phab-arc-mcp
Provides tools for reviewing Phabricator revisions and creating draft inline comments, with context from linked Maniphest tasks.
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., "@phab-arc-mcpReview revision D35297"
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.
phab-arc-mcp
phab-arc-mcp is a small MCP stdio server for reviewing Phabricator revisions.
What This Server Exposes
The public MCP surface is intentionally small:
review-phabinline-comments-phab
Related MCP server: AI Code Review MCP Server
What review-phab Does
review-phab is the tool you call when you want an AI reviewer to review a Differential properly.
You give it a revision ID like D35297. It fetches the revision title, summary, changed files, raw diff, and the linked Maniphest task context, then packages all of that together with the review prompt the model should use.
From a user point of view, this means you do not have to manually open the revision, copy the diff, inspect related tasks, and explain the background before asking for a review. The tool gives the model the code changes and the surrounding context in one shot.
That makes reviews more useful because the model can judge the change against the actual task being solved, not just the raw code diff. It can use the overall repository context to reason about likely bugs, regressions, and edge cases, and it can also check whether the revision actually does what the linked task says it should do. In practice.
After that, inline-comments-phab can take the review findings and create draft inline comments directly on the Differential.
Important details:
comments are created as draft inlines, not published comments
Installation
Requirements
Node.js 18 or newer
npmaccess to a Phabricator instance with the Conduit API enabled
a Conduit API token for an account that can read the revisions and tasks you want to review
1. Clone the repository
git clone https://github.com/wxomi/node-chat.git
cd mcp-phab2. Install dependencies
npm install3. Build the server
npm run buildThis produces the MCP server entrypoint at dist/server.js.
4. Configure environment variables
Set these before starting the server or before wiring it into your MCP client:
PHAB_API_TOKENrequired
primary token variable used by the server
CONDUIT_TOKENoptional alias for
PHAB_API_TOKEN
PHAB_CONDUIT_TOKENoptional alias for
PHAB_API_TOKEN
PHAB_CONDUIT_URIoptional
default:
https://phab.instahyre.com/should point to the base URL of your Phabricator instance
PHAB_ARC_TIMEOUT_MSoptional
default:
30000request timeout in milliseconds
Example:
export PHAB_CONDUIT_URI="https://phabricator.example.com/"
export PHAB_API_TOKEN="api-xxxxxxxxxxxxxxxx"
export PHAB_ARC_TIMEOUT_MS="30000"If you do not set PHAB_CONDUIT_URI, the server will try https://phab.instahyre.com/.
5. Start the server manually (optional)
You usually do not need this step when using the server through an MCP client. In the normal setup, the MCP client launches the stdio server process for you using its mcpServers configuration.
Run the built server manually only if you want to verify that the binary starts correctly:
npm startFor local development without building on every change:
npm run devThe server uses stdio transport, so manual npm start is optional and is not a required installation step.
Setup In An MCP Client
Codex / MCP config using the built server
{
"mcpServers": {
"phab-arc-mcp": {
"command": "node",
"args": ["/absolute/path/to/phab-arc-mcp/dist/server.js"],
"env": {
"PHAB_CONDUIT_URI": "https://phab.instahyre.com/",
"PHAB_API_TOKEN": "api-xxxxxxxxxxxxxxxx"
}
}
}
}Setup Checklist
Use this to verify the installation quickly:
npm installcompletes successfullynpm run buildcreatesdist/server.jsyour MCP client is configured with
PHAB_API_TOKENPHAB_CONDUIT_URIpoints to the correct Phabricator base URLthe MCP client can see the
review-phabandinline-comments-phabtools
Example Usage
Step 1: get review context
Call review-phab with:
{
"revision_id": "D35297"
}Notes
This server talks to Phabricator over the Conduit HTTP API.
A valid API token is required for every tool call.
Comments created by
inline-comments-phabare draft inlines and are not published automatically.
Available Tools
2 toolsinline-comments-phabA
Creates draft inline comments on a Differential revision from review findings JSON (does not publish).
| Name | Required | Description | Default |
|---|---|---|---|
| findings | No | ||
| is_new_file | No | ||
| review_json | No | ||
| revision_id | Yes | ||
| max_comments | No | ||
| include_title | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| diffId | Yes | |
| results | Yes | |
| isNewFile | Yes | |
| revisionId | Yes | |
| changedFiles | Yes | |
| createdCount | Yes | |
| skippedCount | 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 key side effect of not publishing, which is important. But it does not mention other behavioral traits such as idempotency, permissions, rate limits, or how existing draft comments are handled.
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 immediately states the action, resource, and non-publishing behavior. There is no filler or redundant content, making it highly 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?
Given 6 parameters, 0% schema coverage, and no annotations, a single sentence is insufficient. The description only conveys the overall purpose and non-publication, but leaves essential operational details and parameter semantics unaddressed. The presence of an output schema reduces the need to explain return values, but too many gaps remain.
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%, so the description must compensate for missing parameter meaning. It only hints at 'review findings JSON', which loosely maps to review_json or findings, but leaves revision_id, max_comments, include_title, and is_new_file completely unexplained.
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 ('creates') and names the exact resource ('draft inline comments on a Differential revision') and the input type ('review findings JSON'). It also clarifies the non-publishing behavior, which clearly distinguishes it from the sibling tool 'review-phab'.
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 by stating that the tool creates draft comments and explicitly says it does not publish, which is a useful when-not signal. However, it does not explicitly name alternatives or describe scenarios when to prefer 'review-phab'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
review-phabA
Fetches the exact review prompt and revision context needed to review a Differential without MCP sampling.
| Name | Required | Description | Default |
|---|---|---|---|
| revision_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| next_action | Yes | |
| revision_id | Yes | |
| review_prompt | Yes | |
| review_summary | Yes | |
| revision_context | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does reveal that the tool fetches a review prompt and revision context, implying a read-only operation. However, it lacks details about permissions, rate limits, or any potential side effects, which is a moderate gap for a tool with no annotation support.
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 the action ('Fetches') and immediately states the resource being fetched. There is no extraneous information, making it highly 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 that an output schema exists and there is only one parameter, the description provides enough basic context for an agent to understand the tool's primary function. However, it omits usage guidance and details about error handling or authorization, which prevents a perfect score.
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%, meaning the description does not explain the revision_id parameter at all. While the parameter name is intuitive, the description adds no additional meaning beyond the schema. The tool would benefit from explaining what a revision_id is or where to find it.
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 fetches 'the exact review prompt and revision context needed to review a Differential' using a specific verb and resource. This distinguishes it from the sibling tool inline-comments-phab, which presumably handles comments rather than review context.
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. The description mentions 'without MCP sampling' but does not explain when a user would need this tool, what prerequisites exist, or when to use inline-comments-phab instead.
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.
2 tool updates
v0.1.0- First observed
inline-comments-phab - First observed
review-phab
TDQS
The two tools have clearly distinct purposes: one fetches review context, the other creates draft inline comments. There is no overlap in functionality, so an agent can easily select the right tool.
Tool names do not follow a consistent pattern. 'review-phab' is verb-noun, while 'inline-comments-phab' is noun-noun. The shared '-phab' suffix is a minor unifier but the lack of consistent verb usage makes naming unpredictable.
With only two tools, the server feels thin but not unreasonable for a narrowly focused review workflow. The count is at the borderline where it could benefit from additional operations, but it is not excessively sparse.
The tool surface has significant gaps. It creates draft comments but explicitly does not publish them, and there is no way to submit or finalize a review. This creates a dead end for the agent workflow, as the created comments cannot be acted upon fully.
Maintenance
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