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@writerslogic/ai-agents-mcp

@writerslogic/ai-agents-mcp

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competitor_battlecard

Generates a competitor battlecard from up to three competitor homepages or pricing pages, comparing verified pricing tiers, hidden fees, objection handling, and talk tracks.

Instructions

SaaS Pricing & Competitor Battlecard Spy. Reads up to 3 competitor sites (homepage and pricing page, fetched in parallel) and returns a deal battlecard in under a minute: a verdict headline with confidence, pricing tiers whose amounts are checked against the page text, a scenario cost comparison computed in code, hidden fees and limits, landmine questions, objection handling, wedge features and a talk track. If a site or model step fails you get a signed partial result that names what is missing. Pricing is only as current as the public pages; unverified prices are flagged.

Cost: 1.50 USDC per call, paid automatically over x402 on Base when AGENT_PRIVATE_KEY is configured and the price is within MAX_USDC_PER_CALL.

Input Schema

NameRequiredDescriptionDefault
scenarioNoOptional deal size used to compute monthly and annual cost per verified tier: seats for per-seat prices, monthlyVolume for usage prices.
competitorUrlsYesCompetitor homepage or pricing URLs. A same-site pricing page is discovered automatically.
ourProductDescriptionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
Behavior4/5

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

Beyond the annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false), the description discloses the paid-upfront cost model (1.50 USDC via x402 on Base, gated by AGENT_PRIVATE_KEY and MAX_USDC_PER_CALL), expected latency (under a minute), parallel fetching of a discovered same-site pricing page, and failure semantics (signed partial result naming what is missing). It does not describe rate limits or what happens when payment is out of cap, which keeps it off a 5.

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?

Front-loaded with what the tool returns, then failure behavior, then caveat, then cost. Every clause carries information, though the output-list sentence is dense enough to be mildly fatiguing. No filler or repetition.

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

Completeness4/5

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

With no output schema, the description usefully enumerates the return shape and partial-failure mode, and it covers cost/auth and data-freshness limits for an open-world paid fetch tool. The one gap is that the required ourProductDescription parameter's role and downstream effect are never explained.

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 67% and two of three parameters carry their own descriptions. The description adds meaning to competitorUrls ('up to 3', homepage or pricing page, sibling pricing page auto-discovered) and hints at the purpose of scenario via 'scenario cost comparison computed in code', but says nothing about ourProductDescription, the one parameter with no schema description, leaving the required string's role to inference.

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 names a specific resource and action ('reads up to 3 competitor sites ... returns a deal battlecard') and enumerates the concrete artifacts produced (verdict headline, pricing tiers, scenario comparison, landmine questions, talk track). No sibling in the list does competitive pricing teardown, and an agent can tell instantly what this produces without opening the schema.

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?

Intended use (deal battlecard prep against named competitors) is strongly implied by the output list, and a caveat is given that pricing is only as current as public pages. However, there is no explicit when-to-use versus alternatives such as web_search, page_contents, or market_predictor, and no statement of preconditions like needing a live public pricing page.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.