mcp-gate402

Server Quality Checklist

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Latest release: v0.2.0
Disambiguation5/5
Each tool has a clearly distinct purpose: dedup for cache lookup, html_to_md for conversion, json_repair for fixing JSON, minify for compression, scrape for general scraping, scrape_stealth for hardened scraping, and token_count for token counting. The only potential overlap is between scrape and scrape_stealth, but their descriptions explicitly differentiate them by use case, so no ambiguity.
Naming Consistency4/5
All tools share the 'gate402_' prefix, which is consistent. However, suffixes vary: some are single verbs (dedup, minify, scrape), others are compound with underscores (html_to_md, json_repair, token_count), and one is a phrase (scrape_stealth). This mixed pattern is minor but prevents a perfect score.
Tool Count5/5
With 7 tools, the server covers a well-scoped set of utilities: caching, conversion, repair, minification, scraping (two variants), and token counting. Each tool addresses a distinct need without redundancy, and the count feels appropriate for a utility-focused MCP server.
Completeness4/5
The tool set covers core text processing and web scraping tasks, but there are minor gaps such as the lack of a general format converter (e.g., CSV to JSON) or PDF extraction. However, the existing tools form a coherent set for the advertised functionalities, and no critical operations are missing for the apparent scope.
Average 3.9/5 across 7 of 7 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 21 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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 must disclose behavioral traits. It mentions 'FREE' but does not state that it is read-only, has no side effects, or requires authentication. It lacks information on rate limits or output format, leaving transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, with two sentences that are front-loaded with key information (FREE, then what it does). Every sentence adds value, no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and lack of output schema, the description covers basic purpose and use case. However, it omits important details like return value format (e.g., integer), any limits on input size, or that the count is approximate. These gaps prevent full completeness.
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 100% with a clear description for 'text'. The tool description adds tokenizer info and usage hint, but does not significantly enhance parameter understanding beyond the schema. Baseline of 3 is appropriate.
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 the tool estimates token count for a string, specifying tokenizer types (cl100k/o200k) and a use case. It distinguishes well from sibling tools which focus on other text operations.
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 mentions 'FREE' and 'Use to budget context windows', providing some context. However, it does not explicitly state when to use this tool vs alternatives, nor give exclusions. Since no sibling does token counting, this is adequate but could be improved.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden. It lists types of errors handled but does not disclose behavior on failure (e.g., returns null or error), any destructive actions, or performance characteristics. Minimal transparency beyond basic function.
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?
The description is very concise (one sentence plus a free note). It could be improved by moving the core action before the 'FREE' notice, but it remains direct with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no output schema, the description explains input and examples but omits output behavior (e.g., returns valid JSON string or null) and any limitations. Adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter 'text', and the description adds valuable examples of input (trailing commas, single quotes, unquoted keys) that go beyond the schema's 'Broken JSON string to repair', providing concrete meaning.
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 clearly states the tool's purpose: to coerce malformed JSON (e.g., trailing commas, single quotes, unquoted keys) into valid JSON. It uses a specific verb 'coerce' and resource 'malformed JSON', and is distinct from sibling tools like dedup or HTML conversion.
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 mentions 'FREE' but provides no explicit guidance on when to use this tool over alternatives. Siblings have different purposes, so implied usage is for JSON repair, but no exclusions or context are given.
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?
With no annotations, the description carries the full burden. It discloses critical behavioral traits: exact-match plus cosine similarity threshold (0.88), sub-10ms speed, and pay-per-call pricing. It does not mention potential side effects like cache mutation (though the storeOnMiss parameter suggests optional storage), but overall adds significant value beyond a generic description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences that front-load the purpose and key details. Every sentence adds value, with no superfluous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description should explain return values. It states 'Returns a cache hit/miss for a query' but does not specify the exact format or fields. Given the four parameters, the description could mention optional parameters like vector, namespace, and storeOnMiss. The cost information is helpful, but the description feels slightly incomplete for a production tool.
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 100%, so the baseline is 3. The description does not add additional meaning to the parameters beyond what the schema already provides. Each parameter is adequately described in the schema, and the description does not elaborate on their usage or interactions.
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 clearly states the tool's purpose: a semantic vector-cache lookup using exact-match then 0.88 cosine similarity. It specifies the verb (lookup), resource (cache), and key details (speed, cost). This clearly distinguishes it from sibling tools like gate402_scrape or gate402_token_count.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or sibling comparisons. The cost information is useful but does not constitute explicit usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description covers basic behavior (strips filler, collapses JSON, densifies prose) and cost, but omits return format, idempotency, or destructive effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose and benefit, no redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose and cost but missing output format or return value explanation, which would be helpful given no output schema.
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 100% so parameters are documented; description adds context about compression techniques but no additional param details beyond schema.
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 clearly states the tool compresses text to reduce LLM token spend by ~40%, distinguishing it from siblings like dedup or token_count.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use (to cut token spend) and gives cost details, but lacks explicit when-not or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors: rendering client-side JS, stripping nav/ads, and returning Markdown. It also mentions pricing and free tier. However, it omits details like rate limits, error handling, or authentication requirements, which are important for a web scraping tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: the first describes the core functionality, the second adds pricing context. No wasted words, front-loaded with key purpose.
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?
Given no annotations and no output schema, the description covers the main purpose, input, process, and output format. Missing error cases and size limits, but adequate for typical use.
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?
The input schema has 100% coverage with the 'url' parameter description. The tool description adds no new meaning beyond the schema's 'Public URL'. Baseline is 3 per rules.
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 clearly states the tool fetches any public URL, renders client-side JS, strips nav/ads, and returns clean LLM-ready Markdown. It distinguishes from siblings like gate402_scrape_stealth by emphasizing the non-stealth nature and explicit pricing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Fetch any public URL', providing clear context for when to use. However, it does not mention when not to use or provide alternatives among siblings, such as gate402_scrape_stealth for stealth scraping.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It mentions hardened headless execution and pay-per-call cost, but lacks details on return format, error handling, or limitations such as inability to handle captchas.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences; first sentence states purpose and target, second provides usage guidance and cost. Every word earns its place with no redundancy.
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?
Given a single required param and no output schema, the description sufficiently orients an AI agent about when to use it. Lacks detail on execution behavior and cost implications, but remains adequate for the tool's simplicity.
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 100% and the description adds only slight nuance ('Protected URL') beyond the schema's 'Protected URL to scrape', resulting in marginal added value.
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 clearly states it is a 'hardened headless fetch' for JS-heavy or lightly bot-protected pages, distinguishing it from the sibling tool gate402_scrape by specifying a more robust use case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use when gate402_scrape is blocked or returns little content', providing direct guidance on when to prefer this tool over its sibling.
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?
Description implies a simple, non-destructive conversion. With no annotations, it adequately covers the core behavior, but could mention handling of malformed HTML or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with core action, no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter conversion tool with full schema description and no output schema, the description is complete and provides all necessary context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. Description adds context that HTML should be already obtained, adding value beyond schema.
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?
Clearly states it converts an HTML string to Markdown, with specific verb and resource. Distinguishes from sibling gate402_scrape.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use this tool (when you already have HTML) and when to use alternative gate402_scrape (to fetch a live page).
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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