markdown_to_textConvert Markdown to readable plain text (keyless, offline): strips headings, emphasis, code fences, and turns links/images into their text/alt. Ideal for de-formatting Markdown.
| Name | Required | Description | Default |
|---|---|---|---|
| markdown | Yes | The Markdown text. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "markdown": "# Hello World\n\nThis is **bold** and *italic* text.\n\n[Visit Example](https://example.com)\n\n```code block```"
+ },
+ {
+ "markdown": "## Section\n\nSome paragraph with a [link](https://example.org) and ."
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover safety (readOnlyHint, idempotentHint, destructiveHint). The description adds useful behavioral context beyond annotations: 'keyless, offline' discloses privacy/access requirements, and 'turns links/images into their text/alt' specifies transformation behavior. No contradiction with annotations.
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 main purpose, and every phrase adds value: the parenthetical 'keyless, offline' is a key differentiator, and the specific items stripped are concise and informative. 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 simple single-parameter tool with safe annotations, the description covers the main transformation behaviors and output ('readable plain text'). It could specify edge-case handling (e.g., lists, blockquotes) but given the low complexity and existing structure, it is sufficiently 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 coverage is 100% and the parameter 'markdown' is already described as 'The Markdown text.' The description does not add parameter-specific details (e.g., format limitations) beyond what the schema provides, so the baseline 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 ('Convert') and resource ('Markdown to readable plain text'), and enumerates exactly what it strips ('headings, emphasis, code fences') and how it handles links/images ('text/alt'). This clearly differentiates it from sibling extraction tools like extract_headings and extract_links.
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 phrase 'Ideal for de-formatting Markdown' gives a clear use-case context, implying when to choose this tool. However, it does not explicitly mention alternatives or exclusions, so it falls just short of a 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.
Several tool clusters are hard to distinguish: the four ask_pipeworx variants overlap heavily (beta currently behaves identically to ask_pipeworx, and grounded shares routing), and the half-dozen prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) have fuzzy boundaries. Memory and markdown utilities are clear, but the overlapping data-query and prediction-market clusters create real misselection risk.
Names are almost uniformly snake_case and mostly verb-first (ask_, compare_, extract_, scan_, validate_, remember, forget), which is predictable. Minor deviations like entity_profile, recent_changes, and polymarket_edges are noun-first but still follow the same lowercase_snake pattern, so no chaotic mixing of conventions exists.
34 tools is heavy for a server seemingly named 'Markdown', especially when the majority are actually Pipeworx data-research and prediction-market tools. Several tools duplicate or wrap each other (ask_pipeworx_beta, scan_competitor_ai_presence, bet_research et al.), so the count feels bloated; it is not as extreme as 50+, but it exceeds a well-scoped set.
Within the actual dominant domain — structured data research plus prediction markets — the surface is broad: routing queries, grounded answers, deep research, entity profiles, comparisons, resolution, claim validation, semantic search, discoverability, subscriptions/alerts, and memory are all covered. Minor gaps exist (e.g. no direct pipeworx:// citation-fetch tool, and markdown support is thin), but the core workflows have no dead ends.