A
licenseNot graded
qualityD
maintenanceA Model Context Protocol server that enables AI assistants like Claude to interact with GitHub repositories, issues, and pull requests.
20 npm
MIT
WordPress Trac MCP Server
Officialby WordPress
This server provides read-only access to WordPress.org Trac instances via MCP tools.
Search Trac tickets by keywords, ticket numbers, or structured filters (e.g., status, component, milestone, custom field expressions).
Retrieve full ticket details, including description, metadata, attachments, changesets, human discussion, and linked pull requests.
Fetch changeset/commit information, including the commit message, author, and an optional truncated diff.
View recent Trac timeline activity, including tickets and commits, with date-range and author filtering.
List Trac metadata such as components, milestones, priorities, severities, types, and statuses.
Connect to different Trac instances (Core, Meta, Themes, Plugins, bbPress, BuddyPress, GlotPress, GSOC) via distinct endpoints.
Use a ChatGPT-compatible search/fetch endpoint for simplified ticket and changeset access.
Enables deployment of the MCP server on Cloudflare Workers for global edge distribution of the WordPress Trac data service.
Supports ChatGPT's Deep Research feature with a simplified interface for searching WordPress Trac data and fetching detailed information about tickets and changesets.
Transforms WordPress.org Trac into an AI-accessible knowledge base, providing comprehensive access to WordPress tickets, code changes, and development activity.
Click on "Deploy 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., "@WordPress Trac MCP Serversearch for recent open tickets about the block editor"
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.
WordPress Trac MCP server
A read-only Model Context Protocol server for the WordPress.org Trac instances, starting with WordPress Core Trac. It runs as a Cloudflare Worker and uses Trac's public HTML, CSV, RSS, and diff endpoints.
Live servers:
Production:
Standard MCP:
https://wordpress-trac-mcp-server-prod.a8c-aiops.workers.dev/mcpSearch/fetch compatibility:
https://wordpress-trac-mcp-server-prod.a8c-aiops.workers.dev/mcp/chatgptHealth check:
https://wordpress-trac-mcp-server-prod.a8c-aiops.workers.dev/health
Staging:
Standard MCP:
https://mcp-server-wporg-trac-staging.a8c-aiops.workers.dev/mcpSearch/fetch compatibility:
https://mcp-server-wporg-trac-staging.a8c-aiops.workers.dev/mcp/chatgptHealth check:
https://mcp-server-wporg-trac-staging.a8c-aiops.workers.dev/health
The former staging deployment at https://mcp-server-wporg-trac-staging.a8cai.workers.dev is
deprecated and runs older code. Its a8cai.workers.dev subdomain differs from the active staging
deployment's a8c-aiops.workers.dev subdomain.
Trac instances
Each Trac instance has its own endpoint. /mcp and /mcp/chatgpt serve Core.
Trac | Standard MCP | Search/fetch compatibility |
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The table is a discovery aid rather than an allowlist. Any <slug>.trac.wordpress.org resolves at
/mcp/<slug>, so a Trac added later needs no change here. An instance is bound to the connection
rather than chosen per tool call, so a client cannot read the wrong Trac by mistake.
Instances configure different fields. Themes has no components, and only some instances have
severities. getTracInfo reports a field the instance does not configure as unavailable instead of
failing. Ticket fields behave the same way: focuses exists only on Core and comes back empty
elsewhere.
Filtering is stricter, because Trac answers a filter on a field it does not configure with the
unfiltered result set rather than an error, and that reads as a real match count. searchTickets
rejects such a filter and names the fields the instance does have. This covers both the separate
arguments and the expressions inside query.
Connect to one instance per client entry. Use several entries to read several Tracs.
Related MCP server: WordPress MCP
Tools
The standard /mcp endpoint provides:
Tool | Purpose |
| Search by keywords, ticket number, or structured filters |
| Read a ticket, its attachments, changesets, human discussion, and linked pull requests. |
| Read a changeset and an optional truncated diff |
| Read Trac activity for recent days or a historical date range, with author filtering and day-granular coverage |
| List components, milestones, priorities, severities, types, or statuses |
getTicket leaves bot comments, cc-only changes, and entries with neither a change nor text out of
comments and lists each one under omittedComments with its ID, author, and reason (bot, cc,
or empty), so a gap in the comment numbering is explained rather than mistaken for truncation.
