fizzy-mcp

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with minimal overlap. For example, fizzy_get_card retrieves a single card's details, fizzy_search finds multiple cards with filters, and fizzy_task creates/updates cards, while fizzy_step and fizzy_comment manage sub-resources. The descriptions clearly differentiate when to use each tool, preventing misselection.
Naming Consistency5/5
All tool names follow a consistent 'fizzy_' prefix with descriptive noun suffixes (e.g., account, boards, comment, get_card, search, step, task). This uniform snake_case pattern makes the tool set predictable and easy to understand, with no deviations in naming conventions.
Tool Count5/5
With 7 tools, the set is well-scoped for a task/project management domain. It covers core operations like account management, board listing, card retrieval, search, creation/updates, and sub-resource handling (steps, comments), which is appropriate without being overly sparse or bloated.
Completeness5/5
The tool set provides comprehensive coverage for managing tasks in Fizzy, including CRUD operations for cards (via fizzy_task and fizzy_get_card), searching (fizzy_search), board exploration (fizzy_boards), and handling steps and comments. It also includes account management (fizzy_account), ensuring no obvious gaps for typical workflows.
Average 4.5/5 across 7 of 7 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
- Last stable release on February 1, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under AGPL 3.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
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    "your-github-username"
  ]
}
```
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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?
The description discloses all four actions and their effects, including deletion which is destructive. Without annotations, the description carries the full burden and covers behavior well. It doesn't mention auth needs or rate limits, but returns are described.
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 well-structured with sections for actions and arguments, using markdown for readability. It conveys necessary info without extraneous text, though slightly verbose. Still efficient.
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 output schema, the description mentions return types (comment details, list, confirmation), completing the picture. It covers all actions and parameter requirements. Could specify exact JSON fields, but sufficient for an agent.
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%, but the description adds value by grouping parameters by action and clarifying defaults (e.g., 'Uses session default if omitted' for account_slug). It also specifies markdown format for body, complementing the 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 it manages comments on a card with four actions (create, list, update, delete). The tool name 'fizzy_comment' and its relation to other Fizzy tools (account, boards, etc.) is distinct, providing a specific verb+resource.
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 lists when each action is used and required arguments. While it doesn't contrast with sibling tools, the siblings have different purposes (account, boards, search), so the context is clear. It could mention not to use this tool for other card operations.
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?
No annotations provided; description covers functionality and return format, but doesn't explicitly state read-only nature or any side effects. Adequate but not fully transparent.
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?
Well-structured with sections, front-loaded main purpose, efficient bullet points for arguments, no unnecessary text.
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?
With 15 parameters, no output schema, and no annotations, description covers all parameters, return format, pagination, and related tools. Could mention error handling but sufficient for usage.
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%, so baseline is 3. Description adds value with structured argument list, defaults, and pagination details, providing moderate extra context.
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 searches for cards with filters, lists specific use cases (tag, assignee, board, index category), and distinguishes from sibling tool fizzy_get_card for when card number is known.
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?
Explicit 'When to use' and 'Don't use when' sections, with clear guidance on alternatives (fizzy_get_card). Provides context for filter usage.
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 fully explains behavior: it describes each action's return format and side effects (setting the default). It reveals that get returns null if unset and that account_slug is only needed for set. Missing details like error handling for invalid slugs prevent a 5.
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 well-structured with clear sections (When to use, Arguments, Returns, Related). While slightly verbose, every sentence adds useful information. It could be more terse, but the clarity compensates.
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?
The description covers usage, behavior for all three actions, return formats, and related tools. It is comprehensive for a simple account management tool. Minor omission: no mention of error behavior for invalid slugs, but overall sufficient.
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?
The schema already covers the enum for action and provides descriptions. The description adds value by clarifying the conditional requirement of account_slug (required for set) and giving an example slug, going beyond the schema's 'required': ['action'] alone.
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, set, or list accounts for API calls' and explains it manages the session default. It distinguishes itself from sibling tools like fizzy_boards or fizzy_task by focusing solely on account context.
