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Glama

Firecrawl

by io.github.pipeworx-io

Map

map
Read-onlyIdempotent

Crawl a website and return all discovered URLs (up to 5,000). Optionally filter by a search term to rank discovered links by relevance. Returns {url, count, links[]}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe website URL to map, e.g. "https://example.com"
limitNoMaximum number of URLs to return (default 100, max 5000).
searchNoOptional search term to filter discovered URLs by relevance.
_apiKeyNoOptional — your own Firecrawl API key for higher limits; omit to use the shared Pipeworx key.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "url": "https://example.com"
+  },
+  {
+    "limit": 50,
+    "search": "api reference",
+    "url": "https://docs.example.com"
+  }
+]
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish the tool as read-only, idempotent, and non-destructive. The description adds the 5,000 URL cap and the return shape {url, count, links[]}, but it does not explain what 'count' represents, link normalization behavior, or other runtime characteristics. This is adequate but not richly transparent.

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 three short sentences, front-loaded with the primary action and output, and contains no extraneous detail. Every sentence contributes useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the strong annotations and complete parameter schema, the description gives a solid overall picture. There is no output schema, and the description provides only a partial return shape; however, for a simple crawl-and-list tool, it is close to fully sufficient.

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 full 100% coverage with descriptions for all four parameters, so the description does not need to add much. It restates the search relevance and URL limit, which adds no new meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool crawls a website and returns discovered URLs, with an optional relevance filter. This is specific enough to identify the tool's function, though it does not explicitly distinguish it from sibling tools like scrape or search_within.

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?

Usage is implied: use this to crawl a website and get a list of URLs, optionally filtered by a search term. The description does not provide explicit when-to-use guidance or mention alternatives, but the context is clear enough for basic selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the Polymarket analytics tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) have overlapping focuses that could confuse an agent if descriptions are not read carefully. Overall, detailed descriptions help differentiate them.

Naming Consistency5/5

All tool names are lowercase with underscores and follow a consistent verb_noun pattern (e.g., ask_pipeworx, bet_research, compare_entities). Prefixes like polymarket_ and pipeworx_ group related tools. No mixing of conventions or vague names.

Tool Count4/5

With 33 tools, the count is on the higher side but appropriate given the broad scope covering Pipeworx data access, Polymarket analytics, web scraping, memory, and subscriptions. Each tool has a distinct role, and the set is not overly bloated.

Completeness4/5

The tool surface covers a wide range of tasks: data queries, prediction market analysis, web scraping, memory, and subscriptions. Minor gaps exist, such as no tool for user account management or writing data back, but the core domain is well-covered with multiple specialized tools.

Resources