sessemi-mcp
OfficialServer Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
The two tools have completely distinct purposes: 'credits' for checking account balance and 'scrape' for performing web scraping. There is no potential for confusion.
Naming Consistency3/5The tool names are simple and clear but not consistent in part of speech: 'credits' is a noun while 'scrape' is a verb. A more consistent pattern like 'check_credits' and 'scrape_url' would improve naming.
Tool Count2/5Only 2 tools is very minimal for a scraping service. Typically such servers include tools for managing sources, viewing history, or handling different output formats. The count is too low for the implied domain.
Completeness2/5The tool surface covers only the basic operations: check credits and perform a scrape. Missing obvious features like listing previous scrapes, cancelling jobs, or configuring output options. Agents would likely encounter dead ends.
Average 4/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states 'Check', implying a read-only operation, but does not explicitly confirm no side effects, rate limits, or authentication requirements. Basic behavioral information is missing, resulting in a low score.
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, front-loaded sentence that conveys all necessary information without filler. Every word is useful, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and low complexity, the description adequately covers the tool's purpose. However, it does not specify the structure or format of the returned data, which could be improved for completeness. It is minimally viable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and schema coverage is 100% (empty schema). The description fully compensates by explaining what the tool returns (remaining credits, tier, usage), adding complete meaning beyond the schema.
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 clearly states the tool checks credit balance and specifies what information is returned (remaining credits, tier, usage). The verb 'Check' and resource 'Sessemi API credit balance' are specific, and it distinguishes from the sibling tool 'scrape', which likely scrapes data.
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 implies the tool should be used when the user needs to know their credit balance, tier, or usage. Context is clear, but there is no explicit guidance on when not to use it or alternatives. Given only one sibling, the intent is well understood.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses key behaviors: bypassing specific protections, returning HTML/JSON based on Content-Type, and credit consumption. However, it omits details like error handling or rate limits.
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?
Two concise sentences with no redundant information. Front-loaded with the core action and key differentiators.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters fully described in schema and no output schema, the description covers the core function and cost implication. It could mention timeout behavior but is fairly complete for a scraping tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the description adds minimal value beyond schema descriptions. It doesn't clarify parameter usage further beyond what the schema already provides.
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 clearly states the tool scrapes a URL and bypasses specific anti-bot protections (Cloudflare, DataDome, Akamai), distinguishing it from the sibling 'credits' tool which likely handles credit management.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it costs credits per request, implying usage is limited by credit balance, but provides no explicit guidance on when to use this tool versus alternatives or 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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