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Glama

Read Web Page as Markdown

read

Fetch a public HTTP(S) page and return its readable body as cleaned Markdown for LLM context, preserving headings, links, and lists while dropping navigation, ads, scripts, headers, footers, asides, and forms. Use extract instead when you need metadata, JSON-LD, Open Graph/Twitter tags, or a link inventory. Markdown is capped at 40,000 characters and no JavaScript is executed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic HTTP(S) URL whose readable body is needed as Markdown. Content is fetched without JavaScript rendering and may be truncated at 40,000 characters.

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does so well: it states the output is cleaned Markdown, that navigation/ads/scripts/header/footer/aside/form elements are dropped, that the markdown is capped at 40,000 characters, and that no JavaScript is executed. This gives the agent a realistic model of the tool's behavior without needing annotations.

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?

Three sentences: first states core function and transformations, second points to an alternative, third states the two key constraints (cap and no JS). No filler or repetition; the information density is high and it front-loads the purpose.

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 tool with one parameter and no output schema, this description fully covers inputs, outputs, transformations, limitations, and alternatives. The only potential missing piece is explicit return type, but the description says 'return its readable body as cleaned Markdown' which is 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 covers the sole parameter (url) with a description that reproduces almost all details (public HTTP(S), needed as Markdown, no JS, truncation at 40k). Since schema coverage is 100% and the description does not add new detail beyond the schema, the baseline of 3 applies.

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 opens with a specific verb phrase 'Fetch a public HTTP(S) page and return its readable body as cleaned Markdown,' precisely identifying the action and resource. It also distinguishes itself from the sibling tool by noting 'Use extract instead when you need metadata...' so it avoids ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly names the alternative tool ('Use extract instead') and lists the conditions (metadata, JSON-LD, Open Graph/Twitter tags, link inventory) where that alternative is preferred. It also implicitly defines when to use this tool: anytime the readable body as Markdown is needed, especially for LLM context.

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

A4.1/5.0
Disambiguation3/5

Several tools cluster around the same domain: there are multiple audit tools, multiple preflight tools, multiple receipt/settlement tools, and two wallet-policy-conformance tools. The descriptions are carefully distinguished with 'use X instead' notes, but an agent would still need to read closely to separate `agent_discoverability_audit` from `agent_surface_budget_audit` and `seller_integrity_audit` from `payment_offer_preflight`.

Naming Consistency4/5

Most names follow a readable, snake_case pattern with a domain prefix or action stem, such as `morpho_position`, `transaction_receipt`, `wallet_enrich`, and `contract_qualified_search`. The convention is not fully uniform—`read`, `extract`, `scan`, and `schemaforge` are standalone verbs or compounds, and `agent_surface_budget_audit` is a much longer construction—but the style is consistent enough to navigate.

Tool Count3/5

22 tools is at the heavy end of a data-gateway scope, especially since they spread across x402 discovery, Morpho lending, web domain audits, wallet policy, and blockchain receipts. Each tool explains its existence, but the set feels broader than one central data-gateway concern.

Completeness4/5

The tools form a coherent read-only x402/agent-commerce lifecycle: catalog search, discoverability, surface/seller integrity, payment offer preflight, settlement proof, and transaction receipt verification. There are some peripheral tools that do not directly serve x402, and no payment or execution action exists, but the read-only audit gate is intentionally complete and lacks dead ends.