Which integrations are available for this server?

Provides tools for managing Apache Airflow DAGs, DAG runs, task instances, and logs via the Airflow REST API.

How do I use Airflow MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Airflow MCP list the last 5 runs of DAG 'etl_pipeline'" 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.

Airflow MCP

by zamax14

Overview Schema Source Related Servers Score Discussions Admin

Python

Hybrid

Airflow MCP

A Model Context Protocol server that exposes Apache Airflow's REST API to AI agents (Claude Code, Claude Desktop, etc.), so they can inspect and operate on DAGs, DAG runs, task instances and logs.

The server is general-purpose: point it at any Airflow 2.x/3.x instance via AIRFLOW_BASE_URL. It has no knowledge of any specific project's DAGs, and does not edit DAG files or manage Airflow users/roles/pools/connections.

Tools

Read-only

Tool	Description	Parameters
`list_dags`	List DAGs, optionally filtered by tag or active state	`tags?`, `only_active?`, `limit?`, `offset?`
`get_dag`	Get metadata for a single DAG	`dag_id`
`list_dag_runs`	List DAG run history	`dag_id`, `state?`, `limit?`
`get_dag_run`	Get details for a single DAG run, including trigger conf	`dag_id`, `dag_run_id`
`list_task_instances`	List task instances and their state for a DAG run	`dag_id`, `dag_run_id`
`get_task_logs`	Get logs for a task instance, truncated to `AIRFLOW_LOG_MAX_LINES`	`dag_id`, `dag_run_id`, `task_id`, `try_number?`

Write (sensitive — annotated so MCP clients require confirmation)

Tool	Description	Parameters
`trigger_dag_run`	Trigger a new DAG run (destructive, non-idempotent)	`dag_id`, `conf?`, `logical_date?`
`pause_dag`	Pause a DAG (idempotent)	`dag_id`
`unpause_dag`	Resume a paused DAG (idempotent)	`dag_id`

Related MCP server: MCP Server Airflow Token

Requirements

Python >= 3.12
uv
An Apache Airflow instance reachable over HTTP, with a user that has at least Viewer role (Op if you need the write tools)

Least privilege

Use a dedicated Airflow user for this server, scoped to the minimum role it needs:

Viewer is enough if you only register the read-only tools.
Op is required if you also register trigger_dag_run, pause_dag or unpause_dag.
Never point this server at an Admin account unless you have a specific reason to.

Quickstart

./install.sh

Detects whether you have Docker or uv installed, sets up .env (from .env.example, if missing), builds the image or syncs dependencies accordingly, and prints the .mcp.json snippet to register the server.

Setup

uv sync --group dev
cp .env.example .env

Fill in .env with your Airflow instance's URL and credentials.

Docker

No local Python/uv needed — build once, run anywhere Docker runs:

docker build -t mcp-airflow .
cp .env.example .env  # fill in your Airflow credentials

Run in stdio mode (default, for MCP clients that spawn the process):

docker run -i --rm --env-file .env mcp-airflow

Run in streamable-http mode (standalone service, listens on :8000):

docker run --rm -p 8000:8000 --env-file .env -e MCP_TRANSPORT=streamable-http mcp-airflow

Registering with an MCP client

stdio (recommended for local development)

{
  "mcpServers": {
    "airflow": {
      "command": "uv",
      "args": ["run", "mcp-airflow"],
      "env": {
        "AIRFLOW_BASE_URL": "http://localhost:8080",
        "AIRFLOW_AUTH_MODE": "basic",
        "AIRFLOW_USERNAME": "airflow",
        "AIRFLOW_PASSWORD": "airflow"
      }
    }
  }
}

stdio via Docker

{
  "mcpServers": {
    "airflow": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "--env-file", ".env", "mcp-airflow"]
    }
  }
}

streamable-http (server running as a standalone process)

Start the server with MCP_TRANSPORT=streamable-http uv run mcp-airflow (or the Docker command above), then register:

{
  "mcpServers": {
    "airflow": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

Development

uv run pytest
uv run ruff check .
uv run mypy src

Available Tools

9 tools

get_dagC

Get metadata for a single Airflow DAG.

ParametersJSON Schema

Name	Required	Description	Default
`dag_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

C2.7/5.0

Behavior2/5

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

With no annotations, the description implies a read-only operation but does not explicitly state it is non-destructive, nor does it disclose any error behaviors, rate limits, or authorization needs. Minimal transparency.