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 'When to use' and 'Don't use' sections provide explicit guidance for when to use this tool (list, set, check) and when to avoid it (multi-account operations, suggesting explicit account_slug instead). It also references auto-resolution via env var, giving clear context for alternatives.
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. It discloses behavior beyond a simple list: explains implicit column conventions (Maybe?, Not Now, Done) and how columns array works. This is valuable for correct usage.
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?
Description is well-structured with clear sections: purpose, when to use, conventions, arguments, return format, related tools. Every sentence is meaningful and front-loaded with main purpose.
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?
Given no output schema, the description provides an example return JSON and explains pagination. The implicit column rule is critical for downstream operations, and the 'Related' section ties to sibling tools. Complete for a list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage, but description adds significant meaning: explains account_slug default behavior, limit default range, cursor pagination usage, and column array contents. These details compensate fully.
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 starts with a clear verb+resource: 'List boards in the account with column summaries.' It distinguishes from sibling tools like fizzy_task (create cards) and fizzy_search by focusing on board listing and column discovery.
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 'When to use' section explicitly states purposes: discover board IDs/column IDs, see card counts, find right board/column. It also relates to siblings via 'Related' line. Missing explicit '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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavior: it explains modes, parameter interactions, optionality, and return format. There are no contradictions, and the agent understands side effects (e.g., deletion is described).
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 well-structured with headings and bullet points, front-loading the purpose. It is somewhat lengthy due to comprehensive coverage, but every sentence adds value; minor trimming could improve conciseness.
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?
Given 6 parameters (1 required), no output schema, and no annotations, the description is thorough: it covers all modes, parameter semantics, and return values. An agent can use this tool correctly without additional 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%, so baseline is 3. The description adds substantial value beyond the schema by explaining the mode detection logic, how 'step' can be a substring or index, and how parameters combine. This aids correct invocation.
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: 'Create, complete, update, uncomplete, or delete a step on a card.' It uses specific verbs and a resource (step on a card), and the sibling tool names (e.g., fizzy_boards, fizzy_get_card) confirm it is distinct.
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 provides explicit mode detection logic and examples for each action, guiding when to use which parameters. It does not explicitly contrast with siblings, but the sibling names and context make the tool's domain 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?
No annotations provided, so description carries full burden. Details mode detection, best-effort operations, update behavior (skip same-column moves), and column conventions. Lacks error handling or auth requirements.
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?
Well-structured with headers, bullet arguments, and examples. Every sentence adds value; no redundancy.
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?
Covers all aspects: mode detection, creation/update specifics, column conventions, parameter meanings, return format, and examples. Complete for a moderately complex tool with no annotations or output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and description adds value by grouping parameters (e.g., create-mode required, update-mode optional), explaining enum meanings (open/closed/not_now), and clarifying steps is create-mode only.
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 creates or updates a card with full control, and distinguishes from siblings like fizzy_get_card (read-only) and fizzy_comment (commenting).
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?
Provides explicit mode detection and required parameters for create vs update, but does not explicitly state when to use alternative tools like fizzy_get_card or fizzy_search.
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?
The description explains what the tool returns in detail (JSON fields with examples) and clarifies the two identification methods. It implies a read-only operation but does not explicitly state that. Given no annotations, the description carries the burden adequately, though it could explicitly mention read-only behavior.
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 well-structured with clear sections: purpose, when to use, arguments, important notes, and return format. It is concise with no redundant information, making it easy for an agent to parse.
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?
The description covers all necessary aspects: purpose, input parameters with guidance, output format with example, and related tools. For a simple get tool, this is complete and leaves no ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant value: explains the meaning of card_number (UI #) vs card_id (API UUID), provides preferred usage, and includes example values. This greatly enhances usability beyond the schema descriptions.
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 full details of a card by its number or ID.' It distinguishes the tool from sibling search tool by specifying it retrieves complete card data for a single card, and mentions related tools for deeper interaction.
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 when to use (need full description/metadata) and when not to use (scanning multiple cards - use fizzy_search). Provides clear context for usage and references alternatives.
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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