gapup-mcp

competitive_deep_dive

Read-only

Gold-standard competitive deep dive — STRUCTURED multi-source data (no LLM narrative). Pair tool: competitor_intel for LLM-narrated board briefing + slide script. Aggregates Wikipedia, Yahoo Finance, SEC EDGAR, Wayback Machine, DuckDuckGo, HackerNews, domain scraping — all keyless. Returns agent-shaped JSON: KPIs (funding, employees, revenue, market cap), P0/P1/P2 competitive signals, pricing radar, competitor comparison matrix, Wayback timeline, positioning (sector/industry/icp_hypothesis/moat_signals), quality score. Every field is sourced or marked unavailable — no hallucinated figures. SLA: p50 ~25s, p95 ~30s · score 80+ on listed targets (US/EU/foreign) · score ~40 on private companies (no EDGAR/Yahoo data). Use sync for batch agents (≤30s tolerance). Use competitive_deep_dive_async + competitive_deep_dive_result(job_id) for conversational agents. Inputs: company name or domain (required), optional competitor list (≤5), optional depth (easy/medium/hard).

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If 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.
`depth`	No	Research depth: 'easy' = Wikipedia + DDG (fast, ~15s); 'medium' = + Yahoo Finance + EDGAR + Wayback (default, ~45s); 'hard' = + HackerNews + domain surfaces + competitor deep dive (~120s)
`company`	Yes	Name or domain of the target company (e.g. 'Salesforce', 'notion.so', 'HubSpot CRM')
`competitors`	No	Optional list of competitor names or domains to include in the comparison matrix (max 5)

Output Schema

TableJSON Schema

Name	Required	Description
`kpis`	Yes	Key Performance Indicators sourced from public data
`company`	Yes
`quality`	Yes
`signals`	Yes	Competitive intelligence signals, severity-ranked P0 (critical) to P2 (informational)
`sources`	Yes
`comparison`	Yes	Feature/dimension comparison between target and each competitor
`depth_used`	Yes
`positioning`	Yes	Positioning analysis derived from public data
`generated_at`	Yes
`pricing_radar`	Yes	Pricing tiers extracted from public sources
`domain_resolved`	Yes
`wayback_timeline`	Yes	Historical snapshots of the company website from Wayback Machine

TDQS

A4.8/5.0

Behavior5/5

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

The description transparently discloses that the tool returns structured JSON with sourced fields, no hallucinated figures, and provides SLA (p50 ~25s, p95 ~30s) and performance expectations for different company types. This adds significant behavioral context beyond the annotations, which already indicate read-only and non-destructive behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively long but well-structured and front-loaded. Every sentence adds value, covering purpose, data sources, output format, SLA, and usage guidance. It could be slightly more condensed, but overall it is efficient and clear.

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 tool's complexity (multiple data sources, optional depth, async option, output structure) and the presence of an output schema, the description is highly complete. It explains what the tool does, when to use it, performance characteristics, and limitations (e.g., lower scores for private companies).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the baseline is 3. The description adds additional context by explaining the async parameter's use cases (sync for batch, async for conversational) and reiterating the depth levels, which map closely to schema descriptions but are reinforced in a practical context.

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 performs a structured competitive deep dive using multi-source data with no LLM narrative. It distinguishes itself from the sibling tool 'competitor_intel' which provides an LLM-narrated board briefing. The purpose is specific and unambiguous.

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 usage guidance: it recommends using the synchronous version for batch agents with ≤30s tolerance and the async version for conversational agents. It also pairs with 'competitor_intel' for narrated briefings, giving clear context on when to use alternatives.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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