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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

A4/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the behavioral transparency burden. It makes clear this is a read-only extraction ('Extract the full text'), but it does not mention potential slowness, the async mechanism (though schema covers this), or any other operational caveats. The description adds minimal behavioral context beyond the purpose.

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, front-loaded with the primary function and a clear usage condition. Every word serves a purpose, and it is easy to scan.

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 extraction tool with a well-specified schema and no output schema, the description is largely complete. It covers the core use case and distinguishes from search. However, it does not explicitly mention the availability of async mode or the possibility of large outputs, though those are in the schema.

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 parameter descriptions cover 100% of the parameters, providing full detail on urls, async, summary, and highlights. The description adds little new meaning—only a high-level reference to 'optional highlights and a summary' which corroborates the schema. Baseline of 3 is appropriate given the complete schema coverage.

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 clearly states the tool's function: 'Extract the full text of specific URLs' with optional highlights and summary. This is a specific verb and resource, and the closing phrase 'rather than searching for them' differentiates it from sibling search tools 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 Guidelines4/5

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

Provides direct usage guidance: 'Use when you already know which pages you need, rather than searching for them.' This clearly indicates when to use and implicitly contrasts with search-based alternatives, but it does not explicitly name a sibling tool.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.