mcp-pinterest-brand-presence-mapper
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of confusing it with another. The tool's name and description clearly define its single purpose.
Naming Consistency5/5The single tool name follows a clear snake_case verb_noun pattern (map_pinterest_brand_presence) and is descriptive of its function. No inconsistencies exist.
Tool Count4/5One tool is below the typical 3-15 range, but the server's scope is extremely narrow: mapping a brand's Pinterest presence. This one comprehensive tool fully covers that scope without feeling unnecessarily thin.
Completeness4/5The tool returns all core presence metrics (followers, following, pins, boards, claimed website, last pin date). Minor gaps exist, such as no batch processing or per-board drill-down, but the stated read-only mapping goal is well covered.
Average 4.5/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the bar is lower. The description adds meaningful behavioral context beyond those annotations: identity mismatch returns no counts rather than a stranger's numbers, Pinterest counts are real integers, board count is a better activity signal, and the tool consumes Apify credits. It doesn't contradict any annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single block of dense but well-organized prose. The return payload is front-loaded, followed by use-case interpretation, then identity risk and operational notes. Every sentence adds either behavioral or operational information; no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only resolver with no output schema, the description covers what the tool returns, how it behaves on failure, how to skip risks, what auth is required, cost implications, and caching semantics. This is complete enough for an agent to select, invoke, and interpret the result without opening the schema first.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining why supplying company_name improves accuracy, why handles are riskier, why includeFollowerCounts=false is cheaper, and how skipCache behaves with a seven-day cache. This goes beyond the raw schema descriptions by adding decision context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb-resource pair ('Resolve a company domain to its Pinterest business account') and enumerates exactly what is returned: follower, following, pin, board counts, claimed website, verified merchant status, and last pin date. It also differentiates the tool's identity-check behavior from a naive domain-to-handle guess, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It clearly states read-only usage, requires an APIFY_TOKEN and consumes credits, and explains the recommended input paths: supply a handle to skip discovery or a domain to run full discovery. It does not name sibling tools or explicitly say when not to use it, but the guidance is enough for an agent to decide when to invoke it and what inputs to prefer.
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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