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

Conciseness5/5

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

Single sentence, concise, with no superfluous text. Essential information is front-loaded.

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

Completeness2/5

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

Despite low complexity (one required param, output schema exists), the description fails to provide sufficient context for safe usage, such as authentication requirements, error handling, or expected output structure. Incomplete for an AI agent.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no meaning to the single parameter 'dag_id' beyond its name. The description does not explain how to find or format the dag_id.

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

Purpose4/5

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

The description clearly states the verb 'get' and resource 'metadata for a single Airflow DAG', distinguishing it from sibling 'list_dags' which lists multiple DAGs. However, it does not specify what kind of metadata is returned.

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

Usage Guidelines2/5

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

No guidance provided on when to use this tool versus alternatives like 'list_dags' or 'get_dag_run'. No mention of prerequisites or context.

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

get_dag_runC

Get details for a single DAG run, including its trigger conf.

ParametersJSON Schema

Name	Required	Description	Default
`dag_id`	Yes
`dag_run_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

C2.8/5.0

Behavior2/5

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

No annotations provided, so description must disclose behaviors. It only states the output includes details and trigger conf, but does not mention error handling (e.g., for missing run), idempotency, or that it's a read-only operation.

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

Conciseness4/5

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

Single sentence, efficient, no fluff. However, slightly too terse, missing important details that could be added without losing conciseness.

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

Completeness2/5

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

Given no schema descriptions and no annotations, the description should provide more context. Though output schema exists, the description does not mention required inputs or any usage constraints, leaving gaps.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no meaning to the two required parameters (dag_id, dag_run_id). Their purpose is only inferred from names.

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

Purpose5/5

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

The description clearly states it gets details for a single DAG run, including trigger conf. This distinguishes it from sibling tools like 'list_dag_runs' (which lists multiple) and 'get_dag' (which gets the DAG definition).

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

Usage Guidelines2/5

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

No guidance on when to use vs alternatives. Does not mention prerequisites, context, or that this tool is for retrieving a specific run after identifying its ID.

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

get_task_logsB

Get logs for a task instance, truncated to AIRFLOW_LOG_MAX_LINES.

ParametersJSON Schema

Name	Required	Description	Default
`dag_id`	Yes
`task_id`	Yes
`dag_run_id`	Yes
`try_number`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3/5.0

Behavior3/5

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

Discloses truncation to AIRFLOW_LOG_MAX_LINES, a key behavioral detail. However, no information about permissions, side effects, or error conditions.

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

Conciseness4/5

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

Single sentence is efficient, but could include structured details. No fluff, but under-specification for a tool with 4 parameters.

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

Completeness2/5

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

Despite having an output schema, the description is too minimal. It omits parameter explanations and usage context, leaving gaps for the agent.

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

Parameters1/5

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

Schema coverage is 0%, and the description does not explain any parameter. Parameter names are non-obvious (e.g., dag_run_id), requiring the agent to infer meaning.

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

Purpose5/5

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

The description clearly states it retrieves logs for a task instance and mentions truncation. It is specific and distinguishes from sibling tools which handle DAGs and runs.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives or when not to use it. No context about prerequisites or typical use cases.

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

list_dag_runsC

List DAG run history for a given DAG.

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No
`state`	No
`dag_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

C2.4/5.0

Behavior2/5

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

With no annotations, the description should disclose behavioral traits. It only says 'list history,' implying read-only, but does not mention pagination, default behavior, or any side effects. Minimal behavioral insight.

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

Conciseness3/5

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

The description is a single sentence, which is concise, but it lacks structure and important details. It earns its place but should be expanded to include param behavior.

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

Completeness2/5

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 and optional parameters, the description is incomplete. It does not explain return values, filtering, or pagination, leaving gaps for effective tool invocation.

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

Parameters1/5

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

Schema description coverage is 0%, and the tool description provides no additional meaning for the parameters (dag_id, limit, state). The agent receives only parameter names and types from the schema, which is insufficient for correct usage.

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

Purpose4/5

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

The description clearly states the tool lists DAG run history for a given DAG, specifying the verb and resource. It distinguishes from siblings like get_dag_run (which fetches a single run) and list_dags (which lists DAGs), though it does not explicitly mention these distinctions.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like get_dag_run or list_task_instances. The description lacks any contextual cues for appropriate selection.

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

list_dagsA

List Airflow DAGs, optionally filtered by tag or active state.

