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mcp-knowledge

by io.github.getgapup

web_contents

Extract the full text of specific URLs, with optional highlights and a summary. Use when you already know which pages you need, rather than searching for them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesThe URLs to extract
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
summaryNoAlso return a short summary of each page
highlightsNoAlso return the most relevant excerpts

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It accurately states the core behavior (extract full text) and optional features (highlights, summary). However, it does not discuss error handling, what happens with inaccessible URLs, rate limits, or the structure of the return value, leaving some behavioral gaps.

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 two sentences: the first states the primary action, the second gives usage context. Every word earns its place, and the key purpose is front-loaded. No filler or redundant detail.

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 straightforward tool with 4 well-documented parameters, the description covers the essential purpose and use case. There is no output schema, but the output is implied by 'full text of specific URLs'. It could mention async behavior or return format, but it is not vitally incomplete for an extraction tool.

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?

All 4 parameters have descriptions in the schema, so baseline is 3. The description mentions 'optional highlights and a summary' which mirrors the schema's 'summary' and 'highlights' parameters, adding no new semantic value. It does not clarify any parameter nuances beyond what the schema already provides.

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?

The description uses a specific verb 'Extract' and resource 'full text of specific URLs', with optional highlights and summary. It clearly distinguishes itself from search tools by stating 'Use when you already know which pages you need, rather than searching for them.' This makes the purpose unambiguous and well-differentiated from siblings like web_search.

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

Usage Guidelines5/5

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

The description explicitly provides a usage condition: 'Use when you already know which pages you need, rather than searching for them.' This tells the agent when to choose this tool over search-based alternatives, though it doesn't name specific sibling tools. It is clear, actionable, and addresses the primary decision point.

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

All mcp-knowledge toolsInspect
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TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources