Extract Links
extract_linksRead-onlyIdempotent
Extract all Markdown links text (and bare autolinks) as {text, url} pairs (keyless, offline).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| markdown | Yes | The Markdown text. |
Markdown
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Changed1 schema field changed
- added
Input schema / examplesAdded value: +[ + { + "markdown": "Check out [My Blog](https://myblog.com) and [GitHub](https://github.com/user)." + }, + { + "markdown": "Visit <https://example.com> or see the [documentation](https://docs.example.com/guide)." + } +]
- First observed
TDQS
A4.1/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, open-world, idempotent, and non-destructive hints. The description adds useful behavioral context beyond annotations: 'keyless' (no credentials) and 'offline' (no network), and it clarifies the handling of bare autolinks. These details are not redundant and paint a fuller picture of 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence, front-loading the action and including the output format and key behavioral notes. Every word earns its place; there is no fluff or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is complete: it explains the output format ({text, url} pairs), the input type (Markdown), and key constraints (keyless, offline). The annotations cover safety, and the schema covers the parameter. No critical information is missing for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage for the single 'markdown' parameter with a minimal description ('The Markdown text.'). The description does not add significant meaning beyond the schema, so the high coverage baseline of 3 applies. The examples in the schema provide more practical context than the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the function: 'Extract all Markdown links [text](url) (and bare autolinks) as {text, url} pairs'. It specifies the resource (Markdown links), the exact link formats, and the output structure, distinguishing it from siblings like extract_headings and markdown_to_text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
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 you need to extract links from Markdown) but provides no explicit alternatives or exclusions. It does not mention when not to use it or compare with sibling tools, leaving the usage guidance to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A3.8/5.0
Disambiguation2/5
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.
Naming Consistency4/5
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.
Tool Count2/5
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.
Completeness4/5
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.