Every other field change a person makes is reported in changes with its values, including keyword
edits and description edits with their diff link.
getChangeset expects the numeric revision argument, not rev:
{
"revision": 58504,
"includeDiff": false
}The /mcp/chatgpt compatibility endpoint provides search and fetch. Use a bare number for a
ticket and an r prefix for a changeset: 65739 and r58504.
No tool takes a Trac instance argument. The endpoint you connect to decides which Trac the tools read.
Ticket and changeset text is plain text with one exception: links are kept as <a href="...">
with an absolute URL, because a comment that points at a pull request or another ticket loses its
point without one. Relative Trac links resolve against the instance you connected to.
Search filters
searchTickets accepts plain keywords, ticket numbers, or filter expressions joined with &.
Plain keywords match the ticket summary only; use description~=text to search ticket bodies.
{
"query": "milestone=6.9&status=closed&resolution=fixed",
"limit": 50,
"page": 2
}Expressions can name summary, description, owner, reporter, type, status, priority,
milestone, component, version, severity, resolution, keywords, cc, or focuses, and
take four operators: = exact, ~= contains, != not equal, and !~= does not contain. Repeat
a field to OR its values (status=new&status=assigned), with the same operator each time. Add order=<column> and desc=1 to
sort, for example component=Editor&status!=closed&order=changetime&desc=1. Sortable columns are
the fields above plus time and changetime. Field values differ by instance: getTracInfo
lists the complete components, milestones, priorities, severities, types, and statuses each one configures, in Trac's order. Milestones include open and closed groups as one flat list.
It also accepts status, component, milestone, and resolution as separate arguments. Each
is an exact match on one value and overrides the same field in query; the expression form is
the one to use for substring, OR, or negation. Results include pagination metadata.
Timeline ranges, authors, and coverage
getTimeline reads the last days days (default 7, max 30) or an explicit from/to date range. Historical dates start at 2005-01-01, the start of the verified WordPress Core Trac timeline (younger instances have no events before their own first day), and may span at most 90 days per request because the upstream timeline caps its lookback. Dates are inclusive UTC calendar days: days counts whole days ending today, from on its own ends at today, and to on its own covers the seven days ending at to. A to in the future is rejected, and days cannot be combined with from/to:
{
"from": "2005-01-01",
"to": "2005-01-31",
"author": "saxmatt",
"limit": 20
}author takes one Trac username or a list of up to ten. The server filters by author before the
event limit applies, so a contributor's events stay complete even inside a busy window.
Calls that use only days and limit keep the original recent-activity contract: limit is the maximum number of upstream events, and the response contains results, totalEvents, daysBack, and timelineUrl.
For a date-range or author-filtered call, limit (1 to 100, default 20) is advisory. A response covers whole calendar days: results are rounded down to a day boundary, and the newest complete day of the window comes back in full even when it holds more events than limit.
Each coverage response lists events newest first in results and reports requested, the window that was
asked for, and covered, the part of it this response covers completely. complete says whether
the response covered all of it. When it is false, continueWith is a ready-made arguments object
for the remainder: it preserves the effective limit and the accepted author input when present,
so send it back to getTimeline unchanged and repeat until a response reports complete as true,
or until a response arrives without a continueWith. A continuation window always ends on a day already past, so walking one cannot produce gaps or duplicates. note states the coverage in plain language, and coverage that includes today is accurate as of the request. If one day fills the 500-event upstream fetch, that incomplete day is not included in covered: covered is null, complete is false, and terminalTruncation states that the tool cannot continue within that day.
docs/timeline-pagination.md records why the timeline reports day coverage instead of page numbers.
Tool errors
A failed tool call returns an MCP result with isError: true. Its JSON payload carries a
machine-readable code alongside the human-readable error message. not_found errors also name
the resource and id that were requested:
{
"code": "not_found",
"error": "Ticket 99999999 not found",
"resource": "ticket",
"id": 99999999
}
| Meaning |
| The requested ticket or changeset does not exist |
| An argument passed schema validation but cannot be used, such as an unsupported search filter field |
| Trac throttled the request and bounded retries did not clear it |
| Trac or a supporting service failed or returned unexpected content |
Codes are stable API surface: branch on code, never on error wording. An existing code keeps
its meaning and is only removed or renamed with a major version bump, while messages can change
freely. New codes may be added over time, so treat an unrecognized code as upstream_error.
