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Proxy-rotated web scraping and cross-platform intelligence for AI agents.

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Status
Healthy
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL

TDQS

B3.4/5.0

Scored across 2 tools

Disambiguation4/5

web_digest (single-URL Markdown extraction) and web_research (cross-platform Reddit+web sentiment) have mostly distinct purposes, but both perform web scraping, so an agent may at times be unsure which to pick for a general fetch.

Naming Consistency5/5

Both tools follow the same web_<noun> snake_case convention (web_digest, web_research), making the naming pattern predictable and readable.

Tool Count3/5

Two tools is thin for a stated web-intelligence domain; the surface feels borderline and leaves little room for the range of operations such a server would plausibly need.

Completeness3/5

The set covers URL extraction and cross-platform research but omits obvious operations like plain search, batch fetching, or structured/entity extraction, leaving notable gaps for an agent.

Available Tools

2 tools
web_digestAInspect

Fetch any URL through rotating mobile proxies and return a clean, token-optimized Markdown extract.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL to scrape (e.g., 'https://example.com/article')

TDQS

A3.6/5.0
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 usefully discloses the rotating mobile-proxy fetching mechanism and the token-optimized Markdown output, which are real traits beyond the schema. It says nothing about failure modes, timeouts, JS-heavy pages, paywalls, or rate limits, so the behavioral picture is incomplete.

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?

A single sentence that front-loads the action and mechanism and closes with the return format. Nothing is padded or redundant.

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 one-parameter tool with no output schema, the description covers the mechanism and the return format, which is most of what an agent needs. It stops short of stating limitations (dynamic pages, auth-gated content, size caps) that would make it fully self-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?

Only one parameter (url) with 100% schema description coverage, so the schema already documents it fully. The description adds no format or syntax detail beyond what the schema provides; the baseline of 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?

The description names a specific verb (Fetch), resource (any URL), and output form (clean, token-optimized Markdown extract), so the agent knows exactly what happens. It does not name or contrast with the sibling web_research, so the differentiation is only implied by the 'extract' framing.

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 by the mechanics (fetch one URL, get Markdown) but there is no explicit when-to-use vs web_research and no when-not guidance. An agent can infer this is the single-page fetch path, but must guess at the boundary with the research tool.

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

web_researchBInspect

Scrape Reddit and the web simultaneously, returning cross-platform sentiment and trend intelligence.

ParametersJSON Schema
NameRequiredDescriptionDefault
topicYesThe topic or keyword to research
platformsNoPlatforms to scrape (options: reddit, web)

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It never states that the operation is read-only, whether external API credentials or rate limits apply, how long a scrape takes, or whether the two platforms are queried in parallel or sequentially. Only the output thrust ('sentiment and trend intelligence') is disclosed.

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?

A single sentence with the action, sources, and output front-loaded. No filler or redundancy; every clause earns its place.

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

Completeness3/5

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

With no output schema and no annotations, the description is the only source of return-shape and behavior information, and it gives only a high-level label ('sentiment and trend intelligence'). For a two-parameter scrape tool this is minimally adequate but leaves output format, latency, and safety profile unspecified.

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 'topic' and 'platforms' are already documented in the schema, including the reddit/web options and the default. The description adds nothing about parameter meaning, which is the expected baseline when the schema does the work.

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 names a specific verb ('Scrape') and explicit resources ('Reddit and the web'), plus the output intent ('cross-platform sentiment and trend intelligence'), so the agent knows exactly what the tool produces. It does not, however, distinguish itself from the sibling web_digest, which appears to overlap in the web-content space.

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 choose this over web_digest, no exclusions, and no prerequisites. Usage is only implied by the word 'research'. An agent facing both tools has no stated criteria for selecting one.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updates
    • First observedweb_digest
    • First observedweb_research

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