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bailian-tools-mcp

bailian-tools-mcp

by niradler
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Web extractor (Model Studio)

web_extract

Fetch one or more URLs and extract their page content, summarized around a specified goal. Use it to read pages instead of searching when you need targeted information from known URLs.

Instructions

Fetch one or more URLs through Model Studio's web_extractor tool and return the extracted content, summarized for a given goal. Use when a page must be read rather than searched. Billed to your Model Studio plan (~$10/1k web searches in Singapore) — ask before bulk use.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "urls": "<urls>"
    },
    "name": "web_extract"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlsYesAbsolute http(s) URLs to extract.
promptNoWhat to pull out of the pages. Defaults to the main content.
max_output_tokensNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description bears the full burden; it usefully discloses that output is summarized against a goal rather than raw HTML, and that calls are metered (~$10/1k searches in Singapore) with a pre-approval norm. It says nothing about failure modes, partial-success behavior across multiple URLs, or rate limits beyond cost, so real gaps remain for a mutation-free but billed network call.

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?

Two sentences, front-loaded with the action and output shape, followed by the routing condition and the cost warning in descending priority. No filler or restatement of the title.

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 three-parameter fetch-and-summarize tool with no output schema, the definition covers purpose, filtering intent, output nature (summarized), and cost. It would be stronger with a note on partial failures across multiple URLs or on the token-budget parameter, which is the only genuinely unaddressed element.

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 coverage is 67%, so the baseline is 3. The description reinforces URL plurality and frames prompt as the summarization goal, but max_output_tokens — the one parameter lacking a schema description — is never addressed, leaving the coverage gap unclosed.

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?

Specific verb plus resource ("Fetch one or more URLs ... return the extracted content, summarized") with an explicit differentiator against the sibling capability: "read rather than searched." An agent can route between web_extract and web_search from this sentence alone.

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

Usage Guidelines4/5

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

Gives a condition for use ("when a page must be read rather than searched") and a caution gate for volume use ("ask before bulk use"). It stops short of naming the exact alternative sibling by name or describing what to do when extraction fails, but the when-to-use signal is clear rather than implied.

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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