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web_answer

Get a direct, cited answer to a question, grounded in live web sources. Returns the answer text plus the sources it was built from. Use when you want a conclusion rather than a list of links.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
queryYesThe question to answer
includeTextNoInclude the full text of each cited source (default false)

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full transparency burden. It discloses that the tool works with 'live web sources' and returns 'the answer text plus the sources it was built from', implying a read operation. However, it does not mention potential latency, rate limits, or failure modes (e.g., if no answer is found). Since the operation is read-only and non-destructive, a score of 3 is appropriate – it provides some context but lacks depth.

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 just two sentences and gets straight to the point. The first sentence states the core function, the second explains when to use it. No wasted words, all content is useful and front-loaded.

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?

Given the tool's moderate complexity (3 parameters, no output schema), the description covers the essential context: what it does, what it returns, and when to use it. It does not explain the async behavior or error handling, but the schema already covers the async parameter. The return shape is described as 'answer text plus sources', which is sufficient. This is a solid description for the tool's simplicity, though a note about async or fallback behavior would push it to a 5.

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%, meaning all three parameters (query, async, includeText) are already documented in the input schema. The tool description does not add new parameter semantics beyond mentioning 'sources' which loosely relates to includeText but does not explicitly explain any parameter. Thus it meets the baseline of 3 but does not exceed it.

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 starts with 'Get a direct, cited answer to a question, grounded in live web sources' which clearly states the verb (get), the resource (question grounded in live web sources), and the outcome (answer with citations). It also distinguishes itself from sibling tools by explicitly contrasting with 'rather than a list of links', making the purpose unmistakable.

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?

The description gives explicit guidance: 'Use when you want a conclusion rather than a list of links.' This tells the agent when to choose this tool over a search tool. However, it does not explicitly name alternative tools like web_search or mention scenarios where this tool should not be used, so it falls just short of a 5.

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.