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mcp-knowledge

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

A4.8/5.0
Behavior5/5

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

The description discloses key behavioral details beyond annotations: immediate return, background processing with p50/p95 times, support for polling and webhooks, and depth-specific timings. There is no contradiction with annotations (readOnlyHint=true is appropriate for an async submission tool).

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 a single paragraph but efficiently conveys the key points: async behavior, timing, alternatives, and input structure. It is front-loaded with the most critical information. Minor improvement could be bullet points, but it is clear and not overly verbose.

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 (async with background processing, polling, webhooks) and the presence of an output schema (not shown but referenced), the description fully covers usage, timing, alternatives, and input parameters. No gaps are apparent for an agent to correctly invoke and handle the tool.

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 coverage is 100% with descriptions for all parameters. The description adds practical context (e.g., depth timing 'easy'≈15s) and notes that inputs are the same as competitive_deep_dive, which is helpful for transfer of knowledge. However, the schema itself already describes the parameters well.

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 it is an async variant of competitive_deep_dive, returns a job_id immediately, and runs research in the background. It distinguishes itself from the synchronous sibling tool (competitive_deep_dive) by highlighting the async behavior and response time trade-off.

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 specifies when to use this tool: when the agent cannot wait >15s for a response. It also provides alternatives: polling with competitive_deep_dive_result or registering a webhook via webhooks_manage, giving clear guidance on usage.

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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