page_metadata
OpenGraph / page metadata.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
OpenGraph / page metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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, but it reveals nothing about whether the tool fetches the page, the output shape, error behavior, or whether it returns only OpenGraph tags or all meta tags.
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 short, but this is under-specification rather than conciseness. A single vague fragment ('OpenGraph / page metadata.') earns its place no better than the tautological 'Process' example.
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 simple 1-parameter tool with no annotations and no output schema, a description like 'Returns OpenGraph and other meta tags from the HTML head of the given URL' would be complete. This description provides only a vague label and leaves the agent to guess the tool's behavior and return value.
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 the undocumented 'url' parameter. Although the parameter's purpose is loosely inferable from the tool name, the description adds no detail about expected URL format, restrictions, or how the URL is used.
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 'OpenGraph / page metadata' is a noun phrase that restates the tool name ('page metadata') with only the narrow hint of 'OpenGraph' added. It lacks a verb, so an agent cannot tell whether the tool fetches, extracts, or validates metadata, and it does not distinguish the tool from siblings like read_url or extract_url.
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 related siblings such as read_url, pdf_metadata, extract_url, or cache_url. There are no conditions, prerequisites, or exclusions stated.
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.
Multiple tools have overlapping or identical purposes, such as ocr_url and ocr_image (both OCR from an image URL), compare_texts and text_diff (both compare or diff texts), extract_url and read_url (both extract webpage content), and content_hash and hash_text (both compute hashes). The boundaries between these tools are unclear, causing a high risk of misselection.
Naming conventions are mixed. Many tools use verb_noun (extract_url, validate_email), but others use noun_verb (language_detect, html_clean), single words (advisor, crawl, retrieve), or noun_noun (job_status, page_metadata). This inconsistency makes it harder to predict tool names.
With 100 tools, the server is extremely over-scoped for a generic agent toolkit. While some tools are distinct and useful, the sheer number does not align with a focused purpose; many tools are redundant or highly specialized, and the count exceeds what is typically manageable for an agent to reason about.
The toolkit covers a broad range of utilities including extraction, validation, processing, research, memory, and orchestration. However, there are no CRUD tools for creating/updating/deleting resources, no database or file system operations, and no integration beyond web/API basics. This leaves significant gaps for agents that need general lifecycle management, though it does handle many common tasks.