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gapup-mcp

by io.github.getgapup

competitive_deep_dive_async

Read-only

Async variant of competitive_deep_dive. Returns immediately (<200ms) with a job_id. The research runs in the background (p50≈25s, p95≈30s for depth=medium). Poll the result with competitive_deep_dive_result(job_id) after the eta_seconds hint. Use this instead of competitive_deep_dive when the agent cannot wait >15s for a response. Inputs: same as competitive_deep_dive — company (required), competitors (optional list, max 5), depth (easy/medium/hard, default medium). Async tool — register a webhook via webhooks_manage(register, url, [job.completed]) to receive callbacks instead of polling. Faster + lighter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoResearch depth: 'easy'≈15s, 'medium'≈30s (default), 'hard'≈60s
companyYesName or domain of the target company (e.g. 'Salesforce', 'notion.so')
competitorsNoOptional list of competitor names or domains to include in the comparison matrix (max 5)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesUnique job identifier — pass to competitive_deep_dive_result
statusYesAlways 'queued' on submission
eta_secondsYesEstimated seconds until result is ready
submitted_atYesISO-8601 submission timestamp

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, etc.), the description discloses key behavioral traits: returns in <200ms, background p50/p95 timing, job_id mechanism, and webhook callback support. It also notes the tool is 'Faster + lighter' and how to retrieve results, adding significant context beyond structured fields.

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 well-structured and front-loaded with the core purpose. Every sentence provides useful information: execution model, timing, polling, usage guidance, inputs summary, and webhook registration. No fluff or redundancy; it earns its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists and annotations are provided, the description covers the necessary context: it explains how the async flow works, when to use it, how to get results (polling or webhook), and the input parameters. It also differentiates from siblings. The description is complete for an async tool of this complexity.

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 100%, so the baseline is 3. The description restates the parameters (company, competitors, depth) and even mentions default medium, but this information is already present in the schema descriptions. No new semantic detail is added beyond confirming the inputs are identical to competitive_deep_dive.

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 identifies the tool as an async variant of competitive_deep_dive, stating it returns immediately with a job_id and runs research in the background. This distinguishes it from sibling tools like competitive_deep_dive (sync) and competitive_deep_dive_result (polling), providing a specific verb+resource+scope.

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 tells when to use this tool: 'Use this instead of competitive_deep_dive when the agent cannot wait >15s for a response.' It also names the alternative polling tool (competitive_deep_dive_result) and mentions webhooks as a callback option, giving clear usage context and exclusions.

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.

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