xkcd Comic Suggester
Server Details
Suggests a relevant xkcd comic during a conversation, via semantic search over every comic.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- papjuli/xkcdai
- GitHub Stars
- 0
- Server Listing
- xkcdai
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.6/5 across 1 of 1 tools scored.
With only one tool, there is no potential for confusion between tools. The single tool 'find_xkcd' has a clear and distinct purpose.
The tool name follows a clear verb_noun pattern ('find_xkcd'), and with only one tool, consistency is inherently maintained.
The server's purpose is narrowly focused on suggesting xkcd comics, and a single tool is perfectly sufficient to cover this functionality without unnecessary complexity.
The tool provides all necessary functionality for the server's stated purpose: finding relevant xkcd comics given a context. There are no obvious gaps as the tool handles search, scoring, and returns comprehensive results.
Available Tools
1 toolfind_xkcdAInspect
Find xkcd comics semantically relevant to the current conversation.
Call this whenever an xkcd comic might enrich the conversation — when the
discussion lands on a topic xkcd is famous for skewering (programming, science,
statistics, relationships, the absurdity of standards, etc.).
Pass a concise description of the current topic or theme as `context` (a phrase
or sentence works better than a whole transcript), e.g. "spending hours
automating a task that was faster to do by hand" or "code finally compiling".
IMPORTANT — deciding whether to mention one. xkcd has a comic for almost every
topic, so this tool will nearly always return something. A result being
returned does NOT mean you should bring it up. Use the `score` as a signal and
apply your own judgment about conversational fit:
score >= 0.75 strong match — usually worth mentioning if it fits the moment
0.66 - 0.75 plausible — mention only if it genuinely lands
< 0.66 weak/tangential — almost always better to stay silent
Only one comic, at most, per topic — and only when it actually adds something.
When you do share one, cite it by number and title with its `url`, and quote
the `alt` (mouseover) text — it's half the joke.
Returns a dict with a `results` list (num, title, score, url, image, alt,
explain_url) and a `count`. An empty list means nothing cleared the floor.
| Name | Required | Description | Default |
|---|---|---|---|
| context | Yes | ||
| min_score | No | ||
| max_results | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses that a returned result does not guarantee mentioning, explains the score interpretation, and specifies the return format (dict with results list and count), ensuring the agent understands the tool's behavior.
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 well-structured with paragraphs and bullet points, front-loading the purpose, but is somewhat verbose; the scoring table could be more concise, though clarity is maintained.
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 covers purpose, usage scenarios, result interpretation, citation guidance, and return format, addressing the tool's complexity adequately despite lacking output schema; minor omissions like error handling do not detract from overall completeness.
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?
With 0% schema description coverage, the description must compensate but only elaborates on the required 'context' parameter with usage advice and examples, while ignoring optional parameters 'min_score' and 'max_results', leaving their semantics unclear beyond schema 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 clearly states 'Find xkcd comics semantically relevant to the current conversation,' specifying the verb and resource with a distinct scope, and it differentiates from potential siblings by focusing on conversational relevance.
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 explicitly advises when to call the tool ('whenever an xkcd comic might enrich the conversation'), provides examples of suitable topics, and instructs on how to interpret results with score thresholds, including when not to mention a comic.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Your Connectors
Sign in to create a connector for this server.