mcp-syslog
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-syslogsearch the api service for errors in the last 15 minutes"
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.
mcp-syslog-crunchtools
MCP server for the logs collected by crunchtools/syslog.
Built for RT #1460, to close a specific gap: Hermes gets paged by Nagios and can restart a service, but it cannot read the service's logs — so every remediation is a blind restart. This turns that into an informed one.
Capabilities
Tool | What it answers |
| What can I query? |
| Show me ERRs from this service in the last 15 minutes |
| Where does this string appear across the whole fleet? |
| What are this service's most recent lines? |
| What was everything saying around 03:14? |
| Which service is loudest, and which is actually unhealthy? |
Related MCP server: Log Analyzer MCP Server
The triage loop
nagios_current_problems_tool → what is broken
syslog_search_tool(source=…, → why it broke
severity="ERR",
since="15m")
syslog_context_tool(timestamp=…) → what else was happening at that moment
nagios_schedule_check_tool → confirm the fixsyslog_context_tool without a source is the one that earns its keep. It spans
every source at once, which is how "the app died" gets connected to "the database
container OOMed four seconds earlier".
Design notes
Every result is bounded, and says when it is. Logs are unbounded and this output lands in a model's context window. Each tool caps its results, caps how many lines it will scan, and annotates the answer when either limit is hit:
[!] Stopped after the 2,000,000-line scan limit, so this result is INCOMPLETE
and an empty or short result does not mean nothing happened.That annotation is load-bearing. A caller that cannot distinguish "no errors occurred" from "I stopped looking" will draw the wrong conclusion from an empty result — and this server exists to inform remediation decisions.
Time filtering is cheap. The collector puts the date in the filename, so a ten-minute query opens one file rather than reading ninety days of history.
Source names are untrusted. They come from a model and are used to build a
filesystem path. Each is resolved and then confirmed to still be inside the log
root, which catches traversal, absolute paths, and symlinks pointing out of the
tree — see tests/test_security.py.
Severity is "at least this severe". severity="ERR" returns ERR, CRIT, ALERT
and EMERG. An unrecognised severity is kept rather than dropped, on the grounds
that hiding a line you do not understand is worse than showing it — but it is
not counted as an error in syslog_stats_tool, or a healthy service would
report a 57% error rate.
Severity is not badness. Podman records anything a container writes to stderr
at priority err, and plenty of services log routine INFO there. On lotor,
mcp-trentina sits around 65% "ERR" while being entirely healthy:
PRIORITY=3 | 2026-08-23 15:55:24 INFO httpx: HTTP Request: GET https://... "200 OK"The collector is reporting the journal faithfully; the journal is reporting the file descriptor. Read the message, and prefer a change in error rate to its absolute value. This caveat is in the server's MCP instructions too, so an agent querying it is told the same thing.
Two line formats are parsed. The collector emitted five fields before 2026-08-23 and six after, and the old lines stay in retention for 90 days. Which layout a line uses is decided by where a real severity sits, not by counting fields.
Log format
The collector writes six space-delimited fields:
2026-08-23T15:41:52+00:00 crunchtools.com crunchtools.com httpd ERR AH00169: caught SIGTERM
└─ timestamp ───────────┘ └─ host ──────┘ └─ source ────┘ └prog┘ └sev┘ └─ message ────────┘source is the log stream — normally a container name. program is the process
inside it, which matters for systemd containers where httpd, php-fpm and
mariadb all file under one service name.
Configuration
Variable | Default | Purpose |
|
| Collector log root, mounted read-only |
|
| Cap on entries returned per call |
|
| Cap on lines examined per call |
No credentials — the server reads files off a read-only bind mount.
Running
podman run -d --name mcp-syslog \
--network crunchtools \
-p 127.0.0.1:8027:8027 \
-v /srv/syslog.crunchtools.com/data/logs:/logs:ro \
quay.io/crunchtools/mcp-syslog:latest \
--transport streamable-http --host 0.0.0.0 --port 8027Mount :ro. This server never needs to write, and a read-only mount means a bug
here cannot destroy the forensic record it exists to protect.