ParametersJSON Schema

Name	Required	Description	Default
`tags`	No
`limit`	No
`offset`	No
`only_active`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.5/5.0

Behavior2/5

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

No annotations exist, so description carries full burden. It only states the basic action without disclosing read-only nature, performance implications, pagination behavior, or potential rate limits.

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

Conciseness5/5

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

Extremely concise and front-loaded with the core action and optional filters. No wasted words.

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

Completeness3/5

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

Output schema exists, so return values are covered. However, missing context on pagination (limit/offset), sorting, and full filtering capabilities leaves gaps for effective use.

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

Parameters3/5

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

Schema coverage is 0%, so description must compensate. It mentions 'tag or active state' which maps to tags and only_active, but does not explain limit, offset, or parameter formats. Adds some value but insufficient.

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

Purpose5/5

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

The description clearly states it lists DAGs with optional filtering by tag or active state. It distinguishes from siblings like get_dag (single DAG) and list_dag_runs (runs).

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

Usage Guidelines3/5

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

No explicit guidance on when to use this vs alternatives. While implied, it does not mention that get_dag is for a specific DAG or that list_dag_runs is for runs.

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

list_task_instancesB

List task instances and their state for a given DAG run.

ParametersJSON Schema

Name	Required	Description	Default
`dag_id`	Yes
`dag_run_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.4/5.0

Behavior2/5

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

With no annotations, the description carries full burden but only states core function. It does not disclose behavioral traits such as read-only nature, pagination, rate limits, or any side effects. For a listing tool, this is insufficient.

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

Conciseness5/5

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

The description is a single focused sentence with no redundant information. It is appropriately short for a simple list operation.

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

Completeness3/5

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

Given the tool's simplicity and presence of output schema, the description is minimally adequate. However, the lack of annotations and parameter descriptions leaves gaps in behavioral understanding. Additional context on output format or state values would improve completeness.

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

Parameters2/5

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

Schema coverage is 0%, so description must compensate. It only loosely ties parameters to 'for a given DAG run', implying 'dag_id' and 'dag_run_id' but adds no specific detail beyond the schema titles. Minimal value added.

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

Purpose5/5

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

The description clearly states the verb 'list' and the resource 'task instances' with a specific scope 'for a given DAG run'. It distinctly separates this tool from siblings like 'list_dag_runs' (different resource) and 'get_task_logs' (different operation).

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

Usage Guidelines3/5

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

The description implies usage when needing task instances for a specific DAG run, but it does not provide explicit when/when-not guidance or mention alternatives like 'get_task_logs' for logs. The context from sibling tool names partially compensates.

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

pause_dagA

Idempotent

Pause a DAG. Reversible, but affects the shared scheduler.

ParametersJSON Schema

Name	Required	Description	Default
`dag_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already provide idempotentHint=true and destructiveHint=false. The description adds beyond that: it clarifies the operation is reversible and highlights that it affects the shared scheduler, which is a important behavioral trait not covered by annotations.

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

Conciseness5/5

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

The description is extremely concise: two sentences, no redundancy. Every word adds value, stating the core action, reversibility, and a key side effect.

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

Completeness4/5

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

For a simple tool with one required param and an output schema (not shown), the description covers the essential purpose and a key behavioral note. It does not explain return values, but those are covered by the output schema. Minor gap: no mention of prerequisites or error conditions.

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

Parameters2/5

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

Schema description coverage is 0% (the description does not mention the required 'dag_id' parameter). With low coverage, the description should compensate but fails to add any meaning or guidance about the parameter beyond what the schema provides.

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

Purpose5/5

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

The description clearly states the action ('Pause') and resource ('a DAG'), with the additional note of reversibility. It is distinct from sibling tools like unpause_dag (reverse operation) and trigger_dag_run (different action).

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

Usage Guidelines4/5

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

The description provides clear context ('Reversible, but affects the shared scheduler'), implying when to use it and noting a side effect. However, it lacks explicit when-not-to-use or direct comparison to siblings beyond the reversal hint.

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

trigger_dag_runB

Destructive

Trigger a new DAG run. Sensitive: creates a new, irreversible execution.

ParametersJSON Schema

Name	Required	Description	Default
`conf`	No
`dag_id`	Yes
`logical_date`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

B3.2/5.0

Behavior3/5

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

Annotations already set destructiveHint=true and readOnlyHint=false. The description adds 'irreversible execution,' which slightly amplifies the destructive nature but doesn't disclose new behavioral traits like auth requirements or potential failures. It adds marginal value 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.

