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Glama

synthesize_web_digest

Scrape any webpage and generate an executive summary, key takeaways, and structured entities using Edge LLM synthesis. Handles HTTP 402 micropayments automatically on Base.

Instructions

Scrapes target webpage and executes Edge LLM (Llama 3) context synthesis to extract executive summary, key takeaways, and structured entities. Automatically handles HTTP 402 microtransactions (0.025 USDC on Base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe webpage URL to synthesize.
focusNoOptional focus area or specific query for the synthesis.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.1

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does add genuine behavioral context: the underlying Edge LLM (Llama 3), and notably an automatic HTTP 402 microtransaction of 0.025 USDC on Base, which tells the agent this call has a real monetary cost. However, it omits auth requirements, failure/retry behavior, latency, and rate limits, so the disclosure is partial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with the core action front-loaded and the payment caveat second. Nothing is wasted, though the payment sentence is dense enough that the cost detail could be more clearly flagged.

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?

For a 2-parameter tool with no output schema and no annotations, the description compensates well by naming the return artifacts (summary, takeaways, entities) and disclosing the microtransaction cost. It leaves some operational gaps (auth, failure handling) but is broadly adequate to call 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?

Schema description coverage is 100%, so both 'url' and 'focus' are already documented in the schema. The description adds no syntax, format, or constraint detail beyond what the schema provides, so the baseline 3 applies.

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?

States a specific verb+resource chain (scrape webpage, run LLM synthesis) and enumerates the concrete outputs (executive summary, key takeaways, structured entities). This distinguishes it in spirit from a plain scrape sibling, but it never names clean_web_scrape or otherwise explicitly differentiates itself from the sibling set.

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

Usage Guidelines2/5

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

There is no when-to-use / when-not-to-use guidance and no mention of alternatives such as clean_web_scrape. Usage is only inferable from the fact that this is the 'synthesis' variant, which is weak routing guidance.

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