Development
uv sync
uv run ruff check src tests
uv run mypy src
uv run pytest -vAvailable Tools
6 toolssyslog_context_toolA
Return log entries surrounding a specific moment.
Use this after an alert names a time. Omitting source spans the whole fleet, which is how a failure gets correlated with whatever else was happening.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum entries to return. | |
| source | No | Restrict to one source. Omit to span all sources. | |
| severity | No | Optional minimum severity. | |
| timestamp | Yes | The moment of interest — ISO-8601, or relative like '30m' ago. | |
| after_seconds | No | How far forward from the timestamp to include. | |
| before_seconds | No | How far back from the timestamp to include. |
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 full responsibility for behavioral disclosure. It reveals that omitting source spans all sources, a useful trait, but does not state read-only-ness, response format, ordering, or potential limits. The presence of an output schema mitigates the missing return details, but the description alone leaves several behavioral aspects unaddressed.
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 zero waste. The primary purpose is front-loaded, and the usage hint follows naturally. Every word earns its place; no redundancy.
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 tool with a fully documented schema and an output schema, the description provides sufficient context to call it correctly. It covers the primary use case and a helpful tip about fleet correlation. Minor gaps like time-range boundaries or default behavior are not critical given the complete schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are already documented. The description repeats the source-omission point already in the schema and adds usage context, but no new parameter-level meaning. The description contributes minimal value beyond the schema, so the baseline 3 applies.
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 returns log entries around a specific moment, which is a distinct action from grep, search, or tail. However, it does not explicitly differentiate itself from sibling tools, so the purpose is clear but not maximally disjoint.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives an explicit trigger: 'Use this after an alert names a time.' It also explains a key usage pattern (omitting source for fleet-wide correlation) but does not mention which sibling to use instead in other scenarios, so it provides context without alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
syslog_grep_toolC
Regex search across all sources, or within one named source.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum entries to return. | |
| since | No | How far back to search — relative ('15m', '2h', '3d') or ISO-8601. | 24h |
| source | No | Restrict to one source. Omit to search the whole fleet. | |
| pattern | Yes | Case-insensitive regular expression matched against the message. | |
| severity | No | Optional minimum severity. |
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 the full burden of behavioral disclosure. It only states 'Regex search,' which implies a read-only operation but does not explicitly confirm safety, nor does it mention behavior like result limits, sorting, or whether the search is across all entries by default. The tool's behavior beyond the search intent is opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It conveys the core function and scope efficiently. Given the schema already documents parameters, this length is appropriate and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is minimal and omits crucial context: it does not explain when to prefer this tool over syslog_search_tool or syslog_tail_tool, does not mention the output format (though an output schema exists), and does not clarify that regex matching is case-insensitive (only noted in the pattern parameter schema). An agent has limited information to correctly invoke this tool in the broader workload context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with every parameter having a description in the input schema. Therefore the baseline is 3. The description adds no extra semantic value beyond restating that the 'source' parameter restricts to one named source, which is already documented in the schema. The description does not clarify any parameter interactions or edge cases.
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 a specific verb (search) and resource (syslog sources), and specifies regex matching. It also notes the scope (all sources or one named source). However, it does not differentiate from sibling tools like syslog_search_tool, which likely performs a similar search but perhaps without regex, so an agent cannot easily distinguish which to use.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. The description implies usage for regex search but does not explicitly mention exclusions or direct the agent to syslog_search_tool for non-regex searches. With several sibling search tools, this is a gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
syslog_search_toolC
Search collected logs by source, time window, severity and pattern.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum entries to return. | |
| since | No | Start of the window — relative ('15m', '2h', '3d') or ISO-8601. | 1h |
| until | No | End of the window. Omit for "up to now". | |
| source | No | Container or service name. Omit to search every source. | |
| pattern | No | Optional case-insensitive regular expression matched against the message. | |
| program | No | Restrict to one program within the source (e.g. 'httpd' inside a web container). | |
| severity | No | Minimum severity: EMERG, ALERT, CRIT, ERR, WARNING, NOTICE, INFO, DEBUG. 'ERR' returns ERR and anything more severe. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 only says 'Search collected logs' and gives no details about read-only behavior, result ordering, pagination, or what happens when no logs match. The tool has an output schema, but that is not shown and the description does not summarize return values or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler or redundancy. It is efficiently worded, and the key search dimensions are listed in a compact manner. It is slightly under-specified, but conciseness itself is good.
