@fluf/mcp

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by FLUF-io

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Server Quality Checklist

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Latest release: v0.2.2
Disambiguation5/5
Each tool targets a distinct resource or action: orders, products, channels, crosslisting, bug reporting, and an assistant. No two tools overlap in purpose, so an agent can easily select the right one.
Naming Consistency4/5
Most tools follow a clear verb_noun pattern (get_orders, list_products, list_channels, report_bug). However, 'crosslist' is a single verb and 'ask_intesa' pairs a verb with a proper noun, so the pattern isn't perfectly uniform but remains readable.
Tool Count5/5
Six tools is well within the ideal range for a focused marketplace management server. Each tool adds distinct value without redundancy or bloat.
Completeness4/5
The set covers core read operations (orders, products, channels), the primary write action (crosslist), plus bug reporting and a diagnostic assistant. Minor gaps exist (e.g., no product create/update/delete or order updates), but the inclusion of ask_intesa fills many potential holes, making the surface functional.
Average 4.2/5 across 6 of 6 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- Last stable release on August 2, 2026
- 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 must carry the full burden. It mentions a 'unified shape' but lacks details on pagination, data freshness, authentication, or rate limits. Minimal behavioral context beyond the schema.
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 a single clear sentence (15 words), front-loading the purpose. Every word is necessary and there is no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 7 optional parameters and no output schema, the description is too brief. It does not explain what 'unified shape' means, pagination behavior, or how filters interact. The agent lacks sufficient context for correct invocation.
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 description coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema; it does not elaborate on parameters or their usage.
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 'Get the seller's orders across every connected marketplace, in one unified shape,' specifying the verb and resource. It distinguishes from siblings like list_products (products vs. orders) and list_channels (channels vs. orders).
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 implies usage for retrieving orders from all marketplaces but does not explicitly state when to use it versus siblings or when not to use it, missing alternatives or exclusions.
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?
No annotations provided, so description carries full burden. Discloses asynchronous behavior, queued status, and dedup logic. Adds valuable context beyond basic listing.
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?
Description is front-loaded with purpose and follows with caveats. Each sentence adds value, though slightly verbose with repetition of honesty instruction. Efficient overall.
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?
No output schema, but description mentions reading 'status' per channel. However, lacks full response structure details. Adequate for a 2-param list tool but could be more complete.
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 baseline is 3. Description does not add additional parameter semantics beyond schema; schema already describes vids and targets clearly.
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?
Description clearly states 'List existing FLUF products on one or more marketplaces' with specific verb and resource. It distinguishes from sibling tools like list_products and list_channels by focusing on cross-listing to marketplaces.
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?
Describes when to use (list products on marketplaces) and provides important caveats (async behavior, queued status, 30-second dedup). Lacks explicit alternatives or when-not-to-use, but context is clear.
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 pagination, filtering, and that rows contain fields including vid for crosslist. It does not mention authentication or rate limits, but the tool appears read-only and safe, and the description adds useful context about the output.
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: one for purpose, one for output content. No fluff, no repetition. Every sentence earns its place.
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 description covers the main purpose and output fields, and hints at relationships with other tools. However, with 7 parameters and no output schema, it would benefit from explaining the paginated response structure (e.g., total count, next page) or providing an example.
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 description coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining the tool's purpose, the significance of the 'vid' field, and the relationship with list_channels for the source parameter.
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 action and resource: 'List the authenticated seller's FLUF products.' It also enumerates key output fields (vid, title, price, etc.) that distinguish it from sibling tools like crosslist, get_orders, list_channels, and report_bug.
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 implies the tool is used before crosslist (mentions vid used by crosslist) and references list_channels for filter values. However, it lacks explicit guidance on when not to use it or alternatives, and does not state typical use cases.
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?
No annotations are present, so the description carries the full burden. It discloses that Intesa runs inside the seller's account, takes up to a minute due to a multi-step tool loop, and can perform bulk jobs, search history, and read support docs. This provides useful context about latency and scope, though it does not explicitly warn about potential side effects of bulk actions.
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 four sentences, each earning its place: defining the assistant, listing capabilities, contrasting with direct tools, and setting latency expectations. It is front-loaded with the name and purpose, and contains no filler.
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 the complexity of this open-ended assistant tool and the absence of an output schema, the description covers the key aspects: capabilities, use case, latency, and relationship to sibling tools. It hints at return values by mentioning the conversation_id, but does not detail the reply format, which would be difficult for such a flexible tool. Overall, it is sufficiently complete for practical 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?
Schema description coverage is 100%, with both message and conversation_id fully documented in the schema. The description adds no additional parameter-level detail; the only extra is the mention that conversation_id allows threading, but this is already in the schema description. Baseline 3 is appropriate.
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 identifies the tool as an assistant to be asked questions, using the verb 'Ask' with the resource 'Intesa'. It distinguishes from siblings by explicitly stating it can do things the MCP server cannot, such as diagnosing sync issues and explaining listing errors, making its purpose specific and differentiated.
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?
The description explicitly advises to prefer direct tools like list_products, crosslist, and get_orders for simple reads and writes, and to use this tool for diagnostic or open-ended questions. It even provides an example ('why did my last five Vinted listings fail?'), giving clear when-to-use and when-not-to-use guidance.
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 provided, the description carries the full burden of disclosing behavior. It states that account identity is attached automatically, that replies arrive in the user's FLUF inbox, and that the agent should inform the user after filing. This conveys the effect of the action and expected workflow. While it does not cover system-level details like rate limits or confirmation mechanics, it provides solid context for a support-ticket 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?
Although the description is longer than average, every sentence earns its place: purpose, two trigger situations, exclusion criteria, procedural instruction, and inbox expectation are all essential. It is front-loaded with the core action and organized with numbered points, making it easy to scan and apply.
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 tool with no output schema and moderate complexity, the description is complete: it explains why the tool exists, when to invoke it, what to avoid, what happens after filing, and how replies are delivered. Sibling tools are clearly different, and no critical usage context is missing.
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 already provides 100% coverage of all four parameters with descriptive text for each. The description does not add parameter-specific meaning, but it does indirectly inform severity selection by distinguishing reportable bugs from user-fixable issues. This meets the baseline for schema-covered parameters without needing compensation.
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 opens with 'Raise a bug with FLUF support,' a specific verb+resource that clearly defines the tool's purpose. It further distinguishes from siblings by detailing two precise situations (user-reported and self-encountered issues) that no other listed tool addresses. This goes well beyond a mere statement of function.
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?
The description offers explicit when-to-use guidance via two enumerated scenarios, and equally explicit when-not-to-use exclusions (expired token, lapsed plan, reauthorisation). It also instructs the agent to tell the user after filing and to skip asking for identity, making the call criteria unambiguous.
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?
No annotations provided, but the description discloses that the roster differs per account and changes over time, offering useful behavioral context. However, it does not mention any permissions or potential side effects, which is acceptable for a read-only listing 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 sentences, front-loaded with the main action, no wasted words. Perfectly concise and well-structured.
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?
While there is no output schema, the description hints at the return format by mentioning connected vs. listable channels. For a simple listing with no parameters, this is sufficiently complete.
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?
There are no parameters (empty schema), so the description does not need to add parameter meaning. A baseline of 4 is appropriate given the absence of parameters.
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 lists marketplace channels available on the account, distinguishing between connected and listable. It contrasts with siblings like 'crosslist' which posts to channels, and 'list_products' which lists products.
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 advises calling this before 'crosslist' instead of guessing channel names, noting that the roster varies per account and over time. This provides clear when-to-use and when-not-to guidance.
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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