Arguments that fail the advertised input schema also return isError: true, but as the MCP SDK's
plain-text message beginning Input validation error: rather than a JSON payload, so a model can
read it and retry. An unknown tool name is a JSON-RPC -32602 error.
Connect
Remote-capable MCP clients can connect directly to the standard endpoint. Clients that need a local
bridge can use mcp-remote:
{
"mcpServers": {
"wordpress-trac": {
"command": "npx",
"args": [
"mcp-remote",
"https://wordpress-trac-mcp-server-prod.a8c-aiops.workers.dev/mcp"
]
},
"wordpress-meta-trac": {
"command": "npx",
"args": [
"mcp-remote",
"https://wordpress-trac-mcp-server-prod.a8c-aiops.workers.dev/mcp/meta"
]
}
}
}For ChatGPT, add the compatibility endpoint as a custom app. See OpenAI's current MCP help because product labels and setup steps change.
After changing a configured server URL, reconnect the MCP server or restart the client once. Future deployments to the same URL do not require a client configuration change.
Develop
Requirements: Node.js 22 or later and pnpm 10.
pnpm install
pnpm devOpen http://localhost:8787/ to view the local landing page. See
docs/local-development.md for the full browser-preview workflow and
troubleshooting.
Run the complete local quality gate:
pnpm checkThis runs TypeScript, Biome, Vitest, and a Cloudflare Worker dry-run build. See docs/testing.md for manual protocol and live-data checks.
Deployment requires a configured Cloudflare account:
# Staging
pnpm run deploy
# Production
pnpm run deploy:productionDesign and safety
The server is read-only and has no Trac credentials.
The MCP protocol layer is the official TypeScript SDK. Each endpoint serves stateless 2026-07-28 clients and handshake-era clients (2024-10-07 through 2025-11-25), answering the latter with plain JSON. The advertised input schemas are generated from the same Zod schemas that validate calls.
Tool inputs receive runtime validation before any upstream request.
Upstream requests stay on
*.trac.wordpress.organd the official linked-PR endpoint onapi.wordpress.org. The instance slug comes from the URL path, is validated against a strict pattern before it reaches a request, and every request is checked against the resolved origin.Upstream redirects are never followed.
*.trac.wordpress.orghas wildcard DNS and redirects unknown subdomains to Core, so following one would answer for one instance with another's data. A redirect that leaves the instance origin is reported as an unknown instance; one that stays on it is reported as an upstream failure.Transient transport failures, rate limits, server errors, and Trac bot challenges receive bounded retries. Permanent 403 and 404 responses return immediately.
Responses are parsed from public Trac pages and machine-readable formats.
The Worker keeps no ticket cache or durable state.
Contribute
Keep tool schemas, runtime validation, tests, and documentation aligned. Run pnpm check before
opening a pull request.
License
GPL-2.0-or-later.
Available Tools
5 toolsgetChangesetC
Get information about a specific WordPress code changeset/commit including commit message, author, and diff.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| revision | Yes | SVN revision number (e.g., 58504) | |
| includeDiff | No | Include diff content (default: true) | |
| diffLimit | No | Maximum characters of diff to return (default: 2000, max: 10000) |
TDQS
C2.9/5.0
Behavior2/5
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 mentions retrieving diff content but doesn't disclose behavioral traits such as rate limits, authentication needs, error handling, or response format. For a read operation with no annotations, this leaves significant gaps in understanding how the tool behaves.
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, efficient sentence that front-loads the purpose and key details (commit message, author, diff). Every word earns its place with no redundancy or unnecessary elaboration.
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 tool's complexity (3 parameters, no annotations, no output schema), the description is incomplete. It lacks information on output format, error cases, or how results are structured (e.g., JSON fields). For a tool retrieving detailed commit data, more context is needed to use it effectively.
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 description coverage is 100%, so the schema already documents all parameters (revision, includeDiff, diffLimit) with details like defaults and constraints. The description adds minimal value by implying diff content is included, but doesn't provide additional semantics beyond what the schema offers. Baseline 3 is appropriate as the schema does the heavy lifting.
