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mcp-knowledge

by io.github.getgapup

competitor_pricing_radar

Read-only

Radar pricing concurrents — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: How do my competitors' pricing plans and monthly prices compare to mine? · Which competitor plan undercuts or out-features my equivalent tier? Reference case: Notion — pricing vs ClickUp, Asana, Coda. Inputs are validated server-side — send the documented case fields.

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.
focusNo
competitorsYes
selfCompanyYes

TDQS

B3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint and openWorldHint, so the description's mention of a 'structured, audited deliverable' adds some value. However, it includes vague jargon ('Gapup agent-payable C-suite expertise (CMO)') that does not clarify behavior. No mention of authentication, rate limits, or what happens on failure.

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

Conciseness3/5

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

The description is front-loaded with the purpose, but includes unnecessary jargon ('Gapup agent-payable C-suite expertise (CMO)') and a mix of English and French ('Radar pricing concurrents'). The reference case adds value, but the overall length could be reduced without losing meaning.

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

Completeness2/5

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

Given the complexity (4 parameters, nested objects, no output schema), the description provides insufficient guidance on input structure and expected output. It mentions a reference case but does not define the 'documented case fields' or explain how to format the competitors array. The agent may struggle to use this tool correctly.

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

Parameters2/5

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

Schema coverage is low (25%): only the 'async' parameter has a description. The description adds little beyond the schema: 'Inputs are validated server-side' is generic. It does not explain the 'focus' parameter or the required structure of 'competitors' and 'selfCompany' objects, leaving the agent to rely solely on the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool compares competitors' pricing plans and monthly prices, answering specific questions about undercutting and feature comparison. It gives a concrete reference case (Notion vs ClickUp, Asana, Coda). However, it does not explicitly differentiate from siblings like competitor_pricing_scrape or competitor_intel, relying on the 'pricing' focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for pricing comparison but does not explicitly state when to use this tool versus alternatives. It mentions that inputs are validated server-side and provides a reference case, but lacks exclusion criteria or context for when not to use it.

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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