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 tool with 7 parameters and an output schema, the description is sparse. It does not mention the 'program' filter, the default time window behavior (e.g., 'since' defaults to '1h'), or the case-insensitive regex pattern. It also does not clarify how this search differs from grep or tail. Given the complexity, a fuller description is needed to enable correct use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add any parameter-level meaning beyond what the schema already provides; it merely names the filters without describing formats, defaults, or interactions (e.g., that severity is minimum severity). The schema explains each parameter well, so the description adds no extra value.
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 states a clear verb ('Search') and resource ('collected logs') and names key filter dimensions (source, time window, severity, pattern). It is specific, but it does not differentiate from sibling tools like syslog_grep_tool or syslog_tail_tool, so an agent cannot immediately tell which to pick.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus the sibling tools. It does not state conditions like 'use for historical searches' or exclude streaming/real-time use, nor does it mention the syslog_sources_tool for enumerating sources. An agent is left to infer usage from the vague 'Search collected logs'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
syslog_sources_toolA
List log sources available to query, with size and last-write time.
| Name | Required | Description | Default |
|---|---|---|---|
| pattern | No | Optional case-insensitive substring to filter source names. | |
| include_internal | No | Include the collector's own '_collector' statistics. |
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 implies a read-only operation ('List') but doesn't explicitly state non-destructiveness or any side effects. It also omits behavioral nuances like default filtering or how the 'include_internal' flag affects results, which are left entirely to the schema.
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?
A single, front-loaded sentence that states the action, target, and returned data without any filler. Every word contributes to understanding the tool's core function.
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 an output schema covering return values, and the description succinctly explains the primary purpose. However, it doesn't mention optional behavior (e.g., how the pattern filter works or that internal sources are excluded by default), though these are documented in the schema. For a simple listing tool, this is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for both parameters (pattern and include_internal), so the baseline is 3. The description adds no additional meaning about these parameters—it focuses on the output fields rather than parameter usage, filters, or defaults.
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 ('List') and resource ('log sources'), stating exactly what the tool returns (size, last-write time). It is clearly distinct from sibling tools that search, grep, tail, or provide context, so an agent can differentiate it without opening schemas.
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 purpose implies usage—when you need to enumerate available sources—but the description provides no explicit guidance on when to choose this over siblings like syslog_search_tool or syslog_grep_tool. No exclusions or alternative conditions are mentioned; usage is inferred rather than directed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
syslog_stats_toolA
Summarise log volume and error rate per source.
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | How many sources to list, ranked by volume. | |
| since | No | Window to summarise — relative ('1h', '24h') or ISO-8601. | 1h |
| source | No | Restrict to one source. Omit to cover every source. |
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 bears the full burden of behavioral disclosure. However, it only states the function without mentioning that it is read-only, what it returns beyond the output schema, or any side effects. It essentially restates the tool's name with slightly more detail, adding little beyond what is already obvious.
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?
A single sentence with no extraneous words. The core purpose is front-loaded and directly stated. It is efficient and easy to parse, which is ideal for an agent scanning many tool definitions.
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 description, combined with a fully documented schema and an output schema, provides enough for basic correct invocation. However, it omits context such as common use cases, behavior when source is omitted, or time-window interpretation nuances. These are not critical given the schema, but the description alone would leave an agent without full operational context.
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 schema already provides 100% coverage for all three parameters (top, since, source) with clear descriptions. The tool description adds no supplementary details about parameter usage or formatting. Per the baseline rule for high schema coverage, a score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Summarise') and identifies the resource ('log volume and error rate per source'), which clearly distinguishes it from siblings that search, tail, or list sources. An agent can immediately understand what this tool does and how it differs from syslog_grep_tool or syslog_tail_tool.