Conciseness5/5

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

Two short sentences (12 words total) with no wasted text. The key purpose and sensitivity warning are front-loaded. Ideal conciseness.

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

Completeness3/5

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

While the description states the primary action and notes irreversibility, it lacks crucial context for a destructive tool: prerequisites (e.g., dag_id must reference an existing DAG), what triggers the output schema, or side effects on existing runs. The presence of an output schema mitigates some need, but more operational context would be beneficial.

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

Parameters1/5

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

The description does not mention any of the three parameters (dag_id, conf, logical_date). With 0% schema description coverage, the description fails to provide any semantic meaning for the parameters, leaving the agent to rely solely on the schema's bare definitions.

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

Purpose5/5

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

The description clearly states the verb 'Trigger' and resource 'a new DAG run', distinguishing it from sibling tools like get_dag_run (retrieve) and list_dag_runs (list). The additional note 'Sensitive: creates a new, irreversible execution' reinforces the action.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. Sibling tools are listed but not contrasted. For example, no mention that this is for starting a new run while get_dag_run is for retrieving an existing one.

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

unpause_dagB

Idempotent

Resume a paused DAG.

ParametersJSON Schema

Name	Required	Description	Default
`dag_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

B3/5.0

Behavior3/5

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

Annotations cover key aspects (modification, idempotency, non-destructive). The description adds no additional behavioral context beyond implying state change. It fails to explain what 'resume' means operationally (e.g., enabling scheduling, affecting next run). Adequate but minimal.

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

Conciseness4/5

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

The description is extremely concise (single sentence) and front-loaded with the key action. However, it is so brief that it sacrifices necessary detail for pure brevity. Still, it avoids verbosity.

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

Completeness2/5

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 still lacks completeness. It does not clarify the scope of 'resume' (e.g., does it only unpause or also trigger a run?), error handling, or prerequisites. The agent is left to guess operational semantics.

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

Parameters1/5

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

Schema coverage is 0%, yet the description does not mention the 'dag_id' parameter or its format. The description adds no value beyond the schema's structural definition, leaving the agent with no guidance on what to pass.

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

Purpose5/5

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

Description uses specific verb 'Resume' (synonymous with unpause) and clearly identifies the resource as a DAG. It directly contrasts with sibling tool 'pause_dag' and avoids ambiguity with 'trigger_dag_run'.

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

Usage Guidelines2/5

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 (e.g., when a DAG is already running vs paused), no prerequisites, and no mention of error scenarios or idempotency implications. The description is purely functional.

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.

9 tool updatesv0.1.0Jul 8, 2026
- First observedget_dag
- First observedget_dag_run
- First observedget_task_logs
- First observedlist_dag_runs
- First observedlist_dags
- First observedlist_task_instances
- First observedpause_dag
- First observedtrigger_dag_run
- First observedunpause_dag

Install Server

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TDQS

B3.4/5.0

Scored 2026-07-08 22:25 across 9 tools

Disambiguation5/5

Each tool targets a distinct entity or action: DAG metadata, DAG run details, task logs, listing operations, and state changes (pause, unpause, trigger). No overlapping purposes.

Naming Consistency5/5

All names follow a consistent verb_noun pattern with underscores (e.g., get_dag, list_dags, trigger_dag_run). Verbs are clear and nouns precisely identify the resource.

Tool Count5/5

9 tools is appropriate for an Airflow MCP server, covering core operations without redundancy. The count is well-scoped for the domain.

Completeness4/5

Covers essential DAG, DAG run, and task instance operations. Minor gaps like clearing task instances or updating DAG configuration are missing but not critical for typical monitoring and triggering workflows.

Maintenance

ActivityStale

ResponsivenessResponsive

Resources

GitHub Repository

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The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
Finance E-commerce & Retail App Automation
2

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Airflow MCP

Tools

Read-only

Write (sensitive — annotated so MCP clients require confirmation)

Requirements

Least privilege

Quickstart

Setup

Docker

Registering with an MCP client

stdio (recommended for local development)

stdio via Docker

streamable-http (server running as a standalone process)

Development

Available Tools

Output Schema

Output Schema

Output Schema

Output Schema

Output Schema

Output Schema

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

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astro-airflow-mcpofficial