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 information about') and resource ('WordPress code changeset/commit'), and specifies what information is retrieved ('commit message, author, and diff'). It distinguishes from siblings like getTicket or searchTickets by focusing on code changesets rather than tickets or timelines. However, it doesn't explicitly differentiate from getTimeline or getTracInfo, which might overlap in some contexts.
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?
The description provides no guidance on when to use this tool versus alternatives like getTimeline or getTracInfo, nor does it mention prerequisites or exclusions. It implies usage for retrieving commit details but lacks explicit context for selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
getTicketC
Get detailed information about a specific WordPress Trac ticket including description, comments, and metadata.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Trac ticket ID number | |
| includeComments | No | Include ticket comments and discussion (default: true) | |
| commentLimit | No | Maximum number of comments to return (default: 10, max: 50) |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions what information is returned (description, comments, metadata), it doesn't cover important behavioral aspects like whether this is a read-only operation (implied but not stated), error handling for invalid IDs, rate limits, authentication requirements, or response format. For a tool with no annotation coverage, this leaves significant gaps.
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 a single, well-structured sentence that efficiently communicates the core functionality. It's appropriately sized for a simple retrieval tool and front-loads the essential information. There's no wasted language, though it could potentially be more comprehensive given the lack of annotations.
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 tool has no annotations and no output schema, the description is incomplete. It doesn't explain what the return value looks like (structure, format), error conditions, or important behavioral constraints. For a tool that retrieves potentially complex ticket data with comments and metadata, more context is needed to help an agent use it effectively.
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?
The description doesn't add any parameter-specific information beyond what's already in the schema. Since schema description coverage is 100%, all parameters (id, includeComments, commentLimit) are fully documented in the schema itself. The description mentions 'including description, comments, and metadata' which aligns with the parameters but doesn't provide additional semantic context. This meets the baseline for high schema coverage.
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's purpose: 'Get detailed information about a specific WordPress Trac ticket including description, comments, and metadata.' It specifies the verb ('Get'), resource ('WordPress Trac ticket'), and scope of information returned. However, it doesn't explicitly differentiate from sibling tools like 'getTracInfo' or 'searchTickets' which might also retrieve ticket information, so it doesn't reach the highest score.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'getTicket' over 'searchTickets' for finding tickets, or how it differs from 'getTracInfo' which might provide broader Trac information. There's no context about prerequisites, typical use cases, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
getTimelineB
Get recent activity from WordPress Trac timeline including recent tickets, commits, and other events.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of days to look back (default: 7, max: 30) | |
| limit | No | Maximum number of events to return (default: 20, max: 100) |
TDQS
B3.1/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool 'gets' activity, implying a read-only operation, but doesn't specify aspects like rate limits, authentication needs, or what happens if parameters exceed limits. It adds minimal context beyond the basic action.
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, efficient sentence that front-loads the key action and resource, with no wasted words. It directly conveys the tool's purpose without unnecessary elaboration, making it highly concise and well-structured.
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 moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose but lacks details on behavioral traits, usage guidelines, and output format, which are important for a read operation with configurable parameters.
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?
The input schema has 100% description coverage, detailing both parameters with defaults and limits. The description adds no additional parameter semantics beyond what the schema provides, such as explaining how 'days' and 'limit' interact or the format of returned events. Baseline 3 is appropriate as the schema handles the heavy lifting.
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's purpose with specific verbs ('Get recent activity') and resources ('WordPress Trac timeline'), including the types of events covered ('recent tickets, commits, and other events'). However, it doesn't explicitly differentiate from sibling tools like getChangeset or getTicket, which might handle similar data but with different scopes or formats.
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?
The description provides no guidance on when to use this tool versus alternatives like getChangeset or getTicket, nor does it mention any prerequisites or exclusions. It implies usage for retrieving recent activity but lacks explicit context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
getTracInfoA
Get WordPress Trac metadata like components, milestones, priorities, and severities.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Type of Trac information to retrieve |
TDQS
A3.7/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves metadata but lacks details on permissions, rate limits, response format, or error handling. For a read operation without annotations, this leaves significant gaps in understanding how the tool behaves beyond its 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose and lists the retrievable metadata types without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it appropriately sized and well-structured.