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 explicit guidance on when to use this tool versus alternatives. The name and description imply it is for aggregated statistics, but there is no direct statement like 'use this for summaries, not raw logs' or reference to sibling tools. The context is implied by the purpose, but not enforced.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
syslog_tail_toolB
Return the most recent entries for one source.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of entries to return. | |
| source | Yes | Container or service name. | |
| severity | No | Optional minimum severity. |
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 bears full responsibility for behavioral disclosure. It only states 'return', implying read-only, but does not explicitly state that it is non-destructive, does not require special permissions, or how it orders results. There is no mention of default behavior (e.g., pagination, limiting) beyond the schema default, and no warning about potential performance implications.
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, clear sentence with no redundant words. It is appropriately brief for a tool that likely just needs a quick factual statement. Nothing could be removed without losing meaning.
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 is simple and has a full output schema, so the description does not need to explain return values. However, given the sibling tools (especially syslog_grep_tool and syslog_search_tool), the description lacks context on how this tool differs (e.g., no filtering, just tailing the latest entries). An agent selecting among siblings might need more guidance to pick the right one. This is a moderate gap.
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 schema description coverage is 100%, so the schema already documents all three parameters. The description adds minimal semantic value – 'one source' vaguely references the source parameter but does not clarify accepted formats or relationship between parameters. This meets the baseline for full schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('Return') and resource ('most recent entries for one source'), which is specific enough to understand the basic function. However, it does not differentiate from sibling tools like syslog_grep_tool or syslog_search_tool – there is no mention of filtering or specific use cases, so it could be confused with other tools that also return entries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus the five siblings. It does not mention that this is for quick viewing of the latest logs without search or filtering, nor does it exclude any scenarios. An agent would have to infer usage from the name and schema.
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.
6 tool updates
v0.1.0- First observed
syslog_context_tool - First observed
syslog_grep_tool - First observed
syslog_search_tool - First observed
syslog_sources_tool - First observed
syslog_stats_tool - First observed
syslog_tail_tool
TDQS
Each tool has a distinct purpose: listing sources, regex search, structured search, tailing recent entries, context around a time, and statistics. Although grep and search both query logs, their descriptions differentiate them (regex vs. structured filters), so an agent can select the right one without confusion.
All tools follow the same pattern: 'syslog_' + verb/noun + '_tool'. The naming is consistent in style (snake_case) and structure, making it predictable and easy to infer functionality from the name.
6 tools is a well-scoped set for a syslog query server. Each tool serves a clear operational need without redundancy, and the count is within the ideal range for a focused MCP server.
The tool surface covers the core lifecycle of log querying: discovering sources, searching (both simple and advanced), tailing, contextual analysis, and statistics. No obvious gaps like missing CRUD operations (not applicable here) or missing workflow steps are evident.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Real-time infrastructure monitoring with metrics, logs, alerts, and ML-based anomaly detection.
- SuperlogOAuthsh.superlog
Open-source agent that observes and fixes your application. Query logs, traces, metrics, incidents.
Read-only access to Auralogs production logs: search logs, inspect errors, review AI analyses.
Gain visibility into the performance, availability, and health of your apps and infrastructure.
Related MCP Servers
- FlicenseAqualityDmaintenanceEnables searching and analyzing AWS CloudWatch logs with support for configurable log groups, time-based searches, and service-specific log stream filtering.5-
- FlicenseNot gradedqualityDmaintenanceEnables querying and analyzing logs from multiple remote Unix hosts via the Log Collector API, with tools for search, error detection, and summary generation.-
- FlicenseNot gradedqualityBmaintenanceProvides telemetry tools for retrieving recent logs and system metrics to support root-cause analysis of infrastructure incidents. Enables autonomous incident triage with grounded verification and human-in-the-loop remediation.1-
- FlicenseNot gradedqualityCmaintenanceEnables debugging of distributed transactions by continuously ingesting Docker container logs, indexing them by trace/request ID, and exposing MCP tools to search, tail, and correlate logs across services.-
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/crunchtools/mcp-syslog'
If you have feedback or need assistance with the MCP directory API, please join our Discord server