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Glama

data_feeds

Pre-scraped AI data feeds ($0.001 USDC)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
feedIdYes

Schema Changelog

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

  1. First observed

TDQS

D1.8/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only reveals that feeds are pre-scraped and cost $0.001 USDC, without explaining what happens on invocation, whether payment or auth is needed, or whether there are side effects or failure modes.

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

Conciseness2/5

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

The line is brief and contains no filler, but it is a title-like phrase rather than a structured functional description. The brevity is under-specification, not effective conciseness, because no behavioral or input guidance is provided.

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

Completeness1/5

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

For a tool with one required parameter, no annotations, no output schema, and similar siblings, a single phrase leaves nearly everything unknown: feedId semantics, expected response, payment flow, and error behavior. This is not enough for an agent to select or invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description never mentions feedId. An agent has no idea what value to pass, what the identifier refers to, or what format is expected.

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

Purpose2/5

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

The description is a noun phrase ('Pre-scraped AI data feeds') that restates the tool name rather than stating a specific action. It adds a price and a 'pre-scraped' qualifier, but an agent cannot tell what invoking this tool does or what resource it acts on, and no differentiation from siblings is provided.

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 guidance on when to use data_feeds versus siblings like web_scraper, browser_scraper, or public_data_feed. The word 'pre-scraped' is the only implicit selection hint, but no explicit when-to-use or when-not-to-use conditions are given.

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

C2.7/5.0
Disambiguation3/5

The tools are largely distinct, but web_scraper and browser_scraper overlap on web page scraping, and data_feeds and public_data_feed are hard to distinguish without more detail. A few descriptions do help separate output formats, but an agent could still misfire.

Naming Consistency3/5

All names use snake_case and are descriptive, but the naming pattern is mixed: deploy_contract and render_screenshot are verb-first, while smart_contract_verifier, base_analytics, and data_feeds are noun phrases. This prevents a predictable verb_noun convention.

Tool Count4/5

Twelve tools is a reasonable count and each has a defined paid purpose. However, the set spans scraping, data feeds, DeFi yields, and smart-contract deployment, so it feels slightly broad for a single server.

Completeness3/5

Core operations exist for scraping, extraction, deployment, verification, and data feeds, but the surface is incomplete for lifecycle workflows: contracts can be deployed but not called/managed, and data feeds cannot be listed or refreshed. The gaps are noticeable but not fatal for independent one-off API calls.

Resources