes it from sibling tools like check_airline_policy and compare_airlines, which focus on different aspects.
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 checking pet import/export rules for a country, but does not explicitly state when to use this tool versus alternatives like check_airline_policy or search_countries. It lacks exclusion criteria or contextual cues that would help an agent decide between siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_airlinesA
Compare airlines by criteria: 'cabin' (allow cabin pets), 'cargo' (offer cargo), 'brachycephalic' (allow snub-nosed breeds), 'cheapest' (lowest fees).
Returns a ranked list of airlines matching the criteria.
| Name | Required | Description | Default |
|---|---|---|---|
| criteria | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the output as a ranked list but lacks details on permissions, rate limits, or edge cases (e.g., no matches). The behavior is minimally 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 two concise sentences: the first states the purpose and criteria, the second describes the output. No unnecessary words, information is 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?
Given the simplicity (one parameter, no nested objects, existing output schema) and the context of sibling tools, the description provides sufficient information for an agent to invoke the tool correctly, though it could mention that the output is a list of airline names.
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 0% description coverage and only a generic string type for 'criteria'. The description adds significant meaning by enumerating the allowed values and their definitions (e.g., 'cabin' means allow cabin pets), which helps the agent select appropriate inputs.
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 verb 'compare' and the resource 'airlines', specifying the exact criteria available ('cabin', 'cargo', 'brachycephalic', 'cheapest'), which distinguishes it from sibling tools like check_airline_policy.
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 the criteria for comparison, implying when to use this tool (when needing a ranked list based on specific pet-related features). However, it does not explicitly state when not to use it or mention alternative tools, but the context signals from sibling names provide some differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_coverageA
Get Pawgo's data coverage: how many airlines and countries are tracked.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers the behavior: a read-only operation to get coverage counts. It is clear and has no hidden side effects or disclaimers. Could mention if the data is real-time or cached, but not required for simplicity.
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?
One concise sentence with no wasted words. The key information is front-loaded and clear.
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 no parameters and an output schema, so the description is complete for its low complexity. It adequately informs the agent of the tool's purpose and return value.
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?
No parameters exist, so the description naturally adds meaning beyond the empty schema. It explains what the tool returns, fulfilling the role for parameter-less tools.
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 'Get' and clearly states the resource 'Pawgo's data coverage' and what it provides: counts of airlines and countries. It is distinct from sibling tools like 'check_airline_policy' which focus on specific items.
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 retrieving overall coverage statistics but does not explicitly state when to use it versus alternatives or when not to use it. Given siblings, more guidance would help.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_countriesA
Search countries by criteria: 'no_quarantine', 'easiest' (fewest requirements), 'titer_test' (require titer test).
Returns countries matching the criteria.
| Name | Required | Description | Default |
|---|---|---|---|
| criteria | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavior. It states it returns matching countries but lacks details on error handling, case sensitivity, or whether criteria values are exhaustive. Minimal beyond basic purpose.
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 with no redundancy. The first sentence immediately states the core function and criteria, meeting the front-loading principle.
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 search with one parameter and an output schema, the description is adequate but leaves ambiguity on criteria meaning (e.g., 'easiest' definition). Could more explicitly state the return is a list of country objects.
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 0%, but the description explains the criteria parameter by listing three meaningful values, adding significant context. However, it does not clarify if these are the only allowed values or provide format constraints.
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?
Clearly states the tool searches countries by specific criteria (no_quarantine, easiest, titer_test), differentiating it from sibling tools that check policies or compare airlines.
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 on when to use this vs sibling tools like check_country_requirements or compare_airlines. The description implies a general search use case but does not specify exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
5 tool updates
v1.0.0- First observed
check_airline_policy - First observed
check_country_requirements - First observed
compare_airlines - First observed
get_coverage - First observed
search_countries
TDQS
Each tool targets a distinct aspect: airline policy, country requirements, airline comparison, coverage info, and country search. No overlaps.
All tools follow a consistent verb_noun pattern with underscores, e.g., check_airline_policy, compare_airlines.
Five tools is well-scoped for a pet travel domain, covering essential queries without unnecessary bloat.
The set covers checking policies, requirements, comparisons, searching, and coverage data, leaving no obvious gaps for an information server.
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