Get metadata about erikbethke.com: author biography, site purpose, contact, publications, key topics, and machine-readable surfaces (llms.txt, agents.json, MCP, OpenAPI).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, so the read-only nature is known. The description adds value by listing the specific metadata categories and machine-readable surfaces, offering more context than annotations alone. It does not mention output format, but for a metadata tool this is not a critical gap.
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, well-structured sentence that front-loads the action and resource, then uses a colon to efficiently list specific contents. Every word adds value, with no filler or 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 zero-parameter metadata tool with no output schema, the description is comprehensive—it enumerates all major metadata categories and machine-readable surfaces. The agent has enough information to invoke the tool correctly and interpret the purpose, given the simplicity of the tool.
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 input schema has zero parameters, so there is no parameter detail to clarify. Baseline for 0 params is 4, and the description does not need to explain parameter behavior since none exist.
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 'Get metadata about erikbethke.com' with a specific list of content types (author bio, site purpose, contact, publications, key topics, machine-readable surfaces). This verb-resource pairing is specific and distinguishes it from siblings like get_blog_post or list_projects.
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 implies when to use the tool (when needing site-level metadata) through the enumerated contents, but it does not explicitly mention alternatives or exclusions. Since the purpose is unambiguous and sibling tools cover other resources, the context is clear enough without explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource or action: single post retrieval, graph traversal, series navigation, site metadata, project listing, guestbook read/write, search, and quote verification. Even the related/series tools have explicit disambiguation in their descriptions.
All tool names follow a consistent verb_noun snake_case pattern: get, list, read, search, sign, verify. No mixed conventions or vague verbs.
With 9 tools, the set is well-scoped for a blog/knowledge-graph server. Each tool earns its place and there is no redundancy or bloat.
The surface covers the full read lifecycle (search, retrieve one, related, series), site context, projects, guestbook (read and write), and even a provenance-check tool. No obvious dead ends or missing core operations.