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 low complexity (one parameter with full schema coverage) and lack of annotations or output schema, the description is adequate for basic understanding but incomplete. It covers the purpose and parameters but misses behavioral details like response format or error cases, which are important for a tool without structured output documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, documenting the single parameter 'type' with an enum. The description adds value by listing the specific enum values (components, milestones, priorities, severities), which clarifies what 'Trac metadata' includes, though it doesn't provide additional syntax or format details beyond the schema.
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 specific action ('Get') and resource ('WordPress Trac metadata'), listing the exact types of information retrievable (components, milestones, priorities, severities). It distinguishes this tool from siblings like getTicket or searchTickets by focusing on metadata rather than tickets or changesets.
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 metadata about Trac is needed, but provides no explicit guidance on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or specific contexts that would help an agent choose this tool over siblings like getTicket for ticket-related data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchTicketsB
Search for WordPress Trac tickets by keyword or filter expression. Returns ticket summaries with basic info.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query for tickets (keywords or filter expressions like 'summary~=keyword') | |
| limit | No | Maximum number of results to return (default: 10, max: 50) | |
| status | No | Filter by ticket status (e.g., 'open', 'closed', 'new') | |
| component | No | Filter by component name (e.g., 'Administration', 'Posts, Post Types') |
TDQS
B3.1/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the return format ('ticket summaries with basic info') but lacks critical behavioral details: it doesn't specify if results are paginated, sorted, or limited beyond the 'limit' parameter; doesn't mention authentication needs, rate limits, or error handling; and doesn't clarify what 'basic info' includes. For a search tool with no annotation coverage, 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, efficient sentence that front-loads the core purpose and key details. Every word earns its place: it specifies the action, target, method, and return format without redundancy or fluff. This is optimally concise for a search tool.
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 no annotations and no output schema, the description is moderately complete for a search tool: it covers the basic purpose and return format. However, it lacks details on behavioral traits (e.g., pagination, errors) and doesn't fully compensate for the missing output schema by explaining what 'ticket summaries' entail. This is adequate but has clear gaps, fitting a score of 3.
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 description coverage is 100%, so the schema fully documents all parameters. The description adds minimal value beyond the schema by implying the query can use 'keyword or filter expression' and that results include 'ticket summaries', but it doesn't provide additional syntax examples, format details, or constraints. This meets the baseline of 3 when the schema does the heavy lifting.
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 ('Search for') and resource ('WordPress Trac tickets') with the method ('by keyword or filter expression'). It distinguishes from siblings like getTicket (single ticket retrieval) and getChangeset (code changes) by focusing on multi-ticket search. However, it doesn't explicitly contrast with getTimeline or getTracInfo, keeping it at 4 rather than 5.
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?
The description provides no guidance on when to use this tool versus alternatives like getTicket for single tickets or getTimeline for history. It mentions the search capability but offers no context about prerequisites, typical use cases, or exclusions. This leaves the agent without explicit direction for tool selection.
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.
5 tool updates
- First observed
getChangeset - First observed
getTicket - First observed
getTimeline - First observed
getTracInfo - First observed
searchTickets
TDQS
A3.5/5.0
Scored across 5 tools
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: getChangeset retrieves commit details, getTicket fetches ticket information, getTimeline shows recent activity, getTracInfo provides metadata, and searchTickets searches for tickets. The descriptions clearly differentiate these functions, making misselection unlikely.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using camelCase (e.g., getChangeset, getTicket, getTimeline, getTracInfo, searchTickets). The naming is predictable and readable throughout the set, with no deviations in style or convention.
Tool Count5/5
With 5 tools, this server is well-scoped for its purpose of interacting with WordPress Trac. Each tool serves a specific, non-redundant function, and the count is appropriate for covering key operations like retrieving changesets, tickets, activity, metadata, and searching without being overwhelming.
Completeness4/5
The tool set provides strong coverage for querying and retrieving information from WordPress Trac, including tickets, changesets, activity, metadata, and search. A minor gap exists in the lack of tools for creating or updating tickets or changesets, but this is reasonable for a read-only or query-focused server, and agents can still perform core workflows effectively.
Maintenance
ActivityActive
ResponsivenessSlow
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A Model Context Protocol server for Wix AI tools
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
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.
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