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PyMCP

by anirbanbasu
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Python

Text Web Search

text_web_search

Search the web by query to retrieve text results, with options for region, page count, and result limit.

Instructions

Perform a text web search using the provided query using DDGS.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pagesNoThe number of pages to fetch. Default is 1, maximum is 10.
queryYesThe search query to fetch results for. It should be a non-empty string.
regionNoOptional region to search in.uk-en
max_resultsNoThe maximum number of results to return. Default is 10, maximum is 100.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.0.1
    • addedInput schema / additionalProperties
      Added value: +false
  2. Addedv1.0.0
  3. Removed
  4. First observed

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that a web search is performed via DDGS; it does not mention network dependency, potential rate limits, read-only nature, or any other side effects.

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?

The description is a single, front-loaded sentence with no filler or redundancy. It directly conveys the core operation without wasted words.

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?

The input schema and presence of an output schema cover parameters and return structure, but the description leaves unexplained context such as what DDGS stands for, whether live internet access is required, and how pages relates to max_results. This is adequate for a simple tool but not fully complete.

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 the schema fully documents all parameters and defaults. The description adds little beyond restating that a query is used, which keeps this at the baseline 3 rather than lower.

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 states a clear action ('Perform a text web search') on a specific resource ('using the provided query using DDGS'), which is enough to identify the tool's purpose. It does not explicitly distinguish itself from sibling tools, but the siblings are unrelated, so no confusion arises.

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?

The use case is implied: use this when a text web search is needed. However, the description gives no explicit guidance on when not to use it, nor does it mention any alternatives or conditions, so an agent must infer the trigger from the description alone.

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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