page_htmlFull Wikipedia article HTML (Parsoid output) — use when page_summary's extract isn't enough and you need the complete article body, infoboxes, tables, and embedded content. Returns rendered HTML you can scrape/parse for full-text questions.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | ||
| title | Yes | ||
| project | No |
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | HTML content of the page (Parsoid output) |
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds behavioral context by specifying Parsoid-rendered HTML and that the output can be scraped/parsed, which is useful beyond the annotations. No contradiction is present.
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 two sentences, front-loaded with the core function, and every phrase carries value. It avoids redundancy and is appropriately sized for the tool's simplicity.
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 a simple read-only fetch, and the output schema is present, so the description needn't detail return fields. It communicates the return type (rendered HTML) and the use case. The main gap is parameter semantics, but the context is otherwise sufficient for an agent to select and invoke 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?
Schema description coverage is 0%, so the description must compensate for missing parameter explanations. It only implicitly references the article title (via 'Full Wikipedia article HTML') but does not clarify the meaning of 'lang' or 'project'. This forces an agent to infer the expected values, which is insufficient for a 3-parameter schema.
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 the tool fetches full Wikipedia article HTML (Parsoid output) and explicitly distinguishes it from page_summary by noting it provides complete article body, infoboxes, tables, and embedded content. The verb and resource are specific and unambiguous.
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?
It provides explicit when-to-use guidance: 'use when page_summary's extract isn't enough' and names the alternative tool (page_summary). It also describes what content is included, which helps an agent decide. However, it lacks an explicit when-not-to-use phrase or mention of other alternatives like page_media or page_pdf, so it stops short of a perfect 5.
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.
The set mixes several overlapping families—ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded, six Polymarket tools, ai_visibility_check/scan_competitor_ai_presence, and the memory tools—so an agent can easily misselect. Although many descriptions are rich, the tool boundaries are not distinct enough, and two tools are explicitly near-identical at present.
Names are all lowercase snake_case with some logical prefixes (page_*, polymarket_*, pipeworx_*), but conventions mix imperative verbs (remember, forget, subscribe), bare nouns/adjectives (random, featured, onthisday), and descriptive noun phrases (entity_profile, page_html). The pattern is readable but not consistent enough to predict tool names reliably.
42 tools is far beyond the well-scoped range, especially given that the server is labeled 'Wikimedia Rest' but most tools target Pipeworx data research, prediction markets, npm scanning, memory, and AI marketing. Many tools could be consolidated or split into separate servers.
For reading Wikipedia content the page_* tools are fairly complete, and the Pipeworx side covers ask/research/resolve/validate workflows. However, major gaps exist for a coherent user: no Wikipedia search/resolve-title tool, no article diff or edit workflow, and the 'Wikimedia Rest' server lacks any write or query surface matching its name; the overall domain is so diffuse that completeness is hard to assess.