mcp-outbound-infrastructure-fingerprint
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly defined and distinct.
Naming Consistency5/5The single tool name follows a clear verb_noun pattern ('fingerprint_outbound_infrastructure'), and with only one tool there is no inconsistency to evaluate.
Tool Count4/5The server contains only one tool, which is slightly below the typical 3-15 range, but the tool is comprehensive and serves a very narrow, specific purpose, making the single-tool count reasonable.
Completeness5/5The tool covers the full scope of outbound infrastructure fingerprinting: detecting sending domains, inbox providers, sending platforms, registration clusters, infrastructure vendors, and deliverability posture. No obvious gaps are apparent for the stated purpose.
Average 4.6/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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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
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses important behavioral traits: 'sending platform recall is partial by design', 'requires an APIFY_TOKEN and consumes Apify credits per domain analyzed', and 'Public DNS and HTTP redirects only, no login, no mailbox access.' These details significantly aid the agent in predicting side effects and costs, going far beyond the structured hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: it opens with purpose, then details signals, outputs, and limitations. It is longer than average, but every sentence contributes substantive information (cost, recall limitations, deliverability checks). The only minor issue is a slight redundancy in mentioning 'no login' twice, but overall it is appropriately dense for a complex tool.
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?
With no output schema, the description carries the full burden of explaining return values. It enumerates the key outputs: runs_outbound verdict, evidence, inbox provider, sending platform, registration clusters, and deliverability posture. It also mentions the flat Clay-ready JSON format and the availability of a 7-day cache via skipCache. This is complete enough for an agent to select and invoke the tool correctly.
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?
The input schema already covers 100% of parameters with descriptions, providing the baseline of 3. The tool description adds rationale for key parameters, such as explaining that lookalike sending domains are the 'strongest signal' behind scan_sending_domains, and that disabling it 'caps the verdict.' This extra context helps the agent decide parameter values, slightly exceeding schema-only semantics.
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 and resource: 'Given a company domain, determine whether that company runs cold email outbound and on what stack.' It clearly names the output (runs_outbound verdict) and distinguishes the tool from generic DNS lookup tools by focusing on outbound infrastructure fingerprinting. Although no siblings are listed, the description fully defines the tool's unique function.
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?
The description specifies that it uses only public DNS and HTTP redirects, with no login or mailbox access, setting clear expectations about what the tool can and cannot do. It also notes that sending platform recall is partial, which helps users interpret results. Since no sibling tools exist, explicit alternative recommendations are not applicable, but the context is sufficient.
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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