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mcp-knowledge

by io.github.getgapup

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
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.
depthNoResearch depth: 'easy' = Wikipedia + DDG (fast, ~15s); 'medium' = + Yahoo Finance + EDGAR + Wayback (default, ~45s); 'hard' = + HackerNews + domain surfaces + competitor deep dive (~120s)
companyYesName or domain of the target company (e.g. 'Salesforce', 'notion.so', 'HubSpot CRM')
competitorsNoOptional list of competitor names or domains to include in the comparison matrix (max 5)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kpisYesKey Performance Indicators sourced from public data
companyYes
qualityYes
signalsYesCompetitive intelligence signals, severity-ranked P0 (critical) to P2 (informational)
sourcesYes
comparisonYesFeature/dimension comparison between target and each competitor
depth_usedYes
positioningYesPositioning analysis derived from public data
generated_atYes
pricing_radarYesPricing tiers extracted from public sources
domain_resolvedYes
wayback_timelineYesHistorical 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?

Annotations declare readOnlyHint=true and destructiveHint=false, establishing safety. The description adds significant behavioral context: it explains the multi-source aggregation, keyless access, structured output with all fields sourced (no hallucinations), SLA (p50 ~25s), and quality expectations. No contradictions with annotations.

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 well-structured with front-loaded purpose and pair tool. It is detailed but every sentence adds value; no redundancy. However, it is relatively long; minor gains could be made by condensing some technical details, but overall it's efficient for the complexity.

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 (multi-source, async variants, output schema exists), the description covers all essential aspects: purpose, alternatives, parameters, output structure, SLA, quality constraints. It does not duplicate the output schema but references its existence, which is appropriate.

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?

Schema description coverage is 100%, so baseline is 3. The description adds value by elaborating on each parameter: clarifying that `company` is required, explaining the depth enum with details on sources and speed, and noting that `competitors` is optional with max 5. This exceeds mere schema repetition.

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 ('gold-standard competitive deep dive') and distinguishes it from the sibling `competitor_intel` by noting it provides structured multi-source data without LLM narrative. It specifies the verb (dive) and resource (competitive data), leaving no ambiguity.

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?

Explicitly pairs with `competitor_intel` for LLM-narrated briefings, provides async alternatives (`competitive_deep_dive_async` + `competitive_deep_dive_result`), and gives clear guidance on when to use sync vs async ('Use sync for batch agents... Use async for conversational agents'). SLA and quality scores are also provided.

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

C2.8/5